Banking Article, Banking Finance 2021, Banking Finance September 2021

ARTIFICIAL INTELLIGENCE AND INTELLIGENT BANKING

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. It was in the mid-1950s that John McCarthy, widely recognized as the father of Artificial Intelligence, coined the term “Artificial Intelligence” which he would define as “the science and engineering of making intelligent machines”.

Researchers, scientists, engineers, linguists, domain experts are working continuously and passionately to evolve the A.I. As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optical character recognition are no longer considered to embody artificial intelligence. AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, psychology, health care, sales retails, operations, ecommerce and more. Coming to financial industry, where it is used for fraud detection, trade and sales forecasting ease of operation, voice assisted banking.

Artificial Intelligence (AI) Overview

A thorough and hype-free review of AI in business was published recently by Deloitte, Demystifying Artificial Intelligence, suggesting the term “cognitive technologies” to encourage focus on the specific, useful technologies that emerge from the broad field of AI.

However labelled, the field has many branches, with many significant connections and commonalities among them. The most active today are shown here:

Analytics is subset of A.I. which falls under supervised learning in machine learning segment. Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statisticscomputer programming and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analyticsprescriptive analyticsenterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modellingweb analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modelling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Difference between Analytics, AI, NLP, ML, NN and DL

  • AI or Artificial Intelligence:

          Building systems that can do intelligent things.

  • NLP or Natural Language Processing:

          Building systems that can understand language. It is a subset of Artificial Intelligence.

  • ML or Machine Learning:

          Building systems that can learn from experience. It is also a subset of Artificial Intelligence.

  • NN or Neural Network:

          Biologically inspired network of Artificial Neurons

  • DL or Deep Learning:

          Building Systems that use Deep Neural Network on a large set of data

          It is a subset of Machine Learning.

  • Analytics or Data science:

          The systematic computational analysis of data or statistics

Types of Artificial Intelligence (AI)

There are 3 types of artificial intelligence (AI):

  • Narrow or weak AI,
  • General or strong AI
  • Artificial superintelligence

  • ANI – Artificial Narrow Intelligence: It has a narrow range of abilities.
    It comprises of basic/role tasks such as those performed by chatbots, personal assistants like SIRI by Apple, Cortana by Microsoft, IBM’s Watson, Image / facial recognition software, Disease mapping and prediction tools, Manufacturing and drone robots, Email spam filters / social media monitoring tools for dangerous content, Entertainment or marketing content recommendations based on watch/listen/purchase behaviour.
  • AGI – Artificial General Intelligence: It is on par with human capabilities. Artificial General Intelligence comprises of human-level tasks such as performed by self-driving cars by Uber, Autopilot by Tesla.  Fujitsu-built K, one of the fastest supercomputers, is one of the most notable attempts at achieving strong AI, but considering it took 40 minutes to simulate a single second of neural activity, it involves continual learning by the machines.
  • ASI – Artificial Super Intelligence: It is more capable than a human.
    Artificial Super Intelligence refers to intelligence way smarter than humans.ASI would have a greater memory and a faster ability to process and analyse data and stimuli. Consequently, the decision-making and problem solving capabilities of super intelligent beings would be far superior to those of human beings.

Stages of Artificial Intelligence (AI)

  • Stage 1 – Machine Learning

          It is a set of algorithms used by intelligent systems to learn from experience.

·         Stage 2 – Machine Intelligence

        These are the advanced set of algorithms used by machines to learn from experience. eg – Deep Neural                            Networks. Artificial Intelligence technology is currently at this stage

·         Stage 3 – Machine Consciousness

          It is self-learning from experience without the need of external data.

Intelligence (AI) industry in India – The current status

According to a source, around 500 start-ups and businesses are using AI domains. Most of the growth in AI in India can be seen in the private sector. The government sector in the NITI Aayog plan developed a National level Strategy for bringing Artificial Intelligence in India. Even though the private sector has the major share of AI services in the industry but the government sector is still the largest customer for data science in terms of the Indian economy.

There are several start-ups that are based in cities such as Bengaluru, New Delhi, Mumbai and Hyderabad which work on artificial intelligence principles to serve consumers better. Their product range varies from multi-lingual Chatbots to online shopping assistance and automated consumer data analysis. The companies have been working in areas such as e-commerce, healthcare, edtech, fintech etc. Though in their nascent stage, the performance of these companies have been promising.

India is third in terms of investment, just lags behind U.S.A. and China. With a copious pool of STEM talent and with growing population of youngsters, India will be banking on AI for its economic growth and improvement in quality of life of its citizens.

The challenges Facing India’s Artificial Intelligence (AI) Development

  1. AI-based applications to date have been driven largely by the private sector and have been focused primarily in consumer goods. The emergent scale and implications of the technology make it imperative for policymakers in government to take notice.
  2. Early lessons of AI success in the United States, China, South Korea, and elsewhere offer public and private funding models for AI research that India should consider.
  3. The sequential system of education and work is outdated in today‘s economic environment as the nature of jobs shifts rapidly and skills become valuable and obsolete in a matter of years.

Indian Banks and the Technology

The balanced approach followed by Indian central bank, Reserve Bank of India, is another major factor in any new technology adoption in Indian banking sector. In the last few years—RBI has taken a cautious but pragmatic view of embracing new technologies, often forcing technology adoption on banks through regulation, wherever it has seen scope to enhance customer experience and efficiency using a particular technology. RBI‘s proactive push of new technology adoption has not just been restricted to creating policy frameworks. It has also used a mix of regulatory frame work, various initiatives and even worked with the industry to make things easier and effective.

The creation of National Payment Corporation of India (NPCI) which has significantly brought down the cost of electronic transactions is a paradigm shift in Techno Ambience. The regulator also has an academic/research unit, Institute of Development and Research in Banking Technology (IDRBT) which keeps studying the opportunities and challenges in new technology areas. It is not a coincidence that both these units have been actively involved in testing out blockchain as a proof of concept.

India‘s position is quite unique here. It is a fact that India is a tech-hub. Apart from being a large technology outsourcing destination, India is also the home to vendors with a large core banking market share globally. Two of the top three core banking solution vendors—Infosys and TCS—are headquartered in India. Of late, India has also seen a lot of activity in the Fintech arena. The country has become one of the global fintech hubs. While in many developed markets, Fintechs and banks have enjoyed an uneasy relationship, in India, private banks like ICICI Bank, Axis Bank and HDFC Bank have proactively gone to Fintechs, creating contests and hackathons to get the best of innovations, sometimes even sharing their APIs with these Fintechs.

BankChain was announced on 8 February 2017 by SBI, India’s largest bank. It‘s a 30+ member consortium led by SBI, the country‘s largest lender, and includes banks, NBFCs and the National Payments Corporation of India (NPCI), an organization set up by Indian banks to support retail payments. Simply put, BankChain is a community of banks for exploring, building and implementing Block Chain solutions. Bank Chain is supported by Pune-based Startup Prime Chain Technologies to create these solutions. Currently, it has 37 members and 24 live projects.

Artificial Intelligence (AI) Technology in Banking and Finance

About 32% of financial service providers are already using AI technologies like predictive analytics, voice recognition among others, according to joint research conducted by the National Business Research Institute and Narrative Science. Artificial intelligence in banking is used to establish more meaningful conversations with customers by solving real problems and managing finances. According to a report 2018 published by the World Economic Forum, in collaboration with Deloitte, 76 per cent of decision making authorities in the banking industry agree that AI is a top priority because it is critical for differentiation.

Even older consumers, who may not be as tech-savvy, will be able to process their banking transactions quickly and easily via a smooth online experience. AI can be used to create a smarter, personalised user experience.For instance, it can be used to track data such as a customer’s spending and purchase history over a period of time to help the bank send relevant information regarding budgeting and saving. By offering consumers an individualised service, the bank is able to increase customer satisfaction and retention, creating mutual value for the customer and the bank.Successful AI applications in banking means putting to good use the massive amounts of data collected, no matter which channel it comes through, even if it’s via ATMs, web channels, digital wallets, point of sale activity or mobile devices.It allows for personalisation; digitally transforming a mass service into an individualised and customised one, based on a customer’s unique behaviour, preferences, and requirements. This is what also gives banks a competitive differentiation – improving compliance, increasing customer engagement and optimising the overall operational efficiency including:

Personalized Financial Services

Personalized connect will reach new heights as automated financial advisors and planners provide expertise in making financial decisions. They analyze market temperament against the user‘s financial goals and personal portfolio, and offer recommendation regarding stocks and bonds.

Smart Wallets

Digital wallets are touted as the future of real-world payment technologies, with major players like Google, Apple, Paypal and others, jumping on the bandwagon and developing their own payment gateways. This decreases the dependence on physical cash, thereby expanding the reach of money to greater levels.

 

Underwriting

The insurance sector is also coming up with a storm as they are moving towards congruent automation. By utilizing AI systems that automate the underwriting process, the organizations come armed with more granular information to empower their decisions.

Voice Assisted Banking

Physical presence is slowly fading away as technology empowers customers to use banking services with voice commands and touch screens. The natural language technology can process queries to answer questions, find information, and connect users with various banking services. This reduces human error, systemizing the efficiency.

Data-driven AI applications for lending decisions

Applications embedded in end-user devices, personal robots, and financial institution servers are capable of analyzing a huge volume of data, providing customized financial advice, calculations and forecasts. These applications can also develop financial plans and strategies through research, regarding various customized investment opportunities, loans, rates, fees, etc and track the progress.

Digitalization instead of branch lines

Banking is a lengthy process, with past records of long queues and sluggish response marring the productivity. Even opening a bank account was viewed in negative terms as harried consumers would run pillar to post, while getting the necessary documentation complete. Digitization of documentation eases that pain and creates a comprehensive platform, where the consumers and providers connect.

Block Chain hastening payments

The customer base that banks serve is going through a major shift in terms of buying behaviours and preferences, driven by the digital revolution, particularly social media and mobile. An increased demand for more choice and control in how they interact with a bank is on a rise. Sluggish payment processes will be a thing of the past as Blockchain is set to inculcate the advantage of real-time payment process, hastening up the procedure of payment, thereby increasing support and satisfaction.

Sales and Trade Forecasting

Through history of data, by using the predictive analytics we can forecast the sales and future stock prices related to funds, banks and equities. Then deduce a decision for investment and other financial activities.

Customer support

As speech processing and natural language processing technologies mature, we are drawing closer to the day, when computers (Chatbots) could handle most customer service queries. This would mark an end to waiting in line and hence result in happier customers.

Artificial Intelligence (AI) in Indian Banks

Consumers are not realising their full saving potential because banks are generally still learning to understand their customers’ needs. With most banks still running on legacy systems, it can prove a struggle to complete complex transactions beyond money transfers and deposits.AI, however, will allow banks to focus on their customers by leveraging the data that they own to gain essential insights. This will in turn allow banks to personalise and enhance the customer journey, making it as frictionless as possible, by manipulating the data to offer real-time recommendations.

Amongst many Indian banks, there are 12 banks which have gained continuous media attention for their AI initiatives over the last few years. The list includes:

  1. SBI
  2. Bank of Baroda (BoB)
  3. Allahabad Bank
  4. Andhra Bank
  5. YES Bank
  6. HDFC
  7. ICICI
  8. Axis
  9. Canara Bank
  10. City Union Bank
  11. Punjab National Bank
  12. IndusInd Bank

Smart use of AI means viewing banking operations through both an automation and augmentation perspective. Banks can use the insights gained from the deployment of their Chatbots to improve bankers’ productivity and their interactions with customers. Banks using AI can achieve a more informed banking environment that not only provides customers with assistance and insights, which in turn gives them greater control over their personal finances, but also an added sense of financial security. Chatbots have evolved to a point where they have developed a level of human intelligence. As a result, they can offer an emotionally connected experience for customers who are in the process of making important life decisions.

With increasing consumer expectation, the use of artificial intelligence, machine learning and Chatbots in banking is also of great utility. Banks & Credit Unions worldwide are testing new application and deploying new solutions to improve overall digital experience of customers. Chatbots have already begun to make their mark in Indian banking industry. Chatbots nowadays combines benefits of virtual and human assistance and provide a differentiated customer experience.

Chatbots have begun to mark their presence in Indian banking sector.Kotak Mahindra bank is first to launch voice Chatbot “Keya”. HDFC Bank EVA’s (Electronic virtual assistance) is largest AI powered Chatbot which has already answered 5 million queries with 85% accuracy.Chatbots can analyse and understand not only the content but also the context of customer’s question. The history of Chatbot started with “Elisa”, first ever Chatbot started in 1966. It was a very basic bot providing some basic information related to entertainment, sports and price regulation of stock market as well.With the development of smart phones and the applications these Chatbots have turned all the more smarter .With AI progression’s and voice recognition the application of Chatbot is increasing day by day.Union virtual assistance (Chatbot) being able to provide customer delight and study of pattern of Chatbot usage in union bank is also encouraging.

The Techno savvy customers prefer self-service and they want quick and easy ways to get their queries responded. Chatbot conduct conversation to respond queries and deliver meaningful offer and make everyday banking easier, faster and convenient. Some of the salient features of Chatbot are that they can handle omni-platform concurrent queries, humanistic approach, predictive in nature, they conduct short and simple interaction with easy text driven conversation and also having simple user interface. They are at customer service 24*7*365 i.e. all the time.

Progress made by the Banks in this regard:

State Bank of India (SBI): SBI is currently using an AI-based solution that captures the facial expressions of the customers and helps them in understanding the behaviour of its customers developed by Chapdex. On the front desk, it uses SIA Chatbot, an AI-powered chat assistant developed by Payjo, a Startup based in Silicon Valley and Bengaluru. It addresses customer enquiries instantly and helps them with everyday banking tasks just like a bank representative.

Chatbot of SBI i.e. “SIA” (Sales intelligence Assistant), facilitates customer with numerous consumer banking actions. It is setup to handle nearly 10000 enquiries per second which according to Payjo is 25% of queries processed by google every day. SBI claims SIA continuously learns with each interaction and get better over time. It is a machine learning based product. Currently SIA can address enquiries on banking products and services .It is trained with set of past consumer questions and is said to handle frequently asked questions aptly. Payjo developed SIA after studying how other banks were changing their customer service business models and basing their ideas on what they thought would succeed the most in the future.

Bank of Baroda: BoB has set up of hi-tech digital branch equipped with advanced gadgets like artificial intelligence robot named Baroda Brainy and Digital Lab with free Wi-Fi services.

Allahabad Bank: In a media statement earlier, the Allahabad bank said that its app ‘emPower’ is scheduled to get major enhancements like Chatbot and artificial intelligence-based ecommerce payments.

YES Bank: It has partnered with Gupshup, a bot platform, to launch ‘YES mPower’ – a banking Chatbot for its loan product. Another AI product YES ROBOT is equipped to answer consumer’s banking related queries anytime, anywhere, without the hassle of waiting for on-call or searching online. Also, YES BANK was the 1st Bank in India to introduce Chatbot based banking with the launch of YES TAG in April 2016 which allows customers to perform banking transactions on various social messengers and enables transactions through 5 messaging apps. Customers can carry out a wide range of activities, such as check balance, FD details, status of cheque, transfer money, etc.

HDFC Bank: It has developed an AI-based chatbot, “Eva”, built by Bengaluru-based Senseforth AI Research. Eva can assimilate knowledge from thousands of sources and provide simple answers in less than 0.4 seconds and has in the first few days of its launch answered more than 100,000 queries from thousands of customers from 17 countries. It has addressed over 2.7 million customer queries, interacted with over 530,000 unique users, and held 1.2 million conversations. Going forward, Eva would be able to handle real banking transactions as well. HDFC is also experimenting with in-store robotic applications and launched a prototype robot IRA (“Intelligent Robotic Assistant”).

“EVA” (Electronic virtual assistant) the AI based Chatbotis aimed to serve customer better and faster, by leveraging technology.EVA uses latest AI and natural language processing to understand user queries and fetch relevant information from thousands of possible sources .By conversing with EVA customer can get various information. EVA has already answered more than 5 million queries from around a million customers with 85% accuracy. EVA hold more than 20000 conversation per day.EVA can be connected on all digital channel of HDFC bank i.e. website,mobile site and dedicated portals of bank customers.She is also available on voice via Google assistant .If you just say “ok Google, talk to HDFC Bank” into your Google assistant you can talk to EVA. EVA can also be connected through amazon echo devices, just say “Alexa, open HDFC bank to connect with EVA. Also many questions can be asked such as

1)“Tell me IFSC code of —— branch”,

2) “Current FD rates”

3) “Give me the address of —- branch”

4) “Rates and charges of ———-“

5) “Documents required for ———– loan”

6) “How can I get ——– loan”

ICICI Bank: It has deployed software robotics in 200+ business processes across various functions of the company, created mostly in-house using AI features such as facial and voice recognition, natural language processing, machine learning and bots among others. The software robots at ICICI Bank are configured to capture and interpret information from systems, recognize patterns and run business processes across multiple applications to execute activities. One such product is its AI-based Chatbot, named iPal, which helps in answering queries, helping in financial transactions and discovering new features.The bank claimed to be 1st among country and among a few globally to deploy this technology that emulates human actions to automate and perform repetitive and high volume and time consuming business task. iPAL Chatbot of ICICI, since its inception till February 2020 has attended 3.1 million customers and their 6 million queries along with 90 percent accuracy rate. iPAL is divided into three categories-

  • First category involves FAQ, which are simple questions which you may want to ask your bank executives, for which there are simple structured answers.
  • Second category,It involve financial transactions ,where in you can make fund transfer from person to person ,pay your bill or recharge your bills using queries.
  • In third category it involves helping people discover new features.These answers simple how to tasks such as how to reset ATM Pin,which is a bit more evolved and is like interacting with your bank executive.

The bank is currently in process of integrating iPAL with existing voice assistant such as Cortana, Siri and assistant.

Axis Bank: It launched an AI-enabled app that uses natural language processing to enable conversational banking that helps consumers with financial and non-financial transactions, queries and product information.Innovation lab launched by Axis Bank called “Thought Factory” last year to accelerate the development of innovative AI technology solutions for the banking

Canara Bank: It launched Mitra, a humanoid robot developed by Bengaluru-based Invento Robotics which helps customers navigate the bank. Another one Candi, which is slightly smaller than Mitra is supplementing the human resource.

Punjab National Bank: In 2018, the bank announced its plan to implement AI in account reconciliation as well as using analytics to improve its audit systems. The move came in after the infamous debilitating fraud of approximately INR 20K Cr, took place in February 2018, which almost paralysed the bank’s operation for a short time.

IndusInd Bank: It has launched Alexa Skill, ‘IndusAssist’, using which bank account holders can conduct financial and non-financial banking transactions with Alexa, Amazon’s virtual assistant.

City Union Bank: It launched the banking robot, ‘Lakshmi’. The robot can interact with customers on more than 125 subjects. Apart from answering generic questions, the robot is also programmed to connect with the core banking solution including balance, interest rates and transactional history.

Andhra Bank (Now amalgamated entity of Union Bank of India): Bengaluru-based AI Startup, Floatbot has launched AI Chatbot integrated with Core Banking Servers of Andhra Bank, to digitally engage and automate customer support for its 5 Cr customers. Floatbot will also develop a Chatbot for 20K+ internal employees of Andhra Bank to automate on boarding and training.

Union Bank of India: It launched the banking robot, ‘UVA’. The robot can interact with customers on multiple subjects and product information. Chabot by Union Bank of India (Union Virtual Assistant) is an artificial intelligence (AI) software that can simulate a conversation or chat with a human user in natural language through messaging application (text chats) or voice commands or may be both, which may be through Website, Social media platform or mobile apps.UVA (Union bank of India’s Virtual assistant) help in redefining customer experience by (ASK UVA).The features offered are-

  • EMI CALCULATOR
  • CALL CENTRE EXECUTIVE
  • DEPOSITS
  • LOANS
  • GOVERNMENT SCHEMES
  • INTEREST RATES
  • OTHERS

As Union Bank already effected the amalgamation, it plans to upgrade the analytics centre of excellence and launch many digital banking products.

Both challenger and traditional banks are growing their focus on helping consumers save money. As customers increasingly look to save money for a rainy day and become more fiscally responsible, many banks have reacted by providing a variety of services. Traditionally, these services included basic budgeting apps or digital tools, but AI is now being deployed to help segment different payments, provide suggestions to customers based on payment history, offer a source of advice, and a resource for answering common customer queries via Chatbot.

When a human point of contact isn’t always available, AI-driven virtual assistants or Chatbots are able to respond to customers’ simple banking needs. From identifying funds in a customer’s cash flow that can be automatically moved to a savings account, and alerting customers to any unusual activity in their accounts, to providing personalised financial management insights and advice.
AI-based decision making can ultimately help banks expedite workflow, reduce the volume of customer calls coming into the call centre, and improve customer service.

Benefits of AI for banking sector

Fraud detection: Anomaly detection can be used to increase the accuracy of credit card fraud detection and anti-money laundering.

Customer Support and Helpdesk: Humanoid Chatbot interfaces can be used to increase efficiency and reduce cost for customer interactions.

Risk Management: Tailored products can be offered to clients by looking at historical data, doing risk analysis, and eliminating human errors from hand-crafted models.

Security: Suspicious behaviour, logs analysis, and spurious emails can be tracked down to prevent and possibly predict security breaches.

Digitization and automation in back-office processing: Capturing documents data using OCR and then using machine learning/AI to generate insights from the text data can greatly cut down back-office processing times.

Wealth management for masses: Personalized portfolios can be managed by Bot Advisors for clients by taking into account lifestyle, appetite for risk, expected returns on investment etc.

ATMs: Image/face recognition using real-time camera images and advanced AI techniques such as deep learning can be used at ATMs to detect and prevent frauds/crimes.

In order to provide a sustainable high-level of customer engagement, banks need to gain full visibility of a customer’s history to understand their personal banking habits and needs. Banks therefore require an integrated enterprise system that consolidates customer data from all sources, from apps and APIs to third parties, which can then use AI to provide real-time recommendations to increase loyalty, retention, and value. This combination of AI and omni channel decision making can add value to the overall customer experience.Real-time transaction analysis is crucial, it enables banks to collate data and track transactions at low latency. This not only gives banks a better view of their customers, it would also give them the dataset required to apply AI and deep learning to provide personalised, value-added products to customers as it learns about spending habits over time.

With all this information, banks can now deliver organized financial services and advice better than ever before with the help of AI-based decision making. By tapping into customer profiles and preferences, banks can package products and services together based on personalised needs.Banks can now develop more products affiliated with greater customer loyalty and lifetime value. Whereas, for consumers, they can benefit from the convenience of working with a trusted organisation that understands their personal requirements. As the adoption of AI-based decision making tools grow, relationship managers will be able to more accurately and consistently assist a customer with the best products and services for managing personal finances.Relationship managers will also be able to analyse a customer’s banking experience on existing channels. Doing so will allow banks to determine how effectively their current processes operate, whether there are any impediments for instance. They will then be able to model and implement process optimisation across their entire physical, web, digital, and mobile channels to serve customers more effectively, and provide an enhanced
customer experience.

Starting from computerization, the Indian Banking System has transformed radically and witnessed a gigantic leap from Paper Banking to Palm Banking. With the introduction of advance technology like AI in banking arena, there is no iota of doubt that the same can revolutionize the way of Banking in the days to come. However, looking to the manifestations of bottlenecks at different juncture, this may take significant amount of time to reap the benefits of AI in banking and brick-and-mortar banking may still continue for some more years.

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