Technology
Banking on AI for your customer engagement
Published by Jessica Weisman-Pitts
Posted on November 4, 2022
4 min readLast updated: February 3, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on November 4, 2022
4 min readLast updated: February 3, 2026
By Sanat Rao, Chief Business Officer, Global Head, Infosys Finacle
The past number of years have seen banks deal with a number of new issues, such as rising consumer expectations, competition from digital competitors with superior experiences for customers, technological advances, and tighter regulation. Many banks have begun to digitise in order to respond to these challenges and stay relevant, yet as traffic begins to flow to digital channels, banks downsized their branch networks to save costs.
When the pandemic hit, digital transformation was ramped up further. This was especially true for the traditional brick and mortar banks – whose cost to income ratios were double that of their digital counterparts.
Now we are in a recession, which has forced all of us to tighten our belts. Spending is down and the current situation could remain as it is for a while. The economic downturn has impacted most banks as well, with many having to cut spending on recruitment and in a lot of instances, let further staff go. While this downsizing may have alleviated some of the economic pressures (an estimate from the Financial Conduct Authority suggests that closing one bank branch saves £590,000 in a year), banks still need the resources to be able to engage with their customers to ensure that they are able to retain them and attract new ones.
A solution to this is to use data and AI to acquire customer knowledge and build engagement. Machine learning (ML) can be used to analyse data to provide banks with insights that feed into contextual experiences, or conversational AI to enable “human-like” interactions with customers. Cora – the virtual assistant at NatWest – showed how this can be done by helping thousands of customers impacted by the pandemic to reschedule their loan and credit card payments.
Understanding customers is the key to better engagement. AI and analytical solutions allow this by providing insights to help customers manage their finances, as well as recommending next steps to them. However, comprehensive data is needed in order to provide the most effective engagement. Banks should use a platform that gathers information across customers’ other commercial relationships and location to be able to understand preferences and anticipate future needs. For example, a collection of data based on a customer’s income and expenditure pattern, age and financial assets and liabilities, will allow a bank to recommend investing in a certain company or reducing spending on eating out too often.
Another benefit of the use of data analytics is that it stops banks from annoying customers with services and offers they do not need. Instead, it allows banks to deliver the right service at the right moment of time to the right person. This reduces the chance of customers going elsewhere to fulfil their financial requirements.
The previously mentioned examples can allow banks to implement a high level of engagement, where they effectively use AI to deliver highly personalised, contextual experiences, and recommendations that can improve their customers’ financial well-being.
Yet, while providing customers with this data can help them manage their finances more effectively and thereby allow banks to retain them, insights from our research, Maximising Digital Banking Engagement, show that only 5 percent of banks are successful in creating effective customer engagement. Most only do so with ad-hoc fixes focused on customer experience instead of a long-term plan.
The technologies required for this data collection and analytics can also present challenges. The costs can be high and the outcomes are dependent on the data used. Banks should therefore make their choices based on their own position, considering such factors as their business goals, customer bases, and technology readiness.
Sanat Rao
As Head of Infosys Finacle and Member of board of EdgeVerve Systems (an Infosys company), Sanat leads Infosys’ globally successful banking software and platforms business. He brings over 3 decades of global experience in international banking and technology-led business transformations. His experience spans strategic partnerships with clients through their digital journey across multiple markets globally. In his present role, Sanat is responsible for growing Infosys’ banking software business and driving industry leadership with innovations in the banking space.
Sanat is a keen global traveller, a passion that he shares with his wife and 2 sons, and he counts snorkelling and diving at the barrier reef in Australia and trekking at Machu Picchu, Peru amongst their more memorable experiences. By qualification, he holds an MBA, besides being a Digital Anthropologist (University College London) and an AI Ethicist (University of Cambridge). Sanat lives in Surrey, UK.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn. In banking, AI enhances customer service and operational efficiency.
Digital banking involves the use of digital technology to provide banking services. It allows customers to conduct financial transactions online, improving convenience and accessibility.
Data analytics is the process of examining data sets to draw conclusions about the information they contain. In banking, it helps understand customer behavior and improve services.
Machine learning is a subset of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed.
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