“AI is probably the most important thing humanity has ever worked on.” – Sundar Pichai, CEO, Google and Alphabet.

Artificial Intelligence (AI) seems to be everywhere nowadays – at home, on our phones – Alexa, Siri, etc. Before we know it, AI will be in just about every product and service we buy and use. Further, its application to business problem solving is growing by leaps and bounds.

Alan Turing, who broke the Nazi encryption machine “Enigma” and helped the Allied Forces win World War II, is considered the father of AI or Machine Learning. In its essence, AI is the endeavour to replicate or simulate human intelligence in machines.

Artificial intelligence and cognitive computing are redefining numerous areas of business, including banking. Processes and functions that involve large amounts of information, significant complexity and nuanced analysis are all prime candidates for an AI-enabled revolution. AI can help banks improve their processes, significantly reduce time delays, increase accuracy and reduce cost. And given the information-intensive nature of banking, multiple processes and functions are ripe for cognitive-enabled transformation.

Today’s customers increasingly expect faster, personal, and meaningful services and interactions with their banks and have little tolerance for generic unsolicited messages. Therefore, banks must leverage AI to balance the need for privacy and security with personalisation and engagement.

How are Indian Banks employing AI?

Most of the Indian banks are using AI to improve customer experience by adding chatbots as an additional interface for customers. SIA by State Bank of India (SBI), EVA by HDFC Bank and iPal by ICICI Bank can be cited as examples.

HDFC Bank’s Eva can assimilate knowledge from thousands of sources and provide simple answers in less than 0.4 seconds. Going forward, Eva would be able to handle real banking transactions as well. SBI’s SIA addresses customer enquiries instantly and helps them with everyday banking tasks just like a bank representative. ICICI’s iPal helps in answering queries, in financial transactions and discovering new features. It now supports an average of 1.5 million customer queries every month.

State-owned banks have been slow to leverage AI, largely because AI implementation requires banks to operate outside of the traditional privacy framework. Justice Srikrishna Committee has opined that the biggest challenge in regulating emerging technologies such as AI lies in the fact that they may operate outside the framework of traditional privacy principles. Reserve Bank of India (RBI) would need to frame regulations on emerging technologies and data privacy, thus ensuring the business interests of the banks.

While improving customer experience has been the primary focus of most of the Indian banks in using AI, it can also be used in other areas such as fraud prevention and detection, compliance, risk monitoring, to name a few.

Fraud detection and prevention and Risk Management

According to RBI’s Annual Report, 2019, losses due to banking frauds have risen by a whopping 73.8%. As per RBI data, it took an average of 22 months for the banks between the occurrence of fraud and its detection. AI can play an important role in fraud detection and prevention. Hence, banks must deploy context-sensitive AI solutions to enable advanced and adaptive real-time monitoring of their payment networks.

These AI solutions additionally leverage relevant data points to assess transaction risk, true identity-matching, and identification of complex typologies and patterns. For example, Axis Bank is using an AI algorithm to authenticate paper checks rather than relying on tellers to do so. The technology analyses check signatures to detect changes or signs of hesitation that indicate fraud, including blots or skips that could be invisible to the human eye.

Online fraud is another area of massive concern for banks as they digitise at scale. Risk management at internet scale cannot be managed manually or by using legacy information systems. AI would help banks develop predictive analytics to examine transactions on a real-time basis. This would include mitigating fraud by scanning transactions for suspicious patterns and enabling risk analysts with the right recommendations for curbing risks on a real time basis.

Compliance

Compliance is one of the most vital aspects of banking. This is especially true for Indian banking, where systems, procedures and regulations will always be going hand-in-hand with the businesses. Abiding by these is a necessity for the banks to stay and survive in the market and AI will be able to provide smarter solutions by way of using cognitive technologies.

Digitization of processes

Indian consumers are next only to China in the use of mobile devices and the internet. AI would be able to analyse the consumers’ spending patterns and would be able to offer them suitable recommendations on investment and risk profiles, based on these data. Already many private sector banks are digitizing the KYC process to eliminate the need for submission of physical documents and their verification. This process can be further simplified by leveraging AI-based computer vision technology to verify documents, Optical/Intelligent Character Recognition (OCR/ICR) technologies to digitise scanned documents, and Natural Language Processing (NLP) to make sense of them.

Decision making in respect of loan products

Given that processing loans involve judgemental decision making on the part of loan officers, verification of the antecedents of the borrowers etc., most of these processes have remained manual. Some of the banks have automated retail loan processing to a certain extent, but still, there is a long way to go. AI can be used in respect of decisions based on available structured and unstructured data. For example, it can help predict potential loan defaulters and offer loss mitigation strategies that will work for them. AI can also help collate data from multiple sources and arrive at inferences, which can be used to make decisions. AI can also improve straight-through processing using Intelligent Automation to automate repetitive processes that need decision making. In fact, many Fintech companies are already using consumer profiles based on social media like Facebook, Twitter etc. for arriving at loan decisions.

Way forward

While AI has immense scope in transforming Indian banking, given the magnitude of the challenge, banks would benefit from a consortium-based approach for knowledge sharing on AI. This would also aid the smaller regional private players, cooperative banks etc. benefit from a broader nationwide secure banking network. AI in Indian banking is only set to grow and the banks would reap rich dividends over time.