#ArtificialIntelligence Archives

7 Benefits of Artificial Intelligence for Business

Posted on 05/11/202105/11/2021 by Tushar

One of the newest benefits of cloud computing is that it enables businesses to take advantage of artificial intelligence (AI). This rapidly developing technology offers significant development opportunities that many companies have already been quick to seize upon. In this post, we’ll look at some of the ways your company can benefit from cloud-based Artificial Intelligence.

1. Improving personalized shopping experiences

Providing customers with personalised marketing increases engagement, helps generate customer loyalty and improves sales. This is why companies are putting so much effort into it. One of the advantages of using AI is that it is able to identify patterns in customersâ€™ browsing habits and purchasing behaviour. Using the millions of transactions stored and analysed in the cloud, AI is able to provide highly accurate offers to individual customers.

2. Automating customer interactions

Most customer interactions, such as emails, online chat, social media conversations and telephone calls, currently require human involvement.Artificial Intelligence, however, is enabling companies to automate these communications. By analysing data collected from previous communications it is possible to program computers to respond accurately to customers and deal with their enquiries. Whatâ€™s more, when AI is combined with machine learning, the more the AI platforms interact, the better they become.

One example of this is AI Chatbots which, unlike humans, can interact with unlimited customers at the same time and can both respond and initiate communication â€“ whether on a website or an app.

It is estimated, that by 2020, 85 percent of all customer interactions will be taken care of by intelligent machines that are able to replicate human functions. The days of using a call centre look like they are coming to a close.

3. Real-time Assistance

Artificial Intelligence is also useful for businesses that need to constantly communicate with high volumes of customers throughout each day. For example, in the transport industry, bus, train and airlines companies, which can have millions of passengers a day, can use AI to interact, in real-time, to send personalised travel information, such as notice of delays. Some bus companies, for example, are already tracking the location of their buses and using AI to provide travellers with real-time updates about where the bus is along its route and its estimated time of arrival. Customers receive this information on the bus company app.

4. Data mining

One of the biggest advantages of using cloud-based AI is that artificial intelligence apps are able to quickly discover important and relevant findings during the processing of big data. This can provide businesses with previously undiscovered insights that can help give it an advantage in the marketplace.

5.Operational automation

AI is able to operate other technologies that increase automation in business. For example, AI can be used to control robots in factories or maintain ideal temperatures through intelligent heating. In Japan, human-looking robots now serve as receptionists in some of the countriesâ€™ hotels automating check-ins, booking services and dealing (in four languages) with customer enquiries. In retail, Artificial Intelligence is also being linked with RFID and cloud technology to track inventory. In China, police forces use AI to catch criminals. The country has a vast CCTV network and AI uses facial recognition to spot and track suspects so that they can be apprehended.

6.Predicting outcomes

Another advantage of AI is that it is able to predict outcomes based on data analysis. For example, it sees patterns in customer data that can show whether the products currently on sale are likely to sell and in what volumes. It will also predict when the demand will tail off. This can be very useful in helping a company purchase the correct stock and in the correct volumes. It is predicted that, within 10 years, the days of seasonal sales will be over as AI will mean there is too little leftover stock to sell off.

This ability to predict is not just useful in retail. Artificial Intelligence is also being used in many other areas, for example, in banking where it can predict currency and stock price fluctuations or in healthcare where, remarkably, it can predict outbreaks of infections by analysing social media posts.

7.Improve the recruitment process

It may be bad news for recruitment companies, but AI is now helping businesses automate the recruitment of new employees. It is able to quickly sift through applications, automatically rejecting those which do not meet the companyâ€™s personal specification. This not only saves time (or money spent on a recruitment agency), but it also ensures that there is no discrimination or bias in the shortlisting process. The AI programs available can even take care of the many administrative tasks of recruitment.

Conclusion

As you can see, AI systems provide businesses with a wide range of benefits, including personalised marketing, customer service, operational automation, inventory management and recruitment. And these are just a few of the many ways AI can be used. Whatâ€™s remarkable, however, is that many of the AI apps, which are designed specifically for cloud-based systems, are quickly and easily deployable. Companies whose systems are in the cloud can be benefitting from them in no time at all.

5 Ways Fintech Industry is Using Artificial Intelligence

Posted on 30/09/202113/11/2021 by Tushar

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.

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.

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

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

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

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

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 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

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.

Here’s how AI is transforming business processes

Posted on 16/06/202005/06/2021 by Blogs

Business process are transformed through AI

The rise of artificial intelligence (AI) to drive business value has been truly incredible in recent years. Enterprises that recognize the power of AI and know how to effectively apply it to their business can reap significant rewards in a quickly evolving and hyper-competitive marketplace.

Just a few years ago, analytics was all about gaining insights from data to help make better business decisions. More recently, enterprises have been seeing massive benefits from adding AI to the analytics mix that is designed to transform and strategically influence business processes. The effective application of AI within business process transformation can produce many benefits including more efficient operations, faster delivery and reduced costs.

But how is AI actually helping companies transform business processes?

There are two primary ways AI is doing this today. Some enterprises are actively implementing AI programs company-wide as part of their core functionality. And there are other companies that are building AI into their business in a sequential, controlled way via managed proof-of-concepts (POCs) to address particular aspects of their operations.

Artificial Intelligence Is Transforming Business

AI is specifically used as a tool to speed up the corporate buying process. It does this in two ways: by making recommendations of suitable suppliers who should be invited to a tendering process and by quickly sifting through dozens of supplier submissions and creating a ranking system to identify the best supplier agencies for bespoke projects. This type of accelerated procurement can shave weeks off the selection process and additionally save up to 20% on project budgets.

On the other side of the spectrum is a large UK retail chain with thousands of stores across the country. This company is implementing AI in a controlled way through their corporate transformation department and rolling it out to each store. The retailer is conducting live trials of the system and can conduct A/B testing for different approaches for 10% or 20% of their stores from the get-go, which did not happen before.

This retailer is seeing success by implementing AI to assist with critical business functions such as setting prices and managing stock. AI is taking on the work previously performed by humans, including analyzing pertinent purchasing data to set prices intended to keep products flying off the shelves and boost profit margins.

From the two cases above, we’ve seen that currently the best performance in AI applications is achieved when AI is combined with humans; where AI does the core number crunching and recommendations and humans oversee the process. This helps humans concentrate on fine-tuning and making improvements on those initial recommendations.

Time and budget savings are achieved during the corporate buying process due to the presence of AI. And for the retailer, cost savings are clearly achieved as AI does the routine processing work that was previously performed manually by humans.

These uses of AI yield fewer mistakes and demonstrate how AI can support efforts to optimize spend, ultimately impacting the bottom line. For both companies, AI is the go-to solution for solving business problems.

Humans will always have a place in transforming business processes, that goes without saying. But AI is quickly becoming an invaluable automation tool to drive efficiencies and reduce costs.

Five essential pillars of AI-enabled business

Posted on 15/06/202005/06/2021 by Blogs

Successful AI implementations rarely hinge on the unique innovation of a specific algorithm or data science technique. Those are important factors, but even more foundational to successful AI enablement are the core data operations and enabling platforms. These act as the fuel and chassis of the AI machine that a business must build and evolve for continued competitive advantage.

Here are the five foundational elements to be addressed to enable a successful transformation to an AI-empowered business:

1. Define an integration strategy for embedding AI and analytic insights into business operations

Successful digital transformations focus on evolving and optimizing business operations through the better use of data assets combined with modern technologies such as machine learning, AI, and robotics. These paradigm shifts result in the creation of new operating patterns rather than simply more efficient legacy operations. In this way, digital transformation represents the enterprise operations in the way the business wants to be run, rather than the way it has been running due to technical and operational limitations and barriers constraining it.

To go beyond siloed or single-use insights and fully benefit from AI and analytics, it must first be decided how the business desires/needs to function in the future. Determining your business transformation priorities then evaluating the advanced technology and data science options for addressing them is a key step towards maturing and evolving to a data-driven enterprise. This understanding will identify the type of AI and analytics that will be the most beneficial for your business and the technology required to accomplish it. Additional thoughts on overall data strategy can be found in the white paper “Defining a data strategy: An essential component of your digital transformation journey.”

2. Establish a holistic data and analytics platform

Selecting and configuring an integrated set of technologies to support data management and applied analytics is a complex challenge. Fortunately, solutions to such technical integration have matured in recent years into pre-built core platform components and best practices that can be accelerated and augmented further through value-added third party software and partner services.

Cloud-based modular platform environments bring together technical flexibility and financial elasticity with an ever-maturing technical set of capabilities, including interoperability across hybrid environments that include legacy on-premises deployments and geographical federation. In addition to open source components, such platforms include the option to integrate select native modules and commercial technology components for broader flexibility and a customizable architecture that can be deployed as prebuilt services for simpler adoption and integration.

The tools to support and enable AI integration into business operations are beginning to leverage the same capabilities they enable. For example, data pipeline tools are beginning to use machine learning (ML), metadata tools are using AI and ML to identify content and auto-generate the metadata on the fly, and user interfaces are embedding chatbot and digital assistant AI technology to guide end-users through the complexities of data science for accelerated insights. By adopting toolsets and platforms that have embedded AI and analytics in their core, the use and integration of AI into business operations will be more natural and accelerated across the enterprise community.

3. Know your data

Fully understanding the data your enterprise has access to may seem like a fundamental need when supporting operational reporting and analytics within the enterprise. Many organizations, however, stop with simple source systems listings and maybe some high-level business definitions and schemas.

Truly knowing your data includes a lineage-based view of where the data comes from and what business process it represents, what operations are performed on it prior to your access, what transformations are performed thereafter, the associated level of quality and, of course, the core “Vs” of big data; volume, velocity, variety, veracity and value.

Building an easily searchable, enterprise-wide data catalog of information is one of the first steps towards empowering the enterprise with data. Exposing the catalog to a crowdsourced editing model ensures richer content and wider adoption of such information across the enterprise.

4. Control and govern your data

Understanding the types of controls and governance your data needs is a natural extension of knowing your data. By reviewing the types of data and their business content with associated metadata, enterprises can align and define proper governance and compliance policies related to internal policies and to external standards such as HIPAA for healthcare, PCI DSS for secure payments, and PII and GDPR for data privacy.

It is also important that source data retains its original state integrity without over processing or over-filtering it. Aligning to the data pipeline workflow principles of “ingest, refine, consume” allows the same data to be leveraged efficiently for different uses with different policies and operational needs while ensuring security. Such controls can also be extended to support and define quality standards required for using the available data and to trigger any necessary control processes to correct or adjust for deviations in such standards.

You can safeguard proper policy compliance, improve ease of use and increase trust and adoption by the end user community by ensuring that governance controls are built into your data management operations from the start.

5. Simplify access to your data

To further expand the adoption of AI and analytics, it is important to simplify and automate data workflows and the use of analytical tools. Reducing manual process overhead can significantly improve time to market and quality of results. Providing clear and flexible governance allows enterprises to control such access without it becoming a barrier for use.

Self-service leads to rapid user community adoption and better integration of data and insights into business operations. By reducing the dependency on IT resources for complex data integration and preparation tools, average business users can interact with the data through simple common interfaces and receive results in simple and easily consumable formats.

Once these foundational elements are in place, organizations can take full advantage of the unique value proposition offered by advanced analytics and AI. And they can do so with the confidence that the resulting solutions are enterprise-grade in their scalability, security, quality and usability. It is this kind of confidence that leads to business user adoption and, in turn, successful digital transformation.

Why Do SMEs Need to Be Digitally Transformed?

Posted on 15/04/202015/06/2021 by Tushar

Digital transformation is just something for big business, right? Erm, no! In fact, it’s not completely true that bigger organisations led the way: many of the early movers were the disrupter startups that were digital from the offset. Indeed, it’s being digital that enabled them to become disruptors and forced their larger competitors to react. Today, as more businesses and consumers shift towards digital technology, it’s more important than ever for SMEs to consider how digital transformation can help them stay relevant and competitive. Here, we’ll examine why.

Reacting to change

Shifting consumer behaviours, the evolution of new ways of working and the constant roll-out of new technologies means SMEs are consistently having to adapt quickly to change. Not only are more consumers moving online; they are demanding digital services that provide better customer experiences. Employees, meanwhile, are increasingly seeking to work with companies that enable them to work flexibly and away from the office. To acquire and keep new customers and talented employees, and keep up with competitors, SMEs need to be able to adopt new technologies quickly, for example, deploying remote working platforms for staff or personalisation engines for customers.

To do this, they need to be agile, and its digital transformation that enables this. In particular, it’s the adoption of cloud technology, an infrastructure that allows companies to deploy new servers and applications instantly and which provides them with all the scalable resources they need to undertake their workloads. Indeed, by migrating to the cloud, SMBs eradicate the need for expensive, in-house infrastructures, making them even more agile and putting them on a level playing field with their larger competitors.

Taking advantage of AI

Artificial intelligence is starting to permeate all areas of business operations and its easy accessibility means it’s increasingly being utilised in all sectors. Today, SMEs can use AI for a myriad of purposes. It can help find better ways to procure products or materials, make industrial processes faster and more efficient, monitor machine and system health, provide data insights that identify new opportunities in the marketplace, streamline logistics, deliver human-like chat conversations with customers and identify risk, whether that’s financial risk or risk to life.

A key element of digital transformation, AI is something most SMEs can gain enormous benefit from. The good news is that adopting AI is no longer the major technological leap it used to be. Its popularity means there is a growing number of both open-source and proprietary AI applications that are designed to integrate with company systems – the majority of which are cloud-based. For SMEs that have migrated to the cloud, such technology is merely a click away.

Cyber defence

Being an SME doesn’t protect a company from cyberattack, nor does it remove the obligation to comply with regulation and ensure that data is safe. For this reason, SMEs need to develop a robust security strategy and put disaster recovery and business continuity plans into place.

Though ransomware attacks grab the headlines, there are various other ways companies can fall victim, such as through hacking, phishing, malware infection and DDoS attack. Data can be stolen, systems taken offline, websites taken control of and money syphoned from company accounts. It’s a serious issue: 60% of companies that fall victim to a cyberattack fold within 6 months.

The growing sophistication of cyberattacks means criminals can now buy ransomware as a service or brute force software with built-in AI. As a result, SMEs need advanced tools to defend against them. The adoption of these tools is also a key element of digital transformation. Providing this level of security in-house, however, is not only expensive but requires expertise and this puts it beyond the means of many SMEs. In the cloud, however, both the expertise and advanced technologies like next-gen firewalls, intrusion and malware prevention, etc., are part of the service. Companies will also find that vendors provide backup, disaster recovery and business continuity solutions to help them recover swiftly should the worst happen.

Data storage

Data is essential for digital transformation as it provides the information that applications need to process in order to deliver improvements. Today, businesses gather huge quantities of data, not just from customers and their online activity, but from the monitoring of machinery, vehicles, IoT devices, LED lighting systems, energy usage, inventory systems and more. For digital transformation to work, finding the right storage solution for all that data is essential.

SMEs will need to centralise data in order to control access, create customer journeys and ensure employees have access to the latest versions of documents and files. As data grows, businesses need expandable storage rather than having to go through the risk or rigmarole of upgrading to a bigger server whenever capacity is low. That storage also needs to be secure. Cloud storage is ultra-fast, can be expanded easily, has security features like encryption and access control, and is more cost-effective than buying dedicated servers. Crucially, being in the cloud, it enables remote workers to access data wherever they have an internet connection.

Conclusion

More SMEs are undergoing digital transformation than ever before. Indeed, to stay agile and adapt to change, it is becoming a necessity rather than an ambition. It is, however, a necessity that brings with it many rewards. The starting point for digital transformation lies in the adoption of cloud technology, as it is cloud infrastructure with its robust security and cost-effective pricing, on which the applications, tools and platforms of digital transformation are best run.

Most popular questions companies have regarding AI and ML

Posted on 22/02/202015/06/2021 by Tushar

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly evolving to help companies with both digital transformation and innovation. There has been a lot of hype and discussion about these topics, and, in a very short time, we have moved the conversation from “AI is cool” to “AI can drive specific business outcomes.”

Experience has allowed companies to clarify the economics of AI and reduce time to value. In addition, the technology has continually improved, with advancements including:

extracting unstructured data for improved insights and processes
moving from simple chatbots to more sophisticated conversational assistants that are smart, use more natural language interaction (text and/or voice), and enable the initiation of transactions
integrating operational and knowledge management systems

As our clients start to understand more about AI/ML, there are 2 key questions typically asked:

Question 1: “How do AI and Machine Learning differ from traditional programmed systems?”

There are three different capabilities that AI/ML typically extends and enhances into existing applications. One aspect is understanding – enabling systems to understand language, other unstructured data, including pictures, just like humans do. A second aspect is reasoning – enabling systems to grasp underlying concepts, form hypotheses, and infer and extract ideas, similar to humans. The third aspect is focused on learning – improving over time and avoiding repeated mistakes. These capabilities, often referred to as U-R-L (understand, reason, learn), are easily integrated into existing applications as consumable APIs, can reduce time to value.

Question 2: “Where is the value from Artificial Intelligence/Machine Learning?”

As we look across client environments, most clients are very comfortable with structured data that is within their firewall – things like transaction systems, customer records, and even predictive models. Many analysts estimate that 80% of data created today is unstructured, which requires clients to expand their current perspectives in 2 dimensions:

Structured to unstructured, such as documents, transcripts, social media, weather, images, IoT and sensor data, and news
Within the firewall to outside the firewall, including public data, licensed private data, and new types/sources being created every day

From a value perspective, the enticing aspect of AI and machine learning is connecting these structured and unstructured data types within and outside these walls. This enables richer, more, and unexpected insights, new business processes, and improved workflows at dramatically reduced cost levels.

As AI and machine learning mature, three use cases patterns have emerged around customer care, human capital management and the rethinking/reimagining of processes thanks to insights gained from unstructured data and natural language capabilities.

We’ll expand on these use cases and how we are applying AI/ML for our clients in our next blog post.

AI and ML Advancement leading to new Application Emergement

Posted on 20/02/202017/06/2021 by Tushar

With most innovative technologies, at some point in the solution evolution there’s a tipping point from “this is cool” to “there are real business outcomes to gain.” We are now seeing that shift with artificial intelligence (AI) and machine learning (ML), especially in three major use case patterns:

1. Customer care

When it comes to customer care, many companies face a similar set of challenges: Customer service representatives (CSRs) typically need to view multiple windows on multiple screens to find answers to a caller’s questions. This results in calls taking a long time to handle, frustrated customers, lower CSR productivity and overall poor customer satisfaction.

AI and ML can help change these outcomes. One insurance customer implemented AI and ML to transform its contact center, which responds to approximately 5 million voice calls per year. It also introduced an intelligent voice agent to respond directly to 50-70% of all customer inquiries. The power of AI and ML extended and enhanced the customer experience and the productivity of CSRs, who are now able to deliver more accurate and consistent answers, at lower cost and greater speed. As a result, we have also seen reduced attrition, as the CSRs are happier in their roles and require less training time to become self-sufficient. Overall, by using AI and ML, the contact center is able to provide a high level of customer service, enhanced customer satisfaction and more efficient engagement.

In another example, a customer service center handling 60M service requests per year is implementing AI “smart chat” to transform customer service, drive better quality, improve consistency and lower costs.

The transformation of an operation as complex as a customer call center is a multi-step journey:

Organizing and visualizing the data required to support typical workflows is a critical early step and can include authenticating/validating the caller, providing easy access to the data from different back end systems, initiating transactions and closing out the call.
A second step adds natural language processing (NLP) to allow the CSR to ask more complex questions. NLP uses a trained ML model to determine the most likely answers from a collection of large, unstructured documents.
A third step includes self-service for external callers, using voice and natural language to respond directly to client inquiries without requiring interaction with a CSR.

While the steps themselves remain the same, the sequencing may vary to align to the specific outcomes the organization desires.

2. Human capital management

By applying cognitive capabilities to human resources, companies are able to transform their human capital management (HCM) processes.

One valuable use case improves the employee experience by leveraging intelligent natural language conversation services to integrate data in operational HR data systems, such as Workday, and in knowledge management systems that explain policies, procedures and guidelines. Benefits include improved employee engagement, higher productivity and lower costs.

Other potential HCM use cases that are ripe for AI and ML include recruiting, selection and on-boarding of candidates. AI and ML models are already being used to help analyze and match job descriptions and candidate resumes, and to recommend potential jobs for existing employees.

3. Process rethinking/reimagination

There are also multiple examples of leveraging AI and ML for rethinking and reimagining processes.

In one example, an insurance company reduced claim settlement time and costs by incorporating AI and ML to improve First Notice of Loss (FNOL) initiation and data collection, as well as claim classification/assignment processes.

Companies can improve the speed and accuracy of FNOL by enabling claimant self-service using natural language voice/chat to initiate a claim. They can integrate external data sources such as weather and employ a fraud scoring model to facilitate claim classification and corresponding assignment to the correct processing alternative. Processing alternatives can include:

Straight-through processing without human involvement
Sending appropriate claims to the legal department for potential early intervention to reduce lawsuits
Rejecting claims because the deductible is higher than the claim
Assigning claims to a human analyst/adjustor

Predictive modeling to triage injured worker claims has been developed and applied to help determine “return to work” outcomes, along with typical treatment plans for various types of injuries (NOT replacing doctors) and estimated treatment costs. This technique optimizes case manager resource assignment and helps injured workers get back to work, improving service quality and lowering costs.

Applying an ML model to extract information from unstructured documents and forms helps to provide insights from previously unanalyzed sources. This capability works by accelerating processing of information buried in those large unstructured documents and drives new insights, lowers costs and improves processing efficiency.

In a fourth example, Anteelo is automating the underwriting process for home inspection to lower costs and improve scalability and overall quality. This use case applies visual recognition to highlight details in home inspection photos and use those details to consult the underwriting guidelines and then augment the underwriting decision.

As you can see from these use cases, the ability to improve the customer experience and gain operational efficiencies through AI and ML are real and very achievable. What other use cases can you think of for your industry? I’d love to hear your thoughts!

AI Applications in Documents

Posted on 13/02/202015/06/2021 by Tushar

“We are drowning in information, but starved for knowledge”

This is a famous quote by John Naisbitt which shows the key difference between information and knowledge. Advancement in data engineering techniques and cloud computing have made it easy to generate data from multiple sources but making sense of this data and getting insights is still a huge challenge. The data volumes have now increased exponentially and along with the traditional structured data, data can now reside in different formats like unstructured social media text, log files, audio/video files, streaming sensor data etc.

Applying manual methods to process this diverse data is not only time consuming and expensive but is also prone to errors. Hence the need of the hour is to use Artificial Intelligence (AI) based automated solutions that can deliver reliable insights and also give a competitive advantage to customers. Here are few examples of how customers across industries can benefit from AI driven solutions.

Microsoft Azure based AI solution

In 2017, more than 34,000 documents related to John F Kennedy’s assassination were released. The data volume was huge, and data existed in different formats like reference documents, scanned PDF files, hand written notes and images. It would take researchers months to read through this information and hence manually reviewing this data was not the most optimal solution. Microsoft Azure team applied AI based Cognitive Search solution to extract data from these diverse sources and gained insights. Technical architecture for this use case was built using Azure Cognitive Services components like Computer Vision, Face Detection, OCR, Handwriting Recognition, Search and core Azure components like Blob Storage, Azure ML, Azure Functions and Cosmos Database. This solution also annotated text using custom CIA Cryptonyms.

Hospitals usually deal with a lot of patient data which could reside in electronic medical records (EMR), handwritten prescriptions, diagnostic reports and scanned images. AI based Azure Cognitive Search could be an ideal solution to efficiently manage patient’s medical records and create personalized treatment plan. Many downstream use cases like Digital Consultations, Virtual Nurses and Precision Medication can be built once the patient data is optimally stored.

Google Cloud Platform (GCP) based AI solution

GCP introduced Document Understanding AI (beta) in Cloud Next 19. This is a serverless platform that can automate document processing workflows by processing data stored in different formats and building relationships between them. This solution uses GCP’s vision API, AutoML, machine learning based classification, OCR to process image data and custom knowledge graph to store and visualize the results. Customers can easily integrate this solution with downstream applications like chatbot, voice assistants and traditional BI to better understand their data.

Customers who deal with Contract Management data like Mortgages are usually faced with a lot of manual tasks to ensure that the contracts are complete and accurate. This could mean processing contracts in different formats/languages, reviewing the supporting documents, ensuring that the details are accurate and complies with regulatory standards across documents. By using Document Understanding AI and integrating it with a well-designed RPA framework, customers will be able to efficiently process Mortgage applications, Contracts, Invoices/Receipts, Claims, Underwriting and Credit Reports.

Use cases from other industries

Document Management AI solution can also be applied to diverse use cases from other industries like processing claims related to damages to shipped products by e-commerce companies, handling know your customer (KYC) process in the banking industry, invoice data processing by Finance teams, fraud detection during document processing etc.

As more and more companies embrace the digitization wave, they will be faced with different variations of data/document management challenges. Based on the current trend, number of use cases are only going to increase and an AI driven solution is probably the most efficient way to solve this problem as it can reduce manual work, save cost and deliver reliable insights. This will ensure that companies can spend more time on building their business and less time on manually processing documents and data preparation.

What Does Product Management Look Like in Data Science?

Posted on 11/02/202015/06/2021 by Tushar

The topic related to ‘Product Management’ has received several laurels in recent years. Several rounds of discussions have happened to create an analogy out of client’s stand point. As I heard more of these conversations, there was an uncomfortable ambiguity stemming from disbelief – is this another fad or is there meaning to it? Well, the initial rumblings were from the cool kids in the bay. But, why did grounded Midwest and shoot-from-the-hip south latch on? Must be something deeper, right?!

Product management has been around forever in the software, e-commerce world. But, today, mainstream IT and AI teams in fortune 500 companies are thinking of a product paradigm shift. Leading consulting firms are also developing products or beefing up their technology as an eventuality.

But, the question that begs attention here is – why products? What happened to software as a service, platform as a service, ML as a service? Do we need another paradigm shift? Or as the saying goes – Old wine in a new bottle?

IT teams are today being led by progressive Chief Digital Officers, Chief Data officers. Conventionally, CIOs have been leveraging their value by app dev teams, BI teams, infrastructure teams et al. While this may have become a table stake, it has been around for a while already. The question is – ‘How to deliver incremental value to business?’

So, what has changed?

Demand:

IT is today called upon to be a true business partner. And, given the rate at which business is facing change, the time to deliver value is compressed.

Glocal innovation:

For a fortune 500 firm operating globally, innovation is striking at its core from multiple directions. While the USA is still the biggest revenue (EBITDA generation engine), problem and solution innovation is happening in other markets faster than the USA. For starters, they have less legacy to deal with. The local markets are facing competition from nimbler players. VC money is flowing into firms in China, Israel, Korea, India which are encountering newer problems in e-commerce, voice commerce sectors. Other traditional revenue generating markets, individually facing slower growth, find it difficult to make a business case to invest in solutions led by such innovations.

Problem repeatability:

This is going to sound rhetorical. But, I must state it because it is relevant. Business problems in today’s enterprise are constantly changing. Few of them get recreated, and hence are not available in large volumes. Few others are becoming common across markets and thus moving into a constant state of being a tightly defined problem that can be applied globally. Repeatable.

A good indicator to this is AWS recent product launches – out of the box image, text, voice, reinforcement learning, forecasting. Common problems which are finding repeatable solutions.

The AI candy shop:

Today, nobody wants to use process automation tools that are not embedded in intelligence. Passé, inefficient. Wallstreet, investors and boards are lapping up the buzzwords – cognitive, AI, embedded ML.

Cloud enabling global scalability:

Cloud platforms such as Azure, AWS have ensured that once you have these AI capabilities developed, they can be deployed globally. The global-local adaptation is a key design criterion in this context.

Glocal solution adaptation…er,… maybe Glocal problem adaptation:

Each market has its secret sauce in terms of the market structure, drivers and customer nuances. Thus, before adapting a solution from one market to the other, it is essential to adapt the problem as well. For example, it is an interesting pursuit to adapt the problem structure from the modern trade Australia market to half way across the world in Brazil.

And, then adapt the solution.

So, who’s game is it anyway?

Given the above guard rails, it is quite evident that the business case should be developed by a country specific P&L or ROI measure. It must be a global mandate. IT is one of the few functions which is ideally poised to ride this wave. That, they own the data systems is coincidental. Or, well.. was that the whole plan! Go, Trojan..

Finally, after rambling about half the things in the world – we come to the initial topic of this article. Products. Why?

A product has a life – it evolves constantly. The focus is on continually making the best product for its end user, ever possible. It has a roadmap. In a world of multiple users, it needs a strong owner who plans and decides well. It has a clear value proposition in each update/release. It can be developed in a sprint like manner. It can be defined with a bounded scope and sold internally in enterprises, with greater ease. And, be defined, abstracted, customized for a global roll out.

Looks like a duck, walks like a duck, sounds like a… must be a duck. Yes, I guess it does look like a product.

But, how do we help organize people and teams to get the products rolled out?

While the below roles are common to a product-oriented firm, the thought process is different from conventional IT projects. Sharing of resources across projects being the biggest drawback. The smartest of each of the below folks will perhaps still fail, without an organizing framework. The roles to work in a closely integrated manner, dedicated to making a single product management successful.

Product Designer:

The role of a product designer is someone who can completely immerse himself in the shoes of the end user, without worrying about the AI or Tech related issues that may occur sometimes. Just solve the end user’s real problem and keep tracking the end user’s behaviour as the product usage evolves. In product management, there is a contradictory school of thought which mandates that the designer must appreciate “how” a product works. This, however, might dilute the designer’s objective of empathizing with the end user.

Product owner:

A functional expert of impact analysis who can connect the dots and identify the nuances of each problem. A great problem solver, with functional expertise, has the knack to see through the commonalities, and the uncommon aspects too. Prioritization between the must-haves, nice-to-haves and must-not-haves is a key skill required in the role.

Product BAs

Products are quite massive in terms of their scope today. Primarily, each product usually is broken down into sub products which are owned by individual product Bas.

The AI solution developer(s)

Usually, it is very difficult to get a product owner who really gets AI solution development. By and large, individual intelligence is anyways overrated. It is important to have a dedicated AI solutioning team which can translate the problem into a modular AI solution.

The AI deployment team

It is not enough to develop a modular AI solution. To be able to deploy it in globally scalable platforms requires seasoned IT big data engineering & testing capabilities. The plumbing and wiring required to take the AI concept to enterprise last mile reality is no mean task. It is a specialized function. Truly speaking, they give the product its real-life form.

Scrum & Program Managers

Last but not the least, you need the scrum team and program managers. Everyone benefits from their discipline and order amidst the chaos.

Things to consider while defining Machine Learning and Artificial Intelligence use case

Posted on 10/02/202014/06/2021 by Tushar

While many companies are interested in applying artificial intelligence (AI) and machine learning (ML) to help their businesses transform and innovate, few have an appetite for a lengthy project or a large initial investment. But when companies can see meaningful value in six to eight weeks, they are more likely to expand their usage of Artificial Intelligence(AI) and machine learning (ML) into new initiatives.

To demonstrate the value proposition for Artificial Intelligence(AI) and Machine Learning (ML), we recommend concentrating on unlocking insights from vast amounts of unstructured data to help augment human intelligence, create process efficiencies and lower operational costs. We have also found that the best results tend to come from a business-led/IT supported model.

Four key questions are central to scoping out an initial minimal viable product (MVP) for a valid use case.

Which persona are you trying to assist – external or internal? The answer is not a value judgement, but rather a focus that impacts some of the design considerations and decisions. As an example, in a contact center, the focus for the customer service rep is productivity – quickly giving the rep as much information as possible. This usually means fewer prompts, fewer stops, one- or two-word questions, and fewer and more dense answers. Contrast that with the external customer, where we tend to focus on a warm and engaging experience that incrementally guides the customer to the answers they need. This typically means more prompts, more questions, a wider variety of natural language to ask the questions, and more and shorter (more digestible) answers.
How are you are trying to help that persona? This aspect typically helps us hone in on part of an initial business process to understand how we are going transform — whether by adding more data for consideration, augmenting analysis, and/or automating the execution of steps.
Where does the data come from to support this initiative? This is often the most challenging aspect for several reasons: There may be more questions than current answers, the current answers may be inconsistent or conflicting, or the current answers exist, but may not easily understood or comprehensible. All of these situations tend to require focus on curating, modifying, and/or creating content in order to get the right answers.
What is the value? Value can be determined most successfully when we can be as specific as possible. Very often, benefits can be described in terms of metrics. From a best practice perspective, having specific metrics-based objectives makes it clearer for business and IT to determine the value. Examples include: a) higher customer satisfaction, measured by Net Promotor Scores or revenue increases, b) lower costs, shown by reduced handling times or increased processing volumes with the same staff and c) better employee engagement, indicated by reduced attrition and training time.

To further the dialogue as you consider progressing towards transforming your business by leveraging artificial intelligence, here are some additional questions to guide your thinking:

Are you focused on transformation and innovation?
For what initiatives are you considering AI/ML support?
What business outcomes are you driving?
What is your current level of experience with AI and ML?
Have you identified any use cases or do you need some assistance in defining them?

The key to evolving your approach from MVPs to multiple initiatives developed and deployed in production at scale, is to effectively balance faster time-to-value and major transformation outcomes. This balance demonstrates business results in smaller time increments and sets the foundation for flexible and continuous transformation and innovation.

1. Improving personalized shopping experiences

2. Automating customer interactions

3. Real-time Assistance

4. Data mining

5.Operational automation

6.Predicting outcomes

7.Improve the recruitment process

Conclusion

Uses of Machine Learning and AI in Fintech for Millennials

1. Algorithmic trading

2. Better Targeting

3. Better Customer Support

Chatbots

Personalized experience

4. Help with underwriting services

5. Stock Market Changes Prediction

<img decoding="async" class="aligncenter" src="http://www.smartmicros.com/wp-content/uploads/2019/09/artificial-intelligence-ai-is-rapidly-becoming-a-common-part-of-how-modern-business-operates.jpg" alt="Artificial Intelligence (AI) in business " width="742" height="499" />

1. Define an integration strategy for embedding AI and analytic insights into business operations

2. Establish a holistic data and analytics platform

3. Know your data

4. Control and govern your data

5. Simplify access to your data

Reacting to change

Taking advantage of AI

Cyber defence

Data storage

Conclusion

Question 1: “How do AI and Machine Learning differ from traditional programmed systems?”

Question 2: “Where is the value from Artificial Intelligence/Machine Learning?”

1. Customer care

2. Human capital management

3. Process rethinking/reimagination

Microsoft Azure based AI solution

Google Cloud Platform (GCP) based AI solution

Use cases from other industries

Demand:

Glocal innovation:

Problem repeatability:

Cloud enabling global scalability:

Product Designer:

Product owner:

Product BAs

The AI solution developer(s)

The AI deployment team

Scrum & Program Managers

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