How AI can pave the way for next-gen banking and finance

Ramprakash Ramamoorthy | Updated on July 26, 2021

Critics of the technology contend that it can be used as a tool for social control by repressive governments.   -  istock/metamorworks

Over the last few years, artificial intelligence (AI) has gained incredible prominence across business verticals with organisations realizing its value. A recent report from the Reserve Bank of India responding to an RTI petition related to bank fraud has shown that the banking and financial services (BFS) sector can greatly benefit from leveraging AI. According to official data, as of March 2021, banks in India reported fraud worth ₹4.92 trillion. With a staggering increase in fraudulent practices by malicious actors, the BFS industry is in desperate need of better monitoring and management tools. We can make it work in five ways in which technology can drive security and ensure better management in the industry.

Continuous monitoring of user behaviour

With banks facing heightened data breaches and cyberthreats, using advanced analytics to help identify potential malicious activity can ensure a safe online banking experience. Continuous monitoring of user behaviour can help banks identify anomalies more quickly and efficiently, letting them take remedial action before end users are impacted and regulators spring into action.

Using a continuous monitoring approach, banks can establish user behaviour patterns and determine whether a transaction is typical for a user or raises suspicion. Banks can offer a more user-friendly experience by eliminating additional authentication measures for typical transactions while maintaining these measures for transactions deemed suspicious. This ultimately diminishes the attack surface to reduce the risk of losses while enhancing the user experience.

Risk management and compliance

Giving credit is a major function of banks yet granting loans has always been considered a risk. Banks typically rely on heavy historical credit data to determine the lending risk of an applicant. AI-assisted underwriting provides a more in-depth view of an applicant, helping banks make well-considered decisions in sanctioning loans.

It draws together big and traditional data; social, business, and internet data; and unstructured data. AI and analytics-aided techniques can detect anomalous behaviour, provide multivariate forecasting, and improve risk control to increase the accuracy of credit card fraud detection.

As AI becomes more prevalent, banks can use it to keep an eye on diminishing returns, gain better visibility on risks, address data management issues, avoid loss of corporate knowledge, and speed up credit decisions.

Anti-money-laundering screening

The process of taking illegally obtained money and making it appear to have come from a legitimate source, or money laundering, has increased over the years. The technology used to identify suspicious activity linked to money laundering continues to evolve and become more accurate. Machine learning (ML) coupled with deep learning can help government systems and large financial institutions monitor for potentially fraudulent activity. These technologies help improve the quality of alerts.

Effective forecasting of cash flows

A cash flow forecast is a crucial part of decision-making in the financial professional space. However, manual adjustments can blur the valuable insights that the data has to offer and can have a huge impact on the realistic or successful usability of the forecast. AI can make forecasting feel effortless for users while providing reliable output. When an organisation implements AI, it consistently learns the cash flow patterns and provides an accurate forecast specific to that organisation.

Automating processes using OCR techniques

Powerful optical character recognition (OCR) engines can be deployed within the banking sector to recognize written letters and characters and reproduce them digitally for later use. This allows the bank to digitise documents, automate invoices and purchase orders, and lowers the chance of human error. Banks can use OCR to scan paper applications, handle bank statements, recognize and alert a human about new text arrangements, and accurately digitise the personal information on bank cards used at ATMs so that it can be verified by a security system.

Businesses in the BFS sector that thrive on data-driven methods and AI are transforming the way we interact with money. The future lies in AI and ML. Embracing digitisation and AI will bring the power of advanced data analytics to combat fraudulent transactions and improve compliance.

The writer is director of research, ManageEngine

Published on July 26, 2021

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