The Future of Artificial Intelligence in Banks

To a 3 year baby makes her mother available her favorite rhyme by typing the rhyming words in the Youtube. The little one discovers that she can watch the rhyme of her choice by merely speaking over the Google voice Assistant. Now, she does not require her mother for this anyway…..! 

Artificial intelligence has been impacting our lives faster than we can imagine. We are familiar with the Virtual Assistant (chatbots) from SIRI in i-phone to DISHA in IRCTC’s ticket booking application to CORTANA of Microsoft Windows to SIA in SBI’s Banking application YONO to the Netflix recommendations on smart TV and so on. The revolution brought by Artificial intelligence has been the biggest in some time.

Artificial Intelligence (AI) is existent in almost all the dimensions of life wherever humans are dependent on machines and technology and that is why Banking is also not immune to this. Why AI has captured attention of Business leaders and strategists is its emerging possibilities and applicability in various fields as a potential tool for harnessing better results and ease of doing things at lower cost.

What is Artificial Intelligence (AI)?

The concept of AI is based on the idea of building machines capable of thinking, acting, and learning like humans. It is the ability of a machine or a computer program to think and learn.

As per Wikipedia, The term “artificial intelligence” dates back to 1956 and belongs to a Stanford researcher John McCarthy, who coined the term and defined the key mission of AI as a sub-field of computer science.

Research in AI has focused on five key components of intelligence. While some of these elements as mentioned below may seem self-evident as a single item, they must work in conjunction with each other to qualify as Artificial Intelligence.

  • Reasoning: Reasoning implies the computer representation of logic systems and has a wide field of applications that includes business rule processing, problem solving, predictive analytics, robotics, natural language process, along with other applications.
  • Learning (Machine Learning / Robots): Machine learning establishes that a computer program can learn, adapt and react to new data without human interference. Its broader method called Deep Learning is designed to recognize patterns in digital representations of sounds, images, and other data.
  • Perception: Perception is the process of acquiring, interpreting, selecting and organizing sensory information which boils down to voice or speech recognition which is the ability of a machine or program to receive and interpret dictation, or to understand and carry out spoken commands.
  • Problem Solving: Problem solving encompasses a number of techniques known as algorithms, root cause analysis, etc. A variety of problem solving is addressed in AI, including planning a series of movements that enable a robot to carry out a given task.

5. Linguistic Intelligence (or Language Understanding): The ability of a machine or program to receive and interpret dictation, or to understand and carry out written and spoken commands. Language understanding is devoted to developing algorithms and software for intelligently processing language data.

Why AI Useful for banking?

“Wouldn’t it be wonderful if somebody we got to the point where there were robots everywhere; they were running farms, they were running Apple, they were running Berkshire Hathway and all you had to do was one person?”                                     – Waren Buffet, Chairman and CEO of Berkshire Hathaway

Cost Savings: AI systems are able to perform more complex automation. Robotic Process Automation (RPA) is one of the key drivers of automation in financial institutions which is evolving into cognitive process automation providing 50-70% cost savings. JPMorgan Chase introduced a new technology called COiN that reviews about 12,000 documents (which, without

automation, would require more than 360,000 hours of work) in just seconds. As per media reports Analysts estimate that AI will save the banking industry more than $1 trillion by 2030.

Operational Efficiency & Reduced Downtime: AI offers an improved workflow and services delivery model by increasing output and accuracy, reducing errors and cycle, and decreasing the need for ongoing training. Unlike humans, robots can work 24 hours a day, seven days a week. Typically, one robot can do the work of multiple Full-Time Employees.

Advanced Analytics: Advanced analytics is an essential element in achieving regulatory compliance, cost effective growth and optimized operations. The analytics can help manage credit risk by predicting potential slow pay and potential bad debts, while providing management with insight into possible industry economic trends and consideration for possible changes to policy.

Enhanced Performance and Quality:

AI optimizes capabilities that grow organizational capacity. A human is likely to make errors, even when carrying out somewhat redundant work. Robots are trustworthy, consistent and tireless. They can perform the same task the same way every time without error or fraudulence. Some companies have shifted a portion of their Full-Time Employees to focus on other important tasks that were previously not receiving the level of attention needed. According to a US-based IT solution provider Bizof the most important reason why AI is gaining popularity is to increase workforce productivity.

These are a few but important applications of AI in banking, and there are many ways AI is being explored in the industry.

Fintech- A manifestation of AI

One of the most discussed manifestation of AI is the term “FinTech”. It is a contraction of the words “finance” and “technology”. It refers to the technological start-ups that are emerging to challenge traditional banking and financial players.  They bring together the lenders and borrowers, seekers and providers of information, with or without a nodal intermediation.

Major FinTech products and services currently in the market place are Peer to Peer (P2P) lending platforms, crowd funding, block chain technology, distributed ledgers technology, Big Data, smart contracts, Robo advisors, E-aggregators, etc.  FinTechs are attracting interest both in banking and investment services, which see them as the future of the financial sector. Further.  Reg Tech, Lend Tech, Insure tech, Wealth tech, Trade Tech, Paytech are the new generation users of AI and they all together put dominantly what kind of financial landscape will emerge in the wake of the ecosystem created by digital transformation.

In a report published by RBI, The Indian FinTech industry, which makes most use of AI, grew 282% between 2013 and 2014, and reached USD 450 million in 2015. At present around 400 FinTech companies are operating in India and their investments are expected to grow by 170% by 2020. The Indian FinTech software market is forecasted to touch USD 2.4 billion by 2020 from a current USD 1.2 billion, as per NASSCOM. The transaction value for the Indian FinTech sector is estimated to be approximately USD 33 billion in 2016 and is forecasted to reach USD 73 billionin 2020.

Block Chain & Cryptocurrency

A major change peeping into the banking sector as an iceberg in the sea is Blockchain technology and Cryptocurrency which work on the AI platform. These are the potential changes the modern banking is witnessing and it is seeping into the system at a very fast pace.  Blockchain technology provides a way for untrusted parties to come to agreement on the state of a database, without using a middleman. By providing a ledger that nobody administers, a blockchain could provide specific financial services.

 blockchain allows for the use of tools like “smart contracts,” which could potentially automate manual processes, from compliance and claims processing, to distributing the contents of a will. It doesn’t need a high degree of decentralization – but could benefit from better  coordination — blockchain’s theory of “distributed ledger technology (DLT),” could help corporates establish better governance and standards around data sharing and collaboration.

Blockchain technology and DLT could disintermediate key services that banks provide, including:

  • Payments:  By establishing a decentralized ledger for payments (e.g. Bitcoin), blockchain technology could facilitate faster payments at lower fees than banks. Facilitating payments is highly profitable for banks more so in Cross-border transactions. Cryptocurrencies like Bitcoin and Ethereum are built on public blockchains that anyone can use to send and receive money. In this way, public blockchains cut down on the need for trusted third parties to verify transactions and give people around the world access to fast, cheap, and borderless payments. Bitcoin transactions can take 30 minutes or up to 16 hours — in extreme cases — to settle. That’s still not perfect, but it represents a leg up from the average processing time for bank transfers.
  • Clearance and Settlement Systems: Distributed ledgers can reduce operational costs and bring us closer to real-time transactions between financial institutions.
  • Fundraising: Initial Coin Offerings (ICOs) are experimenting with a new model of financing that unbundles access to capital from traditional capital-raising services and firms.
  • Securities: By tokenizing traditional securities such as stocks, bonds, and alternative assets — and placing them on public blockchains — blockchain technology could create more efficient, interoperable capital markets.
  • Loans and Credit: By removing the need for gatekeepers in the loan and credit industry, blockchain technology can make it more secure to borrow money and provide lower interest rates.
  • Trade Finance: By replacing the cumbersome, paper-heavy bills of lading process in the trade finance industry, blockchain technology can create more transparency, security, and trust among trade parties globally.

Blockchain technology is still in its infancy and lots of work on its functional and regulatory aspects are to be done and a lot of the actual technology has yet to be perfected. But this can safely be assumed that it has the potential to replace banks altogether. Hence, by supplement traditional financial infrastructure and making it more efficient blockchain technology will indeed transform the banking industry.

AI: The Journey Ahead

Here are some applications of AI in the Banking industry that will revolutionize the industry in the coming years:-

Anti-money laundering (AML) Pattern Detection: Most of the major banks across the globe are shifting from rule based software systems to AI based systems which are more robust and intelligent to the anti-money laundering patterns. Over the coming years, these systems are set to become much more accurate and fast with the continuous innovations and improvements in the field of AI.

Chat bots: Chatbots are artificial intelligence based automated chat systems which simulate human chats without any human interventions. They work by identifying the context and emotions in the text chat by the human end user and respond to them with the most appropriate reply. With time, these chat boats collect massive amount of data for the behaviors and habits of the user and learns the behaviors of user which helps to adapt to the needs and moods of the end user.

Algorithmic trading: Plenty of Hedge funds across the globe are using high end systems to deploy AI models which learn by taking input from several sources of variation in financial markets and sentiments about the entity to make investment decisions. Reports claim that globally more than 70% of the trading is actually carried out by automated artificial intelligence systems. Most of these hedge funds are likely to use AI for better decision making.

Fraud detection and prevention: Fraud detection is one of the fields which has received massive boost in providing accurate and superior results with the intervention of artificial intelligence. It’s one of the key areas in banking sector where artificial intelligence systems have excelled the most. Fraud detection has come a long way and is expected to further grow in coming years. As per the RBI report frauds in banking sector increased to Rs.41167 Cr, a rise of 72%  in the FY 2017-18. Thus, AI may become a profound vigil for the banking sector.

Banking sector has been witnessing a rapid rise in the instances of cybercrimes in the recent years. AI also plays a vital role in protecting personal data, AI-based fraud detection application development services can address the issue of fraud and data breach while developing an AI-powered mobile application for the banks. It can be useful in preventing such attempts and has tremendous scope in the domain of cybersecurity.

Customer recommendations: Recommendation engines are a key contribution of AI in banking sector. It is based on using the data from the past about users and/ or various offerings from a bank like credit card plans, investment strategies, funds, etc. to make the most appropriate recommendation to the user based on their preferences and the users’ history. Recommendation engines can become more successful and a key component in revenue growth accomplishment.

Handle risk management: Risk assessment process while giving loans is very complex and critical process. It requires both accuracy and confidentiality. AI can handle and simplify this process by analyzing relevant data of the prospective borrower. AI can combine and analyze the data related to the latest transactions, market trends, and the most recent financial activities to identify the potential risks in giving the loan. Banks can also get the idea of the prospect’s behavior with AI-based risk assessment process.e.g, SBI launched Credit Underwriting Enginerreing Model under Project Vivek to minimize the probability of error in risk identification. The predictive analytics can manage the entire process smoothly.

Hedge fund management: Globally, hedge funds prefer AI-based models. It is because AI-related tools can fetch real-time data from various financial markets across the world. Hedge fund trading and management can be done on the move with the help of AI-based mobile app solutions for the banking sector. These solutions help the banks to mitigate the risks associated with overexposure and user intervention in the market. AI can provide the next-gen security to the banking sector

Cash Application and Collections Management: Some companies have developed robots that apply payments to the company’s ERP system with no human intervention. This has been accomplished through machine learning. Initially there will have to be some matching and human intervention, but as time goes this system can work for all forms of payment including checks, ACH (Automated Clearing Houses) and wires. AI allows for easy handling of large data volumes with complex and changing patterns.Collection management can take advantage of the uses of “rule-based” algorithms in many different forms as well. This could include the use of collection management software with automated dunning letters, workflow management, escalation rules and more. Also, electronic invoice presentment and payment (EIPP) can be another good use, allowing the customer to receive and pay invoices electronically without the need for human intervention. Automation could also be incorporated to include payment tracking and trend analysis for better predictive cash flow planning.

   Challenges with AI

High Cost to Fully Implement: The purchase, maintenance and repair costs require large capital investment as they are very complex machines. In the case of severe breakdowns, the procedure to recover lost codes and reinstating the system may take time and have high costs. Hence, getting fully dependent on this system may cost heavily due to even a simple mishap.

Loss of Data: As with many highly utilized systems powered by big data, there is always the risk of systems being corrupted that could result in the loss of data. Once lost, it is very difficult (if not impossible) to retrieve the data. This can cause serious trouble to a business.

No Original Creativity: Creativity or imagination is not the forte of artificial intelligence. Human beings are highly sensitive and emotional intellectuals, which AI will not be able to achieve. However, human creativity may develop further in areas that AI has less influence, like credit managers being more involved in the high level/major account decisions most companies may not leave solely to AI processes alone.

Job Security: Most of these jobs have been associated with positions that handle repetitive workflow. AI enables 24*7*365 execution, which human capital cannot achieve. However, AI will not be able to do strategic planning, make high level/exception decisions, negotiate with customers, etc. Within accounts receivable and credit, Full Time Employees reductions may occur with positions that handle repetitive and redundant tasks that are easy to automate with robotics like cash application, along with onboarding new accounts through electronic credit applications and risk model analytics.

Regulatory and Supervisory issues

FinTech has significant implications for the entire financial system in India. The multiplicity of firms and a mosaic of business models complicate the classification of the various types of activities, products and transactions covered under the FinTech spectrum. RBI has taken various initiatives in the technology-enabled banking space as listed below:

(i) Issued in-principle approvals for Payments Banks, of which some have since been licensed

(ii) Allowed entry of non-banks in the payments space both as payment system operators and

technology service providers

(iii) Introduced Bharat Bill Payments System (BBPS)

(iv) Published a consultative paper on Card Payment Infrastructure

(v) Issued a consultation on Peer to Peer (P2P) lending

(vi) Issued Directions on Account Aggregators

(vii) Authorised payment solutions provided by NPCI such as NACH, AEPS, IMPS, Unified Payment Interface (UPI)

(viii) Given in-principle approval for National electronic toll collection project.

Government Support

According to the report of Artificial Intelligence Task Force 10 important domains of national relevance have been identified which includes Fintech and Public Utility Services among others. The vision guiding the report is AI as a socio economic problem solver at large scale rather than only a booster of economic growth.

The reports to address the role of government in AI policy making, AI towards welfare of human being and identification os sectors where employment can be generated.

 A 5-year plan has been deleberated which aims at the following issue:-

i) Setting up of National Artificial bIntelligence Mission(NAIM) being funded under the Union Budget to the tune of Rs.1200 Crore spread over 5 years i.e., Rs.240 Crores every year.

ii) The mission has envisaged setting up of six Centres of Excellence, a generic AI test bed for validation of AI technology developers, centre for aggregation and interpretation of AI data generated and promotion of talent pool through various incentivisations.

iii)setting up of Digital Data Bank,marketplaces and exchanges to ensure availabilty of cross industry data and information of AI application with the required sharing related regulation.

iv) formation of Standard for the design,development and deployment of AI based system.

v) Introduction of AI based curriculum, AI based education and re-skilling and

vi) leveraging key international relationships and participation in AI basedinternational standard setting discussions.

Thus it can be safely assumed that the domestic environment is very conducive for the incubation of AI and with digital drive AI is set to emerge as a big game changer.


The penetration of AI in the banking sector is somewhat limited to date. The distinct data sets and the risk of confidential data are primarily responsible for the sluggishness of AI integration in the banking system.

The banking industry has leveraged the Telecom revolution by integration of AI in Mobile Apps for Banks. Automated AI-powered customer services viz., Digital personal assistants and chatbots have revolutionized the customer services.  Such apps readily meet the user’s expectations with personal, contextual, and predictive services that track the user’s behaviors and give them personalized tips and insights on savings and expenses. It is easy for a banking app integrated with AI-related features to show services, offers, and insights in line with the user’s behavior. Banks are giving online wealth management services and other services by integrating AI advancements into the app.

Wealth management and portfolio management can be done effectively and efficiently with AI. It can bring ‘banking at fingertips’ for the users who just hate to visit the banks. It strengthens the mobile banking facility by managing basic banking services. Customers can get the benefits of automated and safe transactions. They get notification instantly for any suspicious transaction as per their usual patterns.

In conclusion, it is evident that AI is here to stay, and is impacting a large number of industries, Banking is an early adopter of this trend. This trend is likely to grow exponentially in the future.

The regulations are also passing through the age of innovations and Banks / Financial institute that embrace the trend with greater focus on data security and regulatory complianeces are likely to be winners over the next 10 years. Management, therefore, should look forward for a clear plan and infrastructure in place to make the most out of AI systems.

(Sources used: Internet, RBI/Governmrnt reports, International publications, etc.)

Authored by

Senior Manager ( Faculty) at a PSB

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