Page 30 - Banking Finance February 2026

ARTICLE

How AI is changing

credit, compliance,

and customer

Dr Rakesh Agarwal
onboarding Banking Finance
Editor

AI is redefining how banks assess risk, comply with regulation, and engage customers. In credit, it
enables more inclusive and predictive decision-making. In compliance, it enhances oversight while
reducing operational strain. In onboarding, it balances speed with regulatory discipline.

A rtificial intelligence (AI) is no longer an experimen- values. While these models have served the industry well,

they are often backward-looking and slow to respond to
tal add-on in banking. It has moved decisively into
the core of how banks assess creditworthiness, meet
regulatory expectations, and onboard customers at scale. changing borrower behaviour or economic stress.
From loan approvals that once took weeks to instant digital AI-driven credit systems introduce a more dynamic ap-
onboarding journeys completed in minutes, AI is reshaping proach. Machine learning models can process vast volumes
the operating model of banks across retail, corporate, and of structured and unstructured data, identifying patterns
MSME segments. that may not be visible through conventional scoring tech-
niques. Transaction behaviour, cash-flow volatility, spending
This transformation is not merely about speed or automa- patterns, and even seasonality trends can be analysed in
tion. At its core, AI is altering decision-making itself-how risks near real time to generate more nuanced credit insights.
are evaluated, how compliance is monitored, and how cus-
tomer intent is understood. For banks operating in increas- For retail and MSME lending, this has opened new possibili-
ingly complex regulatory environments, the challenge is not ties. Borrowers with limited formal credit history-often ex-
whether to adopt AI, but how to deploy it responsibly, trans- cluded under traditional underwriting-can now be assessed
parently, and in alignment with supervisory expectations. using alternative data sources such as account activity, digi-
tal payments, and supply-chain relationships. This has sig-
AI in credit: from static scoring to dy- nificant implications for financial inclusion, particularly in
emerging markets.
namic risk intelligence
Traditional credit assessment models in banking have relied At the same time, AI enables continuous credit monitoring
heavily on structured historical data-income statements, rather than one-time assessment. Early warning systems
credit bureau scores, repayment histories, and collateral powered by predictive analytics can flag stress signals well

26 | 2026 | FEBRUARY | BANKING FINANCE