Artificial intelligence in financial services

Artificial intelligence (AI) is not a new phenomenon. It is human-made technology, that simulates, replicates and can potentially replace or add to human intelligence. It’s been under development for over 60 years. You and I have been using it for quite some time now — be it the maps we use for navigation, chatbots we use, digital assistants, video gaming, and much more. AI is similarly used a lot in the financial services sector.

The financial services industry has seen adoption of technology to solve for challenges faced by and sometimes even posed by the consumers. The business of finance, at the core of it, is to price the risk. With the India digital stack development in the past decade, as well as mobile telephony adoption, the financial services industry has had to use technology to serve its consumers faster, with cheaper access costs. The sector uses AI to improve efficiencies and to optimise its processes, ranging from risk management, to credit underwriting, to investment options, to claims settlement, to fraud detection.

The consumers of today expect faster turnaround time, quicker decisioning by financial institutions, wider availability of financial solutions, and ease and convenience of interacting with these institutions through digital interfaces. The volumes of transactions and consequent data handling by financial institutions has expanded and grown leaps and bounds. Handling such a large data set (both structured data as well as unstructured data), with data protection and cyber protection measures, many of the financial institutions use AI enabled technologies. Success factor for many of the financial services entities depends in their ability to use data and digital for offering consumer-centric products.

What does AI offer that humans don’t have? At least for now, it’s objectivity without emotions. That’s hugely useable skill set in finance industry. Unlike humans, machines are not biased. Credit scoring, based on complex alogortihms with multiple sets of data is possible. It eliminates human bias and can give better clarity to data analysis. AI algorithms also are of huge help with risk management. The capabilities include identifying early signs of risks, risky behaviour or non-compliant behaviour. With process automation, it can solve for high frequency, repetitive tasks to be automated without human intervention, and allows for consistent output. It is possible for AI based machines to scan data, convert them to machine readable format, verify the contents, use data to analyse and extract necessary information and to give a decision. In this era of personalisation, consumers expect great digital interface from their financial service providers. They expect faster customer onboarding, which is why various tools are used — such as Optical Character Recognition, that automatically extracts relevant information from the uploaded documents. In addition, using data points from various sources, like income documents, banking history, employment history and credit scores, AI generates credit profiling. The financial institutions use algorithms to identify consumer behaviour patterns and can offer the relevant products for the consumers.

AI’s potential in preventing financial frauds is already being harnessed well. In the near future, it could even help the financial regulators in conducting real time supervision of the entities in their sector. Currently banks use AI tools for preventing credit card frauds, and in conducting Anti-money laundering checks. In stock markets, AI can help in detecting suspicious patterns. But then, any AI is only as good as the algorithm that humans want it to perform.

Capabilities & concerns

Data governance: The abundance of data sources is both a boon to the financial sector, as well as a worry. Quality and accuracy of data, veracity of such data, traceability of the data to its origination are key concerns. Only if data governance to prove for these work well, can such a data be useful for analysis.

Omni channel: The consumer expectation is that the finance brand would allow for interaction across physical branches, digital modes. They further expect the brands to use data available (with sufficient data protection and consumer privacy norms) to offer personalised services.

Competition: Traditional financial institutions now have stiff competition from the FinTechs across lending, asset management, payments, insurance space. While there is positive regulatory arbitrage in favour of the traditional entities, this is bound to fade away in years ahead. So the consumers expect the best of experience and product availability. This is where the concerns about data governance comes in, and regulators are rightfully worried about need for higher benchmarks. AI can help in this aspect of preventing data theft or misuse.

Regulations & Compliance: Financial regulators expect lot of data to be submitted by the entities they regulate. AI enabled solutions can offer automated collation of standardised regulatory reports that the system can submit to the regulators, without manual intervention.

Continuous improvements in AI and technological advancements have already changed the way we imagined or dealt with financial institutions, even a decade ago. We can anticipate more changes and convenience of dealing with institutions. But then onus of such an interaction is not with the brands alone. As a consumer of financial services, you must be aware of your consumer rights, as well as responsibility in not sharing data easily, unless it’s regulatorily mandated. As much as rights we expect as consumers, we also have responsibilities to be aware about those rights and how to handle our data better. Financial literacy is still low in India, and it is equally onus of all of us (as consumers) in asking those questions from our financial service providers. In other terms, just don’t get into a financial product that you don’t understand. Human intelligence is still better than AI, at least as of now.

Dr Srinath Sridharan is a corporate adviser and author of Time for Bharat. He tweets @ssmumbai

Published on: Wednesday, February 22, 2023, 06:00 AM IST