5 Ways Fintech Industry is Using Artificial Intelligence

Top 11 Fintech App Development Ideas for Fintech Startups to Invest

The bond which is getting seeded between Fintech business and Millennials is poised to be extremely strong. At the back of the digital inclined focus that Fintech startup companies have been operating with, the domain – as a whole – is staring at a time of complete revamp. While Fintech companies have been quick to adopt this changed demographics, the only answer to how banks could recreate themselves in the age of millennials lies in intelligence. They will have to learn the tricks of trades via introducing artificial intelligence in fintech.

Let us walk you through the Uses of AI and ML in fintech for Millennials, highlighting what both Fintech companies and banks can work around.

Since the past many years, the millennial user group has been upending the markets, making companies scramble to find the right approach to attract the industry’s first digital natives.

With more of these young adults entering the workforce and investing in their futures, monetarily, The Fintech industry too is soon realizing that it will have to relook at its complete approach to appeal to this demographic’s unique set of expectations and needs. In other words, they cannot move with the business as usual mindset with this smartphone generation.

While the millennial class of customers and users have been given a number of unflattering names in the recent years, like “trophy kids” and “entitled”, this tech-savvy lot has been hailed for being progressive and more acceptable of new finance app ideas compared to the last generations.

Fintech App: Complete UI Kit plus Web landing page | Search by Muzli

Millennial users value convenience and transparency. They demand personalized finance service and product on their fingertips, which is not restricted on time and geography. These primal set of characteristics are what Fintech companies need to maintain when aiming to maintain a competitive advantage in the climate of fast-evolving technological and demand change.

Many Fintech companies have already seized upon this niche opportunity – of millennials expecting digital-first service – at the back of the understanding that the traditional banking avenues are getting phased out. They, individually or in partnership with banks have started exploring the mobile domain to match the shifting consumer trends.

Even in the mobile domain, Financial mobile app development companies are now exploring avenues to present themselves as innovative brands which are aligned with the technical inclination of the end-users.

One such avenue that Fintech companies are focusing upon is Artificial Intelligence.

5 Beautiful PayPal User Interfaces Redesigned | by Domenico Nicoli | UX Planet

Artificial Intelligence is one of the biggest disruptions of the business economy with almost every vertical either embracing the technology or planning to add it into their process by the next 5 years. In fact, AI is found to be one of the prime fintech trends for 2020 & beyond, and fintech centric AI and Machine learning app developers are also putting efforts to excel in this field.

The industry is finding use cases specific to Artificial Intelligence which answers why Fintech is targeting millennials using AI to not just improve the millennial customers’ experience but also revamp their business model to its entirety.

Let us look at some of the used cases that the Fintech industry has found in terms of using Artificial Intelligence to change their mobile offering. These cases should be read as a series of new opportunities for a Fintech startup.

Uses of Machine Learning and AI in Fintech for Millennials

1. Algorithmic trading

The good, the bad, and the ugly of algorithmic trading

While Algorithmic trading is not a new concept in the finance domain, using AI to effectively perform the task on millions of devices is.

[Algorithmic trading uses complex formulas, combined with mathematical models and human oversight, for making decisions related to buying and selling of financial securities on the exchange.]

A great number of financial companies invest in the algorithmic trading practice as the frequency of trade that is executed through machine learning is next to impossible to replicate manually.

2. Better Targeting

Targeting and converting your company's sales prospects — FMD

A better chance at targeting is what the core ML and AI benefits in banks are.

Millennials demand personalized service on their fingertips independent of the time and place. For the purpose, fintech companies are making use of machine learning-driven robotic advisors to replace the need of human advisors at all waking hours.

This robo-advisors target towards millennials is driven with the aim to not just attract them but also remove massive processing costs for the financial institutions. The extent of personalization and promptness that the robo-advisors offer is the answer to what is the impact of AI in financial services.

3. Better Customer Support

10 Practical Ways to Improve your Online Customer Service

One of the primary uses of advanced automation and AI technology in the finance industry can be seen in how the Fintech companies and banks are making their customer service digital and real-time. Let’s give a much closer look at examples of how integration of AI in customer support services can be made possible and how it becomes one of the top benefits of artificial intelligence based apps, especially those which are centered around banking and other financial services:


Chatbots are the basic most answer to how Fintech is targeting millennials.

By 2022, banks might automate over 90% of their interactions through chatbots (Foye, 2017).

By making use of technologies like chatbots, AI helps financial institutions solve users issues instantly. A reason why businesses look for the cost of Cleo like chatbot app. Bank of America, for example, introduced a chatbot named Erica to give their customers instant information about their transactions, account balances, and other similar information.

Personalized experience

Personalization is an answer to building long-lasting customer trust and loyalty for any organization and business. People, especially when interacting with finance-related matters value deep relationships and transparency with the institution and mobile app. This is one of the main reasons why people appreciate the introduction of AI in banking and other fintech solutions.

Personalization is the number one thing that businesses ask for when asking how to use AI to develop next-gen apps. ML algorithms can help in analyzing customers’ information and predict the services that would most or least impress the Fintech users.

A few examples of personalization in AI-backed Fintech applications can be seen in:

  • Capital One Second Look program, launched by Capital One monitors expense patterns. After an in-depth analysis, it helps detect if customers have been charged twice for the same purchase and can inform them in time. The platform also analyzes the tips that customers leave at the restaurant and inform them if it is over what they can afford.
  • MoneyLion, the personal finance platform also displays tips and tricks card and blogs to their customers depending on their monetary activities. “We have bank transaction data, credit behavior and location data; we want to be able to match that with a set of advice and recommendations,” said Tim Hong, chief marketing officer at MoneyLion, which connects to customers’ banks accounts through an API.

Applications like these clearly show the importance of AI and machine learning in financial sectors to make millennials feel important and motivated to remain hooked with the application.

4. Help with underwriting services

The future of underwriting in commercial P&C insurance | McKinsey

The underwriting process is related to assessing risks which every financial service users come with. The role of AI in this fintech process comes in the face of analyzing the true worth of the applicants by looking into their stringed data, especially ones related to their personal spending abilities on social media and other places.

The AI algorithms also help in assessing and predicting the underlying loan trends which can influence the financial sector in the coming time.

5. Stock Market Changes Prediction

Why Value Investing – F1Ras value Investing

With the stock market becoming one of the best investment choices for millennials, the demand for apps that would help make the navigation easier has grown. Something that has helped define newer applications of ML and AI in the fintech industry.

Several AI-backed mobile apps have been introduced which analyze the past and real-time information related to companies and their stocks. And on the basis of this information, they help investors identify which stocks should be invested in and which would prove to be a bad investment choice.

So here were the 5 uses of Machine Learning and AI in Fintech for millennials’ user base to get their attention and make them remain invested in the mobile-based financial offering. An offering that fintech companies provide with the help of their partnered AI and Machine Learning app development company.

Now that you know why fintech companies are using AI, it is time to invest in AI based fintech app development.

Having developed multiple AI software and apps for fintech startups and establishments, we have mastered the art of integrating Artificial intelligence and ML in financial processes.

Let us help you.

Delve into the world of Machine Learning (ML)

For years, many industries have explored ways to incorporate artificial intelligence (AI) into their services because it provides a competitive edge. Since it is an evolving technology, the exploration of AI has brought about sub-concepts. One of the more significant concepts of AI is machine learning (ML), coined by Arthur Samuel in 1959 as “the field of study that gives computers the ability to learn without being explicitly programmed.”


So, let’s talk a little more about machine learning and what it is. Just as Samuel described, machine learning is a subset of AI where computer algorithms are used to automatically learn from data and information, without being explicitly programmed.

By learning from data and information, the system is able to change and improve its algorithms on its own. The learning algorithm enables the system to identify patterns in observed data and build predictive models based on the observations. Because ML is used in situations where perfection is not expected, the goal is not to achieve perfect predictions but to achieve predictions that are good enough to be useful.

Just like the rest of the tech world, machine learning is not a simple concept. It’s important to note that the concept of ML is broken down into smaller subsets based on how much data the system is provided: supervised learning, unsupervised learning, and reinforcement learning. The type of learning is determined on if the information it’s fed is labeled or not.

The most popular application of ML is predictive analysis, which uses historical data to make predictions or recommendations for future events. You know how your phone starts to provide suggestions of words to use as you’re typing out a text? That’s predictive analysis at work. The system has recorded patterns of the words you actively use in order to provide suggestions for responses in the future.

Predictive analysis is used across a multitude of apps, like e-commerce, social media, finance, and even transportation. E-commerce apps use predictive analysis to provide consumers recommendations for other products to consider purchasing. The system detects patterns in items that are commonly purchased together and generates suggestions based on these patterns. Social media does the same, but instead of suggesting products, it recommends people to follow. As mentioned earlier, the system collects demographic data from users and (using unsupervised learning) creates patterns to make these suggestions to users.

There are tons of budgeting apps that will use your banking information (after giving permissions to link) to help you manage income and spending. The system analyzes your transaction history and detects patterns to offer appropriate suggestions for spending. That maps app you use to find the best route to get from work to home uses ML, too. The system records past traffic patterns associated with the time of day to provide recommendations for your commute. ML is in use all around you, and you may not have even realized it until now.

You’re probably asking yourself how the system undergoes training. The type of ML that you’re trying to use determines how much training the system has to undergo. The amount of training for the system is determined by how much data is initially provided to the system. Data is the center of ML, without it, the system wouldn’t know how to do its job. Before we dive into why and how we use the different types of ML, let’s talk about what they are first.


Supervised learning is the process of ML when the system is initially provided data where the algorithm’s inputs (x) and their respective outputs (y) are correctly labeled. Because the input and output data are labeled accordingly, the system is trained to recognize patterns in the data with the algorithm. In future scenarios, this allows the system to receive inputs and produce correctly labeled outputs based on the pattern. Supervised learning is beneficial because it can be used to predict outcomes based on future input data without human interference, like when social media automatically recognizes someone’s face after you’ve tagged them in a picture. When you tag your Aunt Sally in a picture (y), social media stores the facial features of Aunt Sally (x). When you upload pictures of Aunt Sally in the future, social media recognizes her facial features (x) and automatically tags Aunt Sally (y).


In unsupervised learning, data is fed to the system, but the outputs are not labeled accordingly like they are in supervised learning. Unsupervised learning allows the system to observe the data and determine patterns with the information it is given, rather than being trained to recognize the pattern. Once the system has stored the patterns it created, future inputs are assigned to a pattern (created by the system) to produce an output. Unsupervised learning is beneficial because it can show patterns in data that may have been overlooked when observed by humans. Unsupervised learning is used when social networks make recommendations of friends to follow; these recommendations are based on patterns created by demographic data individuals share online. If you went to University X and studied calculus, the algorithm is trained to recognize and recommend other individuals that went to University X and studied calculus as people you may know.


Though it is a separate classification, reinforcement learning is a type of unsupervised learning. Similar to unsupervised learning, the data provided to the system is not labeled, so the system is left to create its own patterns. Where reinforcement differs from unsupervised is that when a correct output is produced, the system is told that this output is correct. This type of learning allows the system to learn from its environment and its experiences to explore a full range of possibilities. It is quite literally, learning through reinforcement. When Spotify makes a recommendation for a song (based on a pattern it noticed in your music selection) you are given the option to “thumbs up” or “thumbs down” the recommendation. When you indicate “thumbs up” or “thumbs down,” Spotify utilizes reinforcement learning because it’s learning your music taste based on what you tell their system.


Why all the hype about machine learning? Well, because it is the next step in achieving artificial intelligence, and is a big step for app developers. ML gives apps the ability to improve and adjust based on user data, without developers influencing it to do so. This technology is saving time for developers (which saves you money) by enhancing the user experience with accuracy on what users want. While there is a multitude of uses for machine learning, two significant ones are image processing and predictive analysis.


Image processing is a very common application of ML, one that you probably see every day. It is used through supervised learning where an algorithm is used to detect various objects in a given image. Much like a child is taught that shapes with five sides are pentagons, the machine is taught to recognize objects in images. The machine is trained by providing a set of labeled images containing different objects; when given future inputs, the machine will identify objects within those inputs and label them. Apple’s Face ID feature is an example of image processing; when you set up Face ID, there is a series of steps you undergo to train the system (iPhone) to recognize your face. These steps include taking photos of your face from multiple angles so the system can analyze your face store this data. After being trained, the system recognizes your facial features as a means to unlock the phone.


The most popular application of ML is predictive analysis, which uses historical data to make predictions or recommendations for future events. You know how your phone starts to provide suggestions of words to use as you’re typing out a text? That’s predictive analysis at work. The system has recorded patterns of the words you actively use in order to provide suggestions for responses in the future.

Predictive analysis is used across a multitude of apps, like e-commerce, social media, finance, and even transportation. E-commerce apps use predictive analysis to provide consumers recommendations for other products to consider purchasing. The system detects patterns in items that are commonly purchased together and generates suggestions based on these patterns. Social media does the same, but instead of suggesting products, it recommends people to follow. As mentioned earlier, the system collects demographic data from users and (using unsupervised learning) creates patterns to make these suggestions to users.

There are tons of budgeting apps that will use your banking information (after giving permissions to link) to help you manage income and spending. The system analyzes your transaction history and detects patterns to offer appropriate suggestions for spending. That maps app you use to find the best route to get from work to home uses ML, too. The system records past traffic patterns associated with the time of day to provide recommendations for your commute. ML is in use all around you, and you may not have even realized it until now.

AI’s Possibilities in Healthcare: A Journey into the Future

Artificial intelligence in health care

Artificial intelligence (AI), machine learning and deep learning have become entrenched in the professional world. AI-style capabilities are being embraced and developed globally (over 26 countries/regions have or are working on a national AI strategy) for many different purposes — from ethics, policies and education to security, technology and industry, the scope is broad and multi-faceted. If, like many others, you are unclear as to what this new terminology means, below is a diagram depicting the hierarchy of AI, machine learning and deep learning for you to consider. In healthcare, the opportunities are vast and significant. Just from a financial point of view, AI has the potential to bring material cost savings to the industry.

But where should you start, and where do the opportunities lie?

AI And Human Accountability In Healthcare

Where to start with AI

First, look at where money is invested — in other words, which start-ups are attracting investors and what is their focus. Rock Health (the first venture fund dedicated to digital health) shows that the top four areas for venture capital investment between 2011 and 2017 were research and development, population health management, clinical workflow and health benefits administration. More than $2.7 billion was invested over 6 years, across 206 start-ups.

Another venture capital and digital health community, Startup Health, which also keeps track of global investments, found that funding is doubling every year for companies which use machine learning technology to enhance health solutions. The companies that focused on diagnostics or screening, clinical decision support and drug discovery tools received the largest share of funding for machine learning in 2018 — i.e., $940 million.

Delving into AI’s opportunities

Perhaps the biggest opportunity lies in assisted robotic surgery, with a potential cost saving of US$40 billion per year. AI-enabled robots can assist surgical procedures by analyzing data from pre-op medical records and past operations to guide a surgeon’s instrument during surgery and to highlight new surgical procedures. The potential benefit to the healthcare organization and the patient from this approach is noteworthy: a 21 per cent reduction in length of hospital stay because robotic-assisted surgery ensures a minimally invasive procedure, thus reducing the patient’s need to stay in the hospital longer.

Surgical complications were found to be dramatically reduced, according to one study into AI-assisted robotic procedures involving 379 orthopedic patients. Robotic surgery has been used for eye surgery and heart surgery. For example, heart surgeons have used a miniature robot, called the Heart Lander, to carry out mapping and treatment over the surface of the heart.

Another valuable use of AI is in virtual nursing assistants. One example is Molly, an AI-enabled virtual nurse that has been designed to help patients manage their chronic illnesses or deal with post-surgery requirements. According to a Harvard Business Review article, assistants like Molly could save the healthcare industry as much as US $20 billion annually.

Diagnosis is another exciting development for AI, with some promising findings on the use of an AI algorithm to detect skin cancers. A Stanford University report found that deep convolutional networks (CNNs) performed as well as dermatologists in classifying skin lesions. Other exciting breakthroughs in AI-assisted diagnosis include a deep-learning program that listens to emergency calls, analyses what is said, tone of voice and background noises to determine whether the patient is having cardiac arrest. Astonishingly, a study from the University of Copenhagen found the AI assistant was right 93% of the time, compared with 73% of the time for human dispatchers.

A fourth potential use for AI lies in digital image analysis, which could help to improve future radiology tools. In one example, a team of researchers from MIT developed an algorithm to rapidly register brain scans and other 3-D images. The result reduces the time to register scans with accuracy comparable to that of state-of-the-art systems.

With so much potential to be gained from AI, healthcare organizations will need to enhance their skills in AI and related capabilities. Decision-makers need to inform themselves about the potential and what is required to achieve those objectives, and then ensure that their teams are properly trained. Culture change in understanding how AI can be used to solve current and future problems is paramount to the future of next-generation healthcare and life sciences organizations.

Era of AI in Cybersecurity

Artificial Intelligence to revolutionize cybersecurity

Palo Alto Networks study highlights preference for AI management of cyber  security – Risk Xtra

Cyber attacks are increasing rapidly these days and the trend for zero-day attacks is also not so unknown. To cope up with these evolving cyber threats, it is the need of the hour to be prepared with more advanced counter mechanisms. This is where AI in cybersecurity comes into play.

These days there are tools and security devices that use AI to make the attack detection and prevention process easy and automated. AI in cybersecurity helps to bring out the concepts of behavioral analysis, automation, and many more that help to create a new space in the field.

Role of AI in cybersecurity

AI has opened new horizons and opportunities to detect and mitigate cyberattacks. Every day multiple cyberthreats are born and increase the attack surfaces of the firm. AI in cybersecurity helps to delve deeper into the key areas to find the threats and adjust itself in a suitable way to mitigate them.

AI can identify and prevent cyberattacks

AI has lots of reference modules and predetermined attack engines that helps the user to detect the inbound cyber attacks easily. Some attackers use predefined scenarios, methodologies, and techniques to attack websites and applications. By using AI-based detection techniques, it will be easy for the user to identify the attacks. Once the ongoing attacks are identified, you can add some of the pre-requisites in the AI engine that will help you to mitigate the same.

The automation of cyberattacks

The Real Challenges of Artificial Intelligence: Automating Cyber Attacks |  Wilson Center

AI in cyberspace is rapidly growing and is both boon and bane for the industries. Whereas on one hand, the application of AI in cybersecurity helps to automate the process for mitigation of cyber threats, it also helps malicious actors to create automated cyberattacks. These attacks are pre-programmed based on the analysis of threat vectors of the organization and attack the same in various ways.

The latest research shows that the threat landscape is increasing these days due to the presence of the open-source AI-enabled hacking tools and software. Within the report, the cybersecurity firm documented three active threats in the wild which have been detected within the past 12 months. Analysis of these attacks — and a little imagination — has led small attackers like script kiddies and newbies to create scenarios using AI which could be more dangerous and threatening.

Impact of AI in cybersecurity space

The presence of AI in the cybersecurity space has opened new horizons for attackers and defenders. The landscape of cyberspace is changing its demographics due to the presence of AI, which proves to be uncertain and unbiased. Sooner or later it is going to be the key differentiator between both the veils.

The AI has helped the cybersecurity researchers and continues to do the same in all the way possible.

The presence of the AI has impacted the cyberspace on the following grounds:

  • Identification of the threat
  • Mitigation of the threat
  • Vulnerability assessment of the organization
  • Constant monitoring of the organization’s threat posture
  • Helps in reporting and accounting of cyber threat of the firm


How to Integrate AI and Machine Learning into Your Existing App

AI and ML Technologies: What They Mean For Federal Agencies

When we talk about the present, we don’t realize that we are actually talking about yesterday’s future. And one such futuristic technology to talk about is how to implement ML and how to add AI to your app. Your next seven minutes will be spent on learning what is the role of Machine learning and Artificial intelligence in the mobile app development industry and what you can do to take advantage of it.

The time of generic services and simpler technologies is long gone and today we’re living in a highly machine-driven world. Machines which are capable of learning our behaviors and making our daily lives easier than we ever imagined possible, all the way, making it necessary for us to understand the process of  integrating Machine Learning and Artificial Intelligence into apps.

Technological realm today is fast-paced enough to quickly switch between Brands and Apps and technologies if one happens to not justify their needs in the first five minutes of using it. This is also a reflection upon the competition this fast pace has led to. Mobile app development companies simply cannot afford to be left behind in the race of forever evolving technologies.

Today, if we see, there is Artificial Intelligence and Machine Learning incorporated in almost every mobile application we choose to use. Which makes it all the more important to know How to integrate machine learning and artificial intelligence in mobile apps.

For instance, our food delivery app will show us the restaurants which deliver the kind of food we like to order, our on-demand taxi applications show us the real-time location of our rides, time management applications tell us what is the most suitable time to complete a task and how to prioritize our work.

In fact, Artificial Intelligence and Machine Learning that were once considered top complicated technology to work on or even comprehend is something that has become an everyday part of our lives without even use realizing of its presence. A proof of which is the following functionalities offered by the top brand apps.

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The wide inclusion of the two related technologies has made the need for worrying over simple, even complicated things cease to exist because our mobile applications and our smartphone devices are doing that for us.

The below provided stats will show us that ML and AI powered mobile apps are a leading category among funded startups and businesses.

  • Allied Market Research has predicted that the market for ML will reach $5,537 million in 2023 further demonstrating its growing prevalence.
  • According to the 2019 CIO Survey by Gartner, the number of companies implementing AI technologies in some form has grown by 270% in the past years.
  • According to Microsoft, 44% of organisations fear they’ll lose out to startups if they’re too slow to implement AI.
  • Research by Fortune Business Insights predicts that $117.19 billion is the expected value of the global machine learning market by 2027 at a CAGR of 39.2% during the forecast period.
  • The Wall Street Journal, states that the advancements in AI and machine learning have the potential to increase global GDP by 14% from, now until 2030.

The idea behind any kind of business is to make profits and that can only be done when they gain new users and retain their old users. The difficult task can be made easy through AI as it comes as one of the benefits or advantages of integrating machine learning and artificial intelligence in apps.

Ways To Implement AI and ML

There are three primal ways through which the power of  Machine Learning and Artificial Intelligence can be incorporated in mobile apps to make the application more efficient, sound, and intelligent. The ways which are also the answer to how to add AI and ML to your app.


The fallacy of logical reasoning. When I was young, I used to take pride… | by The Irrational Investor | Medium

AI and ML are two proficient technologies that imbibe the power of reasoning for solving problems. Applications like Uber or Google Maps that are used by individuals to travel to different areas, many times change the course or route based on the traffic conditions. This is where AI works – by harnessing its thinking capacities. This facility is what makes AI beat a human at chess and how Uber makes use of automated reasoning for optimizing routes to get the users to reach their destination faster.

Hence, real-time quick decisions are presently being controlled by AI to provide the best customer service.


As you are familiar with OTT platforms like Netflix, Amazon, and others; the streaming features of these platforms acquire a large number of customers with high rates of user trust and retention. Both Netflix and Amazon have implemented AI and ML into their applications that examine the customer’s decision based on age, gender, location, and their preferences. The technology based on the customer’s choices then suggests the most popular alternatives in their watch playlist or that individuals with similar tastes have watched.

Giving the users insight into what they would require next has turned out to be the secret of success of some of the top brands in the world – Amazon, Flipkart, Netflix, amongst others have been using the Artificial Intelligence backed power for a very long time now. This is an amazingly popular technology for streaming services and is currently being executed into numerous other applications.


How Behavioral Science Could Improve Federal Programs - Government Executive

Learning how the user behaves in the app can help Artificial Intelligence set a new border in the world of security. Every time someone tries to take your data and try to impersonate any online transaction without your knowledge, the AI system can track the uncommon behavior and stop the transaction there and then.

These three primal bases that answer what are the best ways to incorporate machine learning and AI in application development can be used in multiple capacities to enable your app to offer a lot better customer experience.

And now that we have looked at how to integrate AI in android apps along with integration of ML, let us answer the why?

Why should you integrate machine learning and AI into your mobile app?

Why to Integrate Machine Learning and AI Into Your Mobile App?


Leveraging Data and Ecommerce Personalization Types | Acro Media

Any AI  algorithm attached to your simpleton mobile application can analyze various sources of information from social media activities to credit ratings and provide recommendations to every user device. Machine learning application development can be used to learn:

  • Who are your customers?
  • What do they like?
  • What can they afford?
  • What words they’re using to talk about different products?

Based on all of this information, you can classify your customer behaviors and use that classification for target marketing. To put simply, ML will allow you to provide your customers and potential customers with more relevant and enticing content and put up an impression that your mobile app technologies with AI are customized especially for them.

To look at a few AI ML examples of big brands who are setting standards on how to implement Machine Learning in apps?

  • Taco Bell as a TacBot that takes orders, answers questions and recommends menu items based on your preferences.
  • Uber uses ML to provide an estimated time of arrival and cost to its users.
  • ImprompDo is a Time management app that employs ML to find a suitable time for you to complete your tasks and to prioritize your to-do list
  • Migraine Buddy is a great healthcare app which adopts ML to forecast the possibility of a headache and recommends ways to prevent it.
  • Optimize fitness is a sports app which incorporates an available sensor and genetic data to customize a highly individual workout program.

Advanced search

What Are Advanced Search Options?

Through the AI and Machine learning based app development process, you will get an app that lets you optimize search options in your mobile applications. AI and Machine Learning makes the search results more intuitive and contextual for its users. The algorithms learn from the different queries put by customers and prioritize the results based on those queries.

In fact, not only search algorithms, modern mobile applications allow you to gather all the user data including search histories and typical actions. This data can be used along with the behavioral data and search requests to rank your products and services and show the best applicable outcomes.

Upgrades, such as voice search or gestural search can be incorporated for a better performing application.

Predicting user behavior

User Behavior: How to Track it on Your Website + Analysis | Hotjar Blog

The biggest advantage of AI based machine learning app development for marketers is that they get an understanding of users’ preferences and behavior patterns by inspection of different kinds of data concerning the age, gender, location, search histories, app usage frequency, etc. This data is the key to improving the effectiveness of your application and marketing efforts.

Amazon’s suggestion mechanism and Netflix’s recommendation works on the same principle that ML aids in creating customized recommendations for each individual.

And not only Amazon and Netflix but mobile apps such as Youbox, JJ food service, and Qloo entertainment adopt ML to predict the user preferences and build the user profile according to that.

More relevant ads

What are Relevant Ads & How to Create Better Ads for Campaigns (Examples)

Many industry experts have exerted on this point that the only way to move forward in this never-ending consumer market can be achieved by personalizing every experience for every customer.

According to a report by The Relevancy group, 38% of executives are already using machine learning for mobile apps as a part of their Data Management Platform (DMP) for advertising.

With the help of integrating machine learning in mobile apps, you can avoid debilitating your customers by approaching them with products and services that they have no interest in. Rather you can concentrate all your energy towards generating ads that cater to each user’s unique fancies and whims.

Machine Learning app development companies today can easily consolidate data intelligently that will in return save time and money went into inappropriate advertising and improve the brand reputation of any company.

For instance, Coca-Cola is known for customizing its ads as per the demographic. It does so by having information about what situations prompt customers to talk about the brand and has, hence, defined the best way to serve advertisements.

Improved security level

Increasing Security Level. Cyber Security Concept. Wireframe Hand Is Pulling Up To The Maximum Position Progress Bar With The Stock Illustration - Illustration of piracy, concept: 207988653

Besides making a very effective marketing tool, Artificial Intelligence and machine learning for mobile apps can also streamline and secure app authentication. Features such as Image recognition or Audio recognition makes it possible for users to set up their biometric data as a security authentication step in their mobile devices. ML also aids you in establishing access rights for your customers as well.

Apps such as ZoOm Login and BioID have invested in ML and AI application development to allow users to use their fingerprints and Face IDs to set up security locks to various websites and apps. In fact, BioID even offers a periocular eye recognition for partially visible faces.

Now that we have looked at the different areas in which application of AI and ML can be incorporated in the mobile app, it is now time to look at the platforms which will make it possible, which we in our capacity has experienced AI app development company have been relying on, before we head on to the strategy that a business should devise to ensure a smooth implementation.

User engagement

8 Surefire Ways to Increase User Engagement in 2020

The AI development services and solutions engage organizations to offer balanced customer support and a span of features. Few apps provide small incentives to the customers so that they utilize the application consistently. Also just for entertainment purposes, chatty AI assistants are there to help the users and hold a discussion at any hour.

Data mining

10 Data Mining Techniques Every Data Scientist Should Know | Built In

Data mining, also known as data discovery, includes analyzing the vast set of data to gather helpful information and collect it in different areas, including data warehouses and others. ML offers data algorithms that will generally improve automatically through experience based on information. It follows the way of learning new algorithms that make it quite simple to find associations inside the data sets and gather the data effortlessly.

Fraud detection

Reducing false positives in credit card fraud detection | MIT News | Massachusetts Institute of Technology

The cases of fraud are a worry for every industry, particularly banking and finance. To solve this problem, ML utilizes data analysis to limit loan defaults, fraud checks, credit card fraud, and more.

It also assists you with determining an individual’s capability to take care of a loan and the danger related with giving the loan. E-commerce apps frequently exploit ML to discover promotional discounts and offers.

Object and facial recognition

A General Approach for Using 2D Object Detection for Facial ID | KUNGFU.AI

Facial recognition is the most loved and latest feature for the mobile apps. Facial recognition can help improve the security of your application while additionally making it faster to login. It also helps in securing the data from unknown sources.

With the improved security, facial recognition can be utilized by medical professionals to evaluate the health of patients by examining a patient’s face.

Best Platforms to Develop a Mobile App with Machine Learning?

1. Azure

Microsoft Azure | Avantiico

Azure is a Microsoft cloud solution. Azure has a very large support community, and high-quality multilingual documents, and a high number of accessible tutorials. The programming languages of this platform are R and Python. Because of an advanced analytical mechanism, the AI app developers can create mobile applications with accurate forecasting capabilities.

2. IBM Watson 

Bobs Blog : IBM's Watson: Observe, Interpret, Evaluate, and DecideBobs Blog

The main characteristic of using IBM Watson, is that it allows the developers to process user requests comprehensively regardless of the format. Any kind of data. Including voice notes, images or printed formats is analyzed quickly with the help of multiple approaches. This search method is not provided by any other platform than IBM Watson. Other platforms involve complex logical chains of ANN for search properties. The multitasking in IBM Watson places an upper hand in the majority of the cases since it determines the factor of minimum risk.

3. Tensorflow

GitHub - tensorflow/docs: TensorFlow documentation

Google’s open-source library, Tensor, allows AI application development companies to create multiple solutions depending upon deep machine learning which is deemed necessary to solve nonlinear problems. Tensorflow applications work by using the communication experience with users in their environment and gradually finding correct answers as per the requests by users. Although, this open library is not the best choice for beginners.

4. Api.ai 

Google Snaps-up API.ai Startup To Boost Natural Language Capabilities

It is a platform that is created by the Google development team which is known to use contextual dependencies. This platform can be very successfully used to create AI based virtual assistants for Android and iOS. The two fundamental concepts that Api.ai depends on are – Entities and Roles. Entities are the central objects and Roles are accompanying objects that determine the central object’s activity. Furthermore, the creators of Api.ai have created a highly powerful database that strengthened their algorithms.

5. Wit.ai

GitHub - akshitbhalla/wit-ai-with-Hasura: wit.ai API integration with a custom Hasura service

Api.ai and Wit.ai have largely similar platforms. Another prominent characteristic of Wit.ai is that it converts speech files into printed texts. Wit.ai also enables a “history” feature which can analyze context-sensitive data and therefore, can generate highly accurate answers to user requests and this is especially the case of chatbots for commercial websites. This is a good platform for the creation of Windows, iOS or Android mobile applications with machine learning.

6. Amazon AI 

AI or Die: Why companies must Invest in AI | AWS Startups Blog

The famous AI based platform is used to identify human speech, visual objects with the help of deep machine learning processes. The solution is completely adapted for the purpose of cloud deployment and thus allowing you to develop low complexity AI-powered mobile apps.

7. Clarifai 

Clarifai - Insight Platforms

The solution based on AI analyzes information with the help of complicated and capacitive algorithms. The apps made using the platform (which can be integrated in-app using REST API) – can adapt to individual user experience – which makes it the most preferred choice for the developers who wish to invest in Artificial intelligence for app development to enter the world of intelligent assistants.

With this, you now know that the ways your mobile app can become an AI app and the tools that will help with Machine learning and AI app development. The next and the last and the most important part that we are going to discuss now is how to get started.

How to Start Implementation of AI into Apps?

Implementation of Artificial or Machine Learning in an application calls in for a monumental shift in the operation of an application that works sans intelligence.

This shift that is asked for by AI is what demands to look at pointers that are very different from what is needed when investing in the usual mobile app development process.

 Here are the things that you will have to keep into consideration when managing an AI project:

Identify the issue to solve through AI

9 Real-World Problems that can be Solved by Machine Learning

What works in case of applying AI in a mobile app, as we saw in the first illustration of the article is applying the technology in one process instead of multiple. When the technology is applied in a single feature of the application, it is much easier to not just manage but also to exploit to the best extent. So, identify which is that part of your application that would benefit from intelligence – is it recommendation? Would the technology help in giving a better ETA? – And then collect data specifically from that field.

Know your data

Complete Data Solutions – Know Your Data

Before you look forward to AI app development, it is important to first get an understanding of where the data would come from. At the stage of data fetching and refinement, it would help to identify the platforms where the information would come from in the first place. Next, you will have to look at the refinement of the data – ensuring that the data you are planning to feed in your AI module is clean, non-duplicated, and truly informative.

Understand that APIs are not enough

The Ultimate Guide to Accessing & Using APIs

The next big thing, when it comes to implementing AI in a mobile app is understanding that the more extensively you use it, the more unsound Application Programming Interfaces (APIs) would prove to be. While the APIs that we mentioned above are enough to convert your app into an AI app, they are not enough to support a heavy, full-fledged AI solution. The point is, the more you want a model to be intelligent, the more you will have to work towards data modeling – something that APIs solely cannot solve.

Set metrics that would help gauge AI’s effectiveness

Customer service chatbot in eCom, Telecom, Healthcare

There is hardly a point of having an AI or Machine Learning feature implemented in your mobile app until you also have the mechanism to measure its effectiveness – something which can only be drawn after getting an understanding of what exactly do you want it to solve. So, before you head out to implement AI or even ML in your mobile app, understand what you would like it to achieve.

Employ data scientists

What Does a Data Scientist Do? | Role & Responsibilities

The last most important point to consider is employing data scientists on either your payroll or invest in a mobile app development agency that has data scientists in their team. Data scientists will help you with all your data refining and management needs, basically, everything that is needed on a must-have level to stand and excel your Artificial Intelligence game.

This is the stage where you are now ready to implement the intelligence in your mobile application. Since we talked about data a lot in the last segment and because data is an inherent part of Artificial Intelligence, let us look at the solution of problems that can arise out of data as the parting note.

Feasibility and practical changes to make

Techno Economic Feasibility Studies in Majiwada, Thane, Varshasookt Consultants | ID: 20602432512

Now that you have known the which, why and how about the implementation of AI and Machine Learning apps, you might have an idea regarding a plan in mind like what steps should be taken as a top priority and how your application would work/appear, once the changes are made. Along these lines, it is an ideal opportunity to perform a couple of checks prior to moving ahead, for example, –

  • Perform a quick possibility test to know if your future execution will profit your business, improve user experience and increase engagement. A fruitful upgrade is the one which could make the existing users and customers happy and attract more individual towards your product. If an update is not expanding your efficiency, then there is no reason for putting in effort and money for it.
  • Analyze if your current group can deliver what is required. If there is less or no internal team capacity then you need to hire new employees or outsource the work to a reliable and expert artificial intelligence development company.

Data integration and security

Trends 2019: Systems integration and data security take centre stage – GPA

While implementing Machine Learning projects for mobile applications, your app will require a better information configuration model. Old data, which is composed in a different way, may influence the effectiveness of your ML deployment.

When it is decided what abilities and features will be added in the application, it is important to focus on data sets. Efficient and well organized data along with careful integration will help in providing your app with high-quality performance in the long run.

Security is another basic issue, which can’t be overlooked. To keep your application strong and secure, you need to think of the correct arrangement to integrate security implications, clinging to standards and the needs of your product.

Use strong supporting technological aids

You need to pick the right technology and digital solutions to back your application. Your data storing space, security tools, backup software, optimizing services, and so on should be strong and secure, to keep your app consistent. Without this, the drastic decrease in performance may occur.

Solutions to the Most Common Challenges in AI Technology?

How AI Solutions Are Solving 5 Long-Standing Business Challenges

Like any other technology, there is always a series of challenges attached to AI as well. The basic working principle behind machine learning is the availability of enough resource data as a training sample. And as a benchmark of learning, the size of training sample data should be large enough so as to ensure a fundamental perfection in the AI algorithm.

In order to avoid the risks of misinterpretation of visual cues or any other digital information by the machine or mobile application, the following are the various methods which can be used:

1. Hard sample mining 

When a subject consists of several objects similar to the main object, the machine is ought to confuse between those objects if the sample size provided for analysis as the example if not big enough. Differentiating between different objects with the help of multiple examples is how the machine learns to analyze which object is the central object.

2. Data augmentation 

When there is an image in question in which the machine or mobile application is required to identify a central image, there should be modifications made to the entire image keeping the subject unchanged, thereby enabling the app to register the main object in a variety of environments.

3. Data addition imitation 

In this method, some of the data is nullified keeping only the information about the central object. This is done so that the machine memory only contains the data regarding the main subject image and not about the surrounding objects.

Concluding Thoughts

Now that you know the reasons and how to implement mobile apps, it is time to apply the top-notch performance and quality for AI and ML together to bring out the best in the application. AI and ML together are the future of advancement of mobile app development.

If you are still confused and want to clear your doubts, you can contact us. If you are looking to develop an app that is advancing with the time and technology and want to update your existing app with all the latest technology features, then you should partner with ML and AI development company that is well-adapted with the changing market needs. You can also opt for professional development providers in your area like AI development services USA or other regions. But make sure you choose the best to get quality results.


AI Technology in Mobile Banking Apps: Redesigning the User Experience

Essentials of Efficient UI/UX Design in Mobile App Development - Mobile Patterns

From Siri in the phone to Netflix recommendations on Smart TV, the Artificial Intelligence technology is revolutionizing our lives. It has become the hottest technology of the present time. Every small to medium businesses- from food chains to personal fitness centers are employing this technology to reap higher benefits. And finance industry is not at all lagging behind. Finance companies are also hiring the top mobile app developers to invest in this technology and enjoy its perks. Before we begin to delve the benefits of Artificial Intelligence in Finance and Banking sector, let’s have a quick recap of what is it all about.

Artificial Intelligence (AI) is a fusion of three state-of-the-art technology, namely Machine Learning, Natural Language Processing (NLP), and Cognitive Computing. The main idea behind this technology is to mimic the human intelligence and provide better communication between the humans & machines.

Artificial Intelligence Technology & Banking Mobile Apps

In the war of gaining a competitive edge in the marketplace, it is quite tough to rely completely on humans. The humans have a limit to the speed with which they do any task. They can’t act with the speed of light to deliver an impressive experience to multiple users at the same time. However, human intelligence machine can do this. And this is the prime reason they are gaining such a huge traction in the market.

Following are some of the benefits finance and banking industry is enjoying with the help of AI:

Customer Support

Customer Support: Why it's Important & How to Improve Your Efforts

The banking sector is adopting the AI technology to enhance its support system and offer better value to users. The AI-based customer service Chatbots are imitating the human interactions and providing them desirable information/result in no time. This feature is helping users with relevant information and in tasks like recovering passwords. This way, it is cutting down the workload of financial specialists, empowering them to focus on more crucial tasks.

“Artificial Intelligence allows us to offer an experience that doesn’t require our customers to go through several pages on our website; they can easily get the information through simple conversations. This is a great time-saving convenience for busy users who are already using Messenger.”

– Steve Ellis, Head of Wells Fargo’s Innovation Group

Fraud Detection Technology

Fraud Prevention Tools For Online Business

Fraud detection is one of the key areas excelled by Finance world with the implementation of Artificial Intelligence. The AI equipped apps easily identify the fraudulent activities and alert the customers. It also helps payment providers and retailers in examining and securing financial activities in connection with their firms.

Financial Planning

Types of Investment in India – Max Life Insurance

The Artificial Intelligence plays a significant role in assisting the users to choose the right plan for them. Using the data from the past activities of the users and offers from banks such as credit card plans, investment policies, funds, etc., the AI-enabled mobile applications are offering the most optimal recommendations to the users.

This feature is the core factor behind the revenue growth in the major banks and so, is highly in demand by many other entrepreneurs. If you are also in the finance business, reach to the top mobile application development companies today!

Automated Transaction

The Benefits of Transaction Automation | Direct Supply

The users love automation and the opportunity of running timeline crucial tasks. Because of the same, the AI-based applications are ruling the industry. At Anteelo, our adept mobile app developers have built various projects based on automated functionalities.

Personalized Remainder

How to Get a Remainder in Your Calculator

Various finance companies are implementing the AI based personalized reminders into their mobile apps. Just like an alarm clock, these reminders remain active for a certain period of time to enable users to perform a particular task on time. This feature is helping the users in renewing their policies and other such financial transactions before the time passes.

Wrapping Up!

Artificial Intelligence is heavily influencing the market, especially the finance industry. The industry’s leading giants have already begun to enjoy its benefits, setting a new stage of the competition. If you are a finance or banking corporation, it’s high time to hire the best mobile app developers and get your own AI-integrated app. Otherwise, you might find difficult to remain on the apex of the market.

How Artificial Intelligence is Transforming the Mobile Economy

How Artificial Intelligence is transforming the Mobile Economy

Artificial Intelligence is continuing to be a buzzword in the market. Everyone is discussing over virtual assistants, chatbots and self-driving cars, and are putting their ideas and thoughts around it. It has become one of the highest searched topics on the Internet, with the involvement of various reputed entities across the globe investing their time, efforts and money into the technology. According to the latest report, Alphabet put around $30 billion for unleashing AI technology, while Baidu has invested nearly $20 billion in the technology last year. Not just this, the China government is also focusing on AI development with the perspective of controlling the future cornerstone innovation. With all these things into consideration, it can be easily said that the business world is progressively turning towards Artificial Intelligence, and we will see various significant changes in the industrial world in 2018. While AI is bringing the next revolution in various business verticals, including healthcare, finance, education and travel, the most significant change is in the mobile economy. The technology is disrupting the mobile app industry, paving a new way to meet the users’ expectation and reap the benefits of app development.

Curious to know how will AI transform the mobile market?

Here are the 7 ways AI is Reforming the Mobile App Industry in Terms of User Engagement and Developer App Revenue

1. Better Conversational Experience

How Artificial Intelligence is transforming the Mobile Economy

Artificial Intelligence is filling the gap between the consumers and brands. The technology, in the form of Chatbots, is offering exceptional conversational experience to the customers. It is not only managing multiple clients at a time but is also proving to be the right solution for satisfying their needs with personalized products/services. According to a survey, 95% of smartphone users feel that customer service has been improved with the inception of chatbots. Another survey revealed that 15% of users have communicated with a bot in the past 12 months, while 35% of customers wish to see more companies investing in chatbot development. In addition to these, it has been expected that 80% of businesses will employ bots by 2020.

This clearly defines that AI/Chatbots are here to stay for a long, and disrupt the market in a positive way.

2. App Personalization

How Artificial Intelligence is transforming the Mobile Economy

By integrating AI into their mobile apps, developers and brands are able to facilitate personalized experience to their users.

Apps will trace the user’s location and provide location-based results automatically; the user need not enter the location every time. Secondly, the technology will let the brands gather in-detailed information about customers via different means, such as online traffic, mobile devices, PoS machines, etc. The collected data along with the recorded user behavior will be further used to provide personalized results. This will improve the user retention rates. Besides, the mobile applications will let the user enjoy exceptional services without making many efforts. For example, Starbucks AI-powered app, My Starbucks Barista allow customers to place an order by just speaking the product they want.

On a larger scale, AI will revamp the search algorithm, shifting focus to richer contextual and personalized app experience.

3. Seamless Search Experience

How Artificial Intelligence is transforming the Mobile Economy

AI is streamlining the user experience by empowering them to make their searches not only via text but also through images and voice. In fact, the technology is also enabling them to make searches in their natural language. This has taken the mobile app experience to the next level, by making it necessary for the app developers to integrate image recognition, voice recognition, and app localization features into their mobile apps.

4. Onboard Experience and Gamification

Gamification: Top 5 Key Principles | by Peter K | UX Planet

Nearly 25% of app users never return to the app after the first try. To minimize these numbers, app developers are turning towards AI. The technological effort understands human psychology and enables the UI/UX designers to design accordingly. It prevents users from memorizing all the details by securely storing with it, which indicates AI is enhancing the app usability. Besides, it let the users have access to various in-app gestures, which turns their app experience to be more enticing.

5. Improved App Security

Here's A Short Guide On Improving Your Mobile App Security in 2021

AI technology will cater the biggest concern of mobile app developers, i.e, security. With parallel technologies like Predictive Analysis and Machine learning, the technology will help in making predictions related to app security and vulnerability levels. The technology will keep an eye on the user behavior pattern and alert is anything suspicious is found. Not only this, the technology will also recommend or implement changes to improve the security of the app. It will also introduce new ways of logging into/out of the mobile application.

In addition to evaluating and implementing improvements in the security mechanism, the Artificial Intelligence technology will also let the app developers find potential security “holes” and “backdoors”, reducing the risk of intruder attack by employing these security leakage points.

6. Enhanced App Marketing

31 Powerful Mobile App Marketing Strategies to Implement

Not only the app development and engagement, the Artificial Intelligence will also refine the app marketing scenario. The AI-enabled machines and apps study the market trends and user behavioral pattern to provide real-time, detailed demographics. By this, these machines and apps not only cut down the efforts of marketers in gathering data but also reduces the chances of an error. By serving with real-time data, the technology enables the marketers to make marketing strategies with a futuristic approach. This improves the conversion rates and sales.

According to a Gartner Research, 30% of companies will employ AI for sales by 2020, which defines investing in AI to be the need of the hour.

7. AI and IoT

Artificial Intelligence (AI) in Internet of things (IoT) for a Better Future

AI is also playing a pivotal role in the growth of IoT. The technology enables the connected devices to collect real-time data and take a decision on their own. In other words, AI-powered mobile apps empower connected devices to learn from the information exchange pattern and act accordingly.

According to statistics, there will be more than 50 billion connected devices by the year 2020, which means a higher demand for AI apps to control these devices.

Artificial Intelligence technology is slowly and gradually revamping the mobile economy with its potential to collect real-time data, understand human emotions and provide a personalized experience. And now, with the announcement of AI chips in the 5G smartphones, the future of the mobile economy is going to be more intelligent and interesting.

Elements That Are Making Your Food Technical: AI, AR, and VR

5 Technology Trends That Will Shape 2017 and Beyond — AI, AR/VR, Analytics, Cloud and Data Science | by Hari Gottipati | Medium

The Ways Artificial Intelligence and Augmented, Virtual Reality Are Impacting the Food and Beverage Industry are too many and too omnipresent to ignore. From inside the kitchen/ store to the food production chain, the three technologies have found their place right at the center of the industry.

While in one restaurant, diners are analysing their calorie count that has come attached with their order using Augmented Reality, on the other side Food brands are using AI based chatbots to send out messages to users according to their eating behaviour.

Although, it’ll be difficult to find case studies on the exact implication of the three disruptive technologies in the industry, but here are the use cases of Augmented Reality, Artificial Intelligence, and Virtual Reality that have started surfacing over time.

In this article, we will talk about how the industry is transforming with the advent of the technologies.

Let us first look at the implications of Augmented Reality and Virtual Reality in the modern F&B industry

IoT use cases for food and beverage industry | IoT in Retail Industry

1. Educate Diners of Nutritional Components

With the help of Augmented Reality, restaurants can give their diners a detailed information of the ingredients and their corresponding nutritional value, which is present in the dish that they have ordered.

Using Virtual Reality, restaurants can also create a virtual chef and introduce him to the diners as they look at how their food is prepared in action.

2. Adding Gamification Elements to the Waiting Time

The long waiting time once the food order has been placed, is one of the biggest turn off points for a diner. In fact, it is also the number one reason behind diners not coming to the restaurant twice. To solve the non returning customer issue, restaurants have started introducing the power of virtual reality in their everyday process.

As customers wait for their order, they can join the kitchen and become a sous chef, or they can take a deep dive in the oceans with seals and dolphins in the ocean themed restaurant, all through the advancement of virtual reality.

Also, while the customers wait, Augmented Reality game where they have to look for hidden spoons on the table or count the cherries in the dish can be introduced.

3. Making Chefs, Bartenders, and Waiters More Skilled

With the help of Virtual Reality, Food and Beverage industry would be able to elevate the learning process. The technology can be used to impart skills related to anything – making impressions on cappuccino, carrying four plates at a time to diner’s table, and the art of mixing showman’s skills into bartending.

Real life simulation of tasks using Virtual Reality would lower both learning time and on the job mistakes.

While Augmented Reality and Virtual Reality are more focussed on the experience part of the diners, Artificial Intelligence looks into the Food and Beverage system from the business or backend front.

Here are the ways Artificial Intelligence domain betters the Food industry –

AI in Food Manufacturing - FutureBridge

1. Slash Food and Drink Production Cost

An AI system, which closely monitors the level of dirt on cooking equipment to make sure that they are clean, holds the power to save over £100m in UK food industry.

The AI system, which is being built by the Derbyshire-based Martec of Whitwell uses optical fluorescence imaging and ultrasonic sensing to measure the exact dirt levels present in the equipment. The AI system is expected to save over 20 – 50% of the cleaning time and energy that the food industry puts behind this part of the production chain.

2. Assortment Management

By studying the buying behaviour of local purchasers, Artificial Intelligence will help in better management of Food Shelves in the supermarket. By giving the owners detailed information on which product is most in-demand, in what quantity, and in what season, the businesses would be able to concentrate on specific items, while cutting down on filling of shelf space with those that are not in demand.

3. Planned Menu

By analysing the diners’ order request is not just your restaurant but also in those that they otherwise frequent, Artificial Intelligence will help you plan your menu according to diners food preferences. It would also give you an idea of their taste preferences, whether they like their food spicy or have a sweet tooth.

When broken down to a more atomic level, AI would also get you insights on diners’ food preference when there is a football match on or when they are nearing end of month with their salary level now standing at single or early double digits.

4. Lesser Food Wastage

The current Food and Beverage Industry is marked by a series of strict rules and policies related to food quality that is expected to be maintained in restaurants and fast food chain. The rules around quality of tomatoes that can go as slice in burger might lead to a lot of wastage in case they don’t meet the criteria.

With Artificial Intelligence, the actual expectation bar would be raised. The technology would give information on exactly what the users expect and the room they can work around when it comes to tomato slices size and french fries length.

5. Development of new Food Items

By reading the users’ reviews on what taste they are developing, what combination they would like to be introduced, and what they need as a blend of their health and taste preference, the world will start looking into more and more food items getting developed at the back of AI insights.

IntelligentX is one such brand that has developed a series of premium beers according to the users’ feedback collected with the help of AI, and so long the brand is seeing a huge success.

While these are just some of the ways Augmented Reality, Virtual Reality, and Artificial Intelligence hold the power to alter and revolutionize the Food and Beverage industry, the time to come will look into more application of the technologies.

Improve AI Customer Experience Strategy [2019-2020 Guide]

ai in travel | How Artificial Intelligence is Reforming The Travel Industry

Artificial Intelligence is no longer science fiction. More and more businesses are showing interest in understanding the basic mechanisms of AI and ways to use the technology for enhancing customer engagement and experience.

But, is the technology really effective? And can it really make a difference in upgrading your customer experience strategy?

Let’s find answers in this article – starting from the very basic, i.e, why you should pay attention to Customer experience.

Table of Content

  1. Why Should Businesses Focus on Customer Experience?
  2. Role of AI In Customer Experience
  3. Different Industries Delivering Higher Customer Experience with AI
  4. Future of Artificial Intelligence in Customer Experience
  5. Steps to Use AI for Delivering Better Customer Experience
  6. Other Technologies that are Innovating Customer Experience in 2019 & Beyond
  7. Frequently Asked Questions (FAQs) about AI in Customer Experience

Why Should Businesses Focus on Customer Experience?

How AI Improves Customer Experience: 6 Benefits - Acquire

“Customer Experience is the new battlefield” –  Chris Pemberton, Gartner

With people understanding the difference between User Experience and Customer Experience, the latter term is becoming the key to unlock unparalleled opportunities in the business market. It has become imperative to the process of understanding your customers and planning a marketing strategy using these insights to give a personalized experience. Thus becoming imperative to gain higher success in the marketplace.

And this can be clearly proven from the following statistics:-

Now as we have taken a glance of why to focus on customer experience, let’s jump directly into where AI stands in all of this. What does AI means to the CX world in 2019. Or better say, what are the advantages of using Artificial Intelligence in your Customer Experience strategy.

Role of Artificial Intelligence (AI) in Customer Experience

1. Know Your Customer

5 ways to KYC your customer | Veri5Digital

One of the foremost reasons why you should use AI to improve customer experience strategy is that it serves you with ample real-time user data. It helps you gather and analyze user data in real-time and in this way, enable you to remain familiar with the change in their behavior and expectations.

2. Simplicity, Efficiency, and Productivity

Benefits of Simplicity to Productivity

Another reason for using AI to improve customer experience is that it adds simplicity, efficiency, and productivity to the business processes.

The technology, in the form of Chatbots and self-driving software, automates repetitive processes which means the efforts and time required for performing repetitive tasks cut down to a half.

It also gathers and analyzes the user data in real-time to help you introduce the features and concepts that they want and in the way, they wish to interact with. Moreover, the inclusion of AI in quality assurance helps you to design an innovative mobile application with a higher scope of efficiency and simple structure.

Besides, these AI-powered bots and platforms perform most of the routine work and give the workforce an opportunity to perform other productive tasks.

3. Better Decision Making

Article: Backchannelling leads to better decision-making: Research — People Matters

Artificial Intelligence is also acting as the right companion for business in terms of the decision-making process. The technology looks into the user interaction history as well as the current market trends, which makes it easier for businesses to predict the future. This eventually provides them with clarity of what feature/functionality to introduce in their business solution for gaining huge momentum in the market.

4. Streamline Purchase Process

Streamline your Procurement Process | A Complete Guide - frevvo Blog

In the present scenario, various customers add products into their cart but never proceed due to slow loading, complicated check out process, and more. Artificial Intelligence, in this context, helps in understanding the challenges faced by the customers and deliver a seamless purchase experience – something that helps businesses to lower down app cart abandonment rate.

5. Fraud Detection

4 ways government program managers can solve the fraud Catch-22 -- GCN

One of the prime uses of Artificial intelligence in finance, retail, and other industries, in terms of customer experience, is that it helps in detecting fraud. The technology, using its potential to gather, store and compare user data in real-time, is making it easier to identify any change in the actions of users, and thus, helping with taking a timely preventive measures against frauds.

6. Customer Analytics

Developing a Comprehensive Customer Analytics Strategy - Wharton@Work

Artificial Intelligence is also showing a remarkable significance in the customer data analytics process. The AI-enabled tools and platforms are simplifying the process to gather a heap of user data from different sources and arrange them effectively as per the key factors.

Furthermore, Artificial Intelligence is making it possible to predict the context of user interactions and build better customer engagement strategies using the right use cases of the technology and insights gained from the data quickly and precisely.

7. Self-Service

An A – Z of IT Self-Service Success Tips | Joe The IT Guy

Many customers these days prefer doing everything on their own rather than hiring an agent or taking help from any machine. This is yet another reason why investing in AI is becoming the need of the hour.

Artificial Intelligence, as we already know, provides you with valuable insights about where customers get stuck and what doubts/queries make them connect with your support team. Using these insights, you can provide users with some options or FAQs that give them a feeling that they have found out the solution to their problem without any interaction, or better say, on their own.

8. Visual, Text, and Voice engagement

How to Use Visuals & Imagery to Improve Content Engagement — Setka Editor

AI-powered platforms are also providing the opportunity to deliver optimal customer experience to the targeted audience based upon their voice or facial expressions.

The technology, using Facial recognition and Virtual assistants, is making it easier to get an idea of the users’ emotions and sentiments at any particular time, and identify ways to deliver an instant positive effect to them through offers or refunds, etc. such that businesses gain long-term profits.

9. Predictive Personalized Experience

How Adobe Experience Platform Predictive Audiences Improves Personalized Experiences | by Jaemi Bremner | Adobe Tech Blog | Medium

Last but not least, AI is making it easier for startups and established brands to analyze the user interaction history and predict their next move and hence, use the information gained to provide them with a perfect marketing offer. And in this way, gaining higher customer engagement and profits.

While this is all about how Artificial Intelligence (AI) improves Customer experience in general, let’s figure out what the technology mean to different business verticals and their customer experience efforts in 2020 & beyond.

Different Industries Delivering Higher Customer Experience With AI

1. Retail

Disruption in Retail — AI, Machine Learning & Big Data | by Prannoiy Chandran | Towards Data Science

When talking about industries that AI is transforming, the very first business domain that comes into the limelight is Retail.

The technology, using a heap of transactional data and machine learning, is making it possible to track and analyze purchase history and behavior of customers, which in turn is helping with determining when and what promotional offer/message to be delivered for getting attention of customers and thus, gain higher ROI.

A clear evidence of the impact of AI in retail is that, as per a survey of 400 retail executives by Capgemini, it was highlighted that the technology will save around $340B annually for retailers by the year 2022.

The survey also revealed that the use of Artificial Intelligence in Retailing customer experience has resulted in a 9.4% increase in customer satisfaction and a 5.0% decrease in user churn rate. An example of how brands are focusing on the usage of AI for bettering customer experience can be seen in Nike’s acquisition of Celect for predicting users’ shopping behaviour.

2. Healthcare

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AI is transforming healthcare in different ways – with customer experience being on the top.

The technology is proving to be the nervous system of the healthcare user experience ecosystem by making it easier to analyze the patient health history and come up with medical treatment (or surgery) that offers higher chances of success.

It is also helping healthcare organizations in providing the best assistance to every patient in the form of Virtual Nursing assistants and thus, taking care of everything – right form notifying about the medicine intake timings to sharing real-time health data with the corresponding doctors.

An impact of this is that the AI health market is predicted to cross $6.6B by the year 2021, with a CAGR of 40%.

3. Entertainment

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AI and its subset, Machine Learning are also leaving no stone unturned in delivering exemplary customer experience in the Entertainment domain. Clear evidence of which is Netflix.

The Entertainment platform is able to get a clear idea of the user behavior, needs, and expectations, and thus, showcase personalized options onto the screen. This is improving the customer retention rate as well as boosting customer loyalty – eventually resulting in higher profits.

To know further about the use of Artificial Intelligence in delivering impeccable customer experience on the Netflix platform, check out this video:-

4. Mobile Banking and Finance

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Artificial Intelligence is also revamping user experience in mobile banking and finance apps. The technology, in the form of Chatbots, is providing 24×7 assistance to users and helping them in determining the right financial plan for themselves. It is also detecting and lowering down the risk of fraud in the processes – ultimately resulting in better customer engagement and retention rate.

As we have covered in this article so far, Artificial Intelligence is helping industries in revamping customer experience one way or the other. But, will this continue to happen in the future also? Will AI be a part of customer experience in upcoming years?

Let’s look into what is the future of AI in the field of Customer Experience to find definite answers to these questions.

Future of Artificial Intelligence in Customer Experience

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The AI market has grown exponentially in the past few years. Over 1,500 companies including Microsoft, Google, IBM, and Amazon have invested their efforts into developing next gen apps for delivering higher customer experience and it is expected that many more will join the bandwagon. Many more companies will trust the AI’s ability to boost productivity and reduce the time and cost involved – something that can be predicted from the statistics shared below.

The technology will revolutionize the future of the business world and the customer experience in numerous ways, such as:-

  1. It will automate routine work and encourage humans to focus on creative things. It will help pay attention to their vision and not on every minor detail of production.
  2. It will make the business-customer interactions go from ‘one click’ to ‘zero click’ – giving a seamless and timeless experience to the target user base.
  3. AI will also leave a significant impact on connectivity networks. It will encourage the idea of pattern analysis to troubleshoot any problem, pull out important user information from multiple channels to quickly and effectively get an idea of what users need.
  4. Above all, Artificial Intelligence will also put the practice of gaining biased data to an end, eventually resulting in a better quality of information gained.

Now as we have covered what, when and how Artificial Intelligence drives customer experience, it’s the best time to head towards how businesses can integrate this technology to gain better insights and improve customer experience in 2020 and beyond.

Steps to Use AI for Delivering Better Customer Experience

1. Design a Customer Experience (CX) Strategy.

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Before looking into how AI improves customer experience, it is necessary to have a clear understanding of your CX vision and strategy. So, bring your team on board to discuss your ‘CX-based’ expectations and ways you follow to meet those expectations. And, based on the insights gained, create/update a robust Customer Experience strategy.

2. Plan and Analyze User Journeys

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Right from discovery to pre-sales, sales, customer support, and beyond, a user connects with your brand at different touchpoints and platforms. So, invest your time and effort into getting a comprehensive knowledge of all those connecting points, and deliver an AI-based omni-channel customer experience.

3. Have a Clear Understanding of AI solutions

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The first step of AI project management lies in understanding that the technology can be used in different forms to improve customer experience strategy, such as Recommendation engines, Virtual assistants, Predictive search engines, Computer vision, Sentimental analyzing tools, etc. However, not all can be the right fit for your business needs and expectations.

So, the next step to employ AI in your Customer experience strategy is to determine what all forms of technology can be integrated into your business model.

4. Decide Whether to Create/Buy AI solutions

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When talking about how to improve customer experience using AI, the next step to consider is to determine whether to integrate AI in your existing application or invest in a pre-made CX/AI solution.

Here, the former one will be the right option for your business, if you have a well-qualified AI expert team in-house or have a partnership with the right AI specialized mobile application development agency. Whereas going with the latter option can be a profitable deal when you have less time to develop an application and the vendor understands your customer issues and has the caliber to focus on critical points.

5. Track and Measure Success

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Lastly, taking the backseat just after incorporating Artificial Intelligence in your CX strategy is not enough. It is imperative to keep a watch on key performance indicators (KPIs) and metrics to track the success ratio of combining Artificial Intelligence (AI) and customer experience. And hence, improve your strategy for a better future.

ALSO READ: Key Metrics to Evaluate Your Chatbot’s Performance

While this is all about how the use of Artificial Intelligence in Customer Experience can bring better outcomes and what steps to consider for implementing it in your strategy, let’s take this conversation further by exploring other possibilities.

Or better say, let’s look into what all other technologies can aid in the process to improve customer experience strategy in 2019-2020 and beyond.

Other Technologies That Are Innovating Customer Experience in 2019-2010 & Beyond

1. Internet of Things (IoT)

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In 2019-2020, the number of connected IoT devices will reach 26 billion. Besides, the 5G technology will become more significant in the market with high-speed, lower latency, and other such features.

This will open new doors for universal connectivity – making it possible for the companies to find better insights to understand customer behavior and lifestyle and thus, come with valuable data points and strategies to deliver memorable customer experience.

Or better say, it will help companies to work with facts and not just assumptions about customer needs and expectations, and eventually redefine their Customer experience strategy.

2. Machine Learning

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With a rise in IoT-based solutions, the volume of data points will also increase gradually. Clear evidence of which is that there will be around 45,000 Exabytes of data volume in the market the year 2020.

Now, with an increase in data volume, the process of gathering, optimizing, and operating data will become a challenge – something that Machine Learning will help with.

Machine learning, with its self-learning algorithms, will enable companies to perform better actions on the data and find new approaches to improve customer experience.

3. Blockchain

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Blockchain is also acting as a catalyst in the process of improving customer experience. The technology, with its key features like decentralization, transparency, and immutability, is making it possible for companies to store user behavioral and demographic data on blocks securely, make them portable and letting users decide with whom to share their immutable details with. The technology enables users to know what exactly is happening with their personal information and thus, experience a sense of security and trustability throughout the process.

4. Voice Technology

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Not only Artificial Intelligence, but Voice technology will also be seen playing an indispensable role in improving customer experience.

The technology, in the form of Voice search and Digital assistants, will continue to help businesses in delivering a faster, seamless and flexible experience to their target audience. It will enable businesses to engage users in a profitable manner and facilitate them with better actions.

And this can be proven by a study by Pindrop, which states that around 28% of companies have already embraced voice technology in their CX strategy while 57% are planning to deploy in the next one year. Also, another 88% believe that voice technology will give a competitive advantage in enhancing user experience.

5. AR/VR

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Lastly, AR/VR is also one of the technologies that are reshaping the world of customer experience.

The technology takes users to the virtual world and enhance their customer journey effectively. It presents feedback form in different ways and increases the chances of getting a positive reply. And above all, it helps in product testing by exposing user/product to different situations and places.

With this, we have covered all about the process and use of Artificial Intelligence in Customer Experience. We have also unveiled what is the future of AI in the CX world as well as what all other technologies will disrupt the world of Customer Experience.

If you still have any doubts, feel free to check the FAQs shared below or directly get in touch with our AI mobility experts.

Frequently Asked Questions about AI in Customer Experience

1. What is the Role of AI in Customer Experience?

AI plays a crucial role in improving customer experience in the business domain in terms of automating repetitive tasks, streamlining processes, reducing the risk of fraud, and above all, delivering personalized options to every individual.

2. Why use AI to improve Customer Experience?

Artificial Intelligence, with its power to gather and analyze customer data in real-time, is helping in getting a better understanding of customer behavior and needs, and eventually creating a personalized customer experience strategy.

3. How AI and Machine Learning are improving Customer Experience?

AI and Machine learning are enhancing customer experience in multiple ways, including streamlining shopping experience, reducing the risk of fraud, and delivering personalized marketing schemes.

4. How to Start using AI to improve Customer Experience?

There are four steps to start using AI to improve customer experience:-

  • Design a Customer Experience (CX) Strategy
  • Plan and Analyze User Journeys
  • Have a Clear Understanding of AI solutions
  • Decide Whether to Create/Buy AI Solutions
  • Track and Measure Success

5. How AI will shift Customer Experience to the Next Level?

AI will bring a drastic shift in Customer experience in the future in the following ways:-

  • It will encourage users to focus more on their vision and creativity, rather than looking into minor details of production.
  • It will turn ‘One Click’ experience to ‘Zero Click’, providing target audience with a quick and seamless experience.
  • It will improve connectivity networks.
  • It will encourage the idea of gathering and employing unbiased society data and deliver quality to all.
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