The rapid rise of Artificial Intelligence (AI) is gradually changing the way businesses operate. While most conversations centre around AI’s impact on productivity and logistics, one crucial area that is ubiquitous yet rarely discussed is its impact on the banking industry.
“Banks have been using AI internally, just not publicising it as much,” said Sarthak Pattanaik, Head – AI Hub, Engineering, Bank of New York Mellon (BNY), an institution thriving for over 240 years.
AI is transforming industries across the globe, and the banking and financial services sector is no exception. The sector is witnessing innovation driven by AI that is disrupting traditional ways of banking. From improving operational efficiency to enhancing customer experience, AI’s potential in banking seems boundless, and its impact is already being felt.
In an exclusive conversation with indianxpress.com, Pattanaik highlighted how AI impacts the banking sector and spoke at length about how one of the world’s largest custodian banks is embracing AI. “Banking, being a regulated sector, has historically valued deterministic solutions as we are trusted with someone else’s money,” Pattanaik explained.
However, he added that at a macro level, banking is about asset creation, movement, storage, and servicing – and an asset is data.
‘Banks have been using AI’
Adding about the significance of data, the executive said the banking industry has taken data, transacted on it, reported on it, and built analytics. “Banks have used that to optimise balance sheets, liquidity needs, collateral needs, etc. through advanced analytical maths and statistical functions – which could be considered AI, though not at the scale of something like Tesla’s computer vision,” he said.
When asked about how banks have been embracing AI, Pattanaik said that based on emerging trends, banking institutions are using AI for customer experience and hyper-personalization, operational efficiency through straight-through processing, automation of call centres etc, managing risk – fraud, cyber security, predictive abilities, and improving employee experience.
“AI allows us to link different data sets to provide a holistic customer view and enable hyper-personalised decisions and recommendations like Amazon. It helps manage fraud better by understanding counterparties and transactions. AI also enables an omnichannel experience whether online, via messaging, files, or mobile,” he said while explaining how AI can enhance customer experience.
‘Operational efficiency and process automation’
Yet another major application is operational efficiency through process automation. Pattanaik cited examples like “decision re-application using AI to learn patterns in incorrect client data and make transactions straight-through.” AI can streamline labour-intensive back-office processes, reducing human errors and costs. Areas like call centre automation and the mobilisation of unstructured data from documents and communications also have immense potential.
Regardless of the merits, AI integration is not without its challenges, especially for the banking industry which is steeped in regulation and risk management. Pattanaik outlined three key risk areas: “Platform risks like data privacy and cybersecurity, model risks around fairness, ethics, accountability, and transparency, and product risks in terms of unintended uses or regulatory compliance.”
BNY Mellon is among the pioneers in the platform-first approach to building guardrails. “We are building an AI platform that any team can plug into for data, compute, processes, and controls – letting them focus on the core problem rather than recreating non-differentiating elements each time,” he said.
‘AI and challenges’
As seen across almost all industries, a major concern is the imminent job loss due to AI adoption and automation. When asked about the surge in layoffs in recent times, Pattanaik shared a different perspective. “I don’t think it’s about layoffs, but about learning new skills, upskilling and reskilling. AI is about automating tasks, not entire jobs.”
He went on to cite examples like call centre agents being augmented to focus on providing expertise rather than just information gathering. According to him, roles across banks will evolve, from a small team building AI models to engineers learning to develop “learning systems” rather than just deterministic code.
Based on our conversation, we gauged that as the AI revolution gathers momentum, traditional banks need to evolve into more agile, data-driven enterprises especially to stay competitive. Several retail banks have already made strategic investments in AI applications designed for fraud detection, trading, and customer service. Startups and fintechs too are emerging to offer banks with AI-powered solutions. According to Pattanaik, custodian banks have a unique opportunity in this wave of disruption. “Our role is to ensure our clients trust us with their assets and money, to keep it safe and mobilise it when required,” he said.
Even as challenges persist, the banking landscape is shifting at a breathtaking pace. Financial institutions that are proactively adopting AI through robust governance models will likely ride this new wave with ease. “The speed of innovation is so high, we have to build architectures to manage that risk. But for us, that’s an opportunity more than anything else,” said Pattanaik.