Artificial Intelligence Is Reshaping Banking Services

For decades, banking was defined by a simple proposition: mobilise deposits, extend credit and safeguard capital. Today, financial institutions have evolved into intelligent, technology-driven ecosystems that enable consumers and businesses to access payment services, credit, investments, insurance, digital banking and foreign exchange through a single, interconnected platform.

This transformation has been underpinned by successive waves of innovation, from cloud computing and advanced analytics to digital public infrastructure, embedded finance and fintech-led business models. Artificial intelligence is now emerging as the next foundational layer, reshaping how financial institutions design products, manage risk, engage customers and operate at scale.

Unlike previous technology cycles that primarily digitised existing processes, AI introduces intelligence into every layer of financial services. It enables institutions to automate complex decision-making, personalise financial experiences, improve operational efficiency and strengthen enterprise-wide risk management. As banks and fintechs compete in an increasingly digital marketplace, AI is becoming less of a competitive advantage and more of a strategic necessity.

Enterprise Scale
Among the various AI technologies being adopted across the banking sector, generative AI has emerged as one of the most transformative. Financial institutions are increasingly deploying large language models to automate documentation, accelerate underwriting and claims processing, generate regulatory reports, support customer service and improve employee productivity.

Generative AI also enables hyper-personalised customer engagement by analysing vast datasets in real time to recommend financial products, anticipate customer needs and support advisory services. Internally, it is helping institutions simplify knowledge management, software development, policy interpretation and enterprise-wide decision support.

However, moving from pilot projects to enterprise-wide deployment requires far more than technology adoption. Institutions are prioritising high-value use cases, adopting phased implementation strategies and building reusable AI capabilities that can be scaled across business functions. At the same time, significant investments are being directed towards data infrastructure, cloud computing, model management and AI-ready technology platforms to support increasingly compute-intensive workloads.

Intelligence Layer
In retail banking, AI is enhancing deposit mobilisation, credit underwriting, collections management, customer service and personalised financial advisory. Banks are increasingly using predictive analytics to improve customer retention, recommend products and optimise lending decisions. Commercial and corporate banking operations are leveraging AI to strengthen credit assessment, trade finance, pricing strategies, relationship management and fraud detection.

Beyond customer-facing services, AI is transforming enterprise operations. Institutions are deploying intelligent systems across client onboarding, know-your-customer verification, anti-money-laundering controls, compliance monitoring, cybersecurity, risk management, software engineering, data governance and real-time operational decision-making. AI is increasingly serving as the intelligence layer that connects front-office, middle-office and back-office functions, enabling faster, more accurate and more resilient financial operations.

Emerging Risks
The increasing adoption of AI across banking operations introduces a range of operational, financial and reputational risks that require ongoing oversight. AI models may generate inaccurate or biased outputs due to data quality issues, model limitations or changing operating environments, potentially affecting credit assessments, customer outcomes and risk management processes. The growing use of generative AI also presents challenges related to explainability and accountability, particularly in regulated functions such as lending, compliance and financial advisory, where transparency and auditability remain essential.

The wider deployment of AI also heightens exposure to cybersecurity, data privacy and third-party risks. AI systems process large volumes of sensitive customer and transactional data, requiring robust safeguards against cyber threats, data breaches and model manipulation. In addition, greater reliance on external AI models, cloud infrastructure and technology providers may increase operational dependencies and vendor-related risks. As regulatory frameworks and industry standards continue to evolve, financial institutions are expected to strengthen AI monitoring, model validation and governance practices to support safe and responsible adoption.

Governance Test
As AI becomes deeply embedded within financial institutions, governance is emerging as one of the defining priorities for the banking industry. These risks extend beyond technology and have direct implications for consumer trust, regulatory compliance and financial stability.

Consequently, boards and senior management will need to assume greater responsibility for AI oversight throughout the model lifecycle, from development and validation to deployment, monitoring and periodic review. Robust governance frameworks, independent model validation, transparent audit trails and meaningful human oversight are becoming essential components of responsible AI adoption.

In India, the Reserve Bank of India has emphasised that AI deployment must remain transparent, accountable, ethical and customer-centric. In its discussions on the Framework for Responsible and Ethical Enablement of Artificial Intelligence, or FREE-AI, the regulator has highlighted the importance of board-approved AI policies, explainability standards, strong data governance and institutional accountability to ensure that technological innovation strengthens rather than compromises consumer protection and financial stability.

Bank-Fintech Model
The next phase of financial services is unlikely to be defined by competition between banks and fintechs. Instead, it is expected to be shaped by deeper collaboration between the two. Banks will continue to perform their core role as regulated financial institutions, providing deposit mobilisation, balance-sheet lending, capital allocation, compliance, risk management and custodianship of customer financial data. Their competitive advantage will increasingly rest on trust, regulatory expertise and resilient financial infrastructure.

Fintechs, meanwhile, will continue to serve as the innovation layer of the financial ecosystem. Their strengths in artificial intelligence, embedded finance, digital onboarding, real-time lending, customer experience and data-driven product development position them to build highly personalised financial solutions that can be rapidly deployed across digital channels.

Credit Rails
The Open Credit Enablement Network, or OCEN, marks a shift in India’s lending ecosystem by creating a standardised, interoperable framework that connects borrowers, lenders and digital platforms through application programming interfaces. Much as the Unified Payments Interface helped standardise digital payments, OCEN seeks to simplify and broaden access to credit by embedding lending directly within digital ecosystems.

It operates as a set of common protocols that allow multiple participants, such as loan service providers, marketplaces and financial institutions, to interact efficiently, thereby reducing the cost and time involved in lending.

A key pillar supporting OCEN is the Account Aggregator framework, which can enable lenders to move beyond traditional credit-score-driven lending models towards cash-flow-based assessments using real-time financial information, such as bank transactions, GST records and tax filings. As a result, creditworthiness can be evaluated more accurately, especially for individuals and small businesses that lack formal credit histories.

OCEN also supports embedded finance, under which loans can be offered at the point of need. For instance, a small business could receive instant working capital through an e-commerce or fintech platform. This model not only enhances accessibility but also improves efficiency by integrating credit into everyday digital interactions.

Furthermore, OCEN promotes financial inclusion by expanding formal credit access to underserved segments such as micro, small and medium enterprises, gig workers and first-time borrowers. In essence, OCEN has the potential to make lending more inclusive, data-driven and real-time.

Digital Lending
Personal loans extended by digital non-banking financial companies, or NBFCs, have witnessed strong growth, supported by rapid digitalisation, faster loan disbursals, expanding financial inclusion and technology-driven underwriting models.

CareEdge Advisory estimates that the personal loan portfolio of digital NBFCs will grow at a compound annual rate of 26-28% through 2029-30, reaching between ₹3.6 trillion and ₹3.9 trillion. The corresponding portfolio at banks is estimated to grow at 13-15% a year over the same period. This difference reflects the increasing adoption of digital credit and the continued expansion of India’s retail lending ecosystem.

Next Phase
The convergence of these complementary capabilities is expected to accelerate the growth of banking-as-a-service, embedded finance, co-lending and AI-powered financial platforms. In this emerging model, banks provide regulated infrastructure and financial resilience, while fintechs deliver technological agility, innovation and more responsive customer experiences.

As AI matures, financial institutions will increasingly compete not merely on the products they offer, but on the intelligence embedded within those products. Institutions that combine trusted financial infrastructure with scalable AI capabilities and responsible governance will be better placed to shape the next generation of global banking and financial services.

(This article is published by arrangement with CARE Analytics and Advisory Private Limited .)