It has been one year since OpenAI introduced its flagship generative AI product ChatGPT to the world. And since then, the world has been slowly warming up. However, initially, the banking and financial services sector hesitated to adopt generative AI, considering it a niche application. 

“Initial reluctance was rooted in regulatory scrutiny focused on data privacy, security, and compliance. The risk-averse nature of the industry also led to a cautious approach, driven by concerns about errors, biases, and unforeseen consequences. But it is gaining mainstream acceptance now,” Ashish Vora, President and Business Head of leading credit rating agency CRISIL Market Intelligence and Analytics, told AIM in an exclusive interview. 

Moreover, the lack of expertise and understanding of generative AI posed a challenge, with many institutions facing a shortage of skilled AI talent. This limited their ability to explore practical applications. Despite these initial barriers, the scenario has changed, with larger banks now leveraging AI for services like powering chatbots for customer service.

Vora brings over 30 years of financial leadership experience, including roles at Kotak Bank and Citibank. A co-founder of Pragmatix Services acquired by CRISIL in 2018, he oversees diverse verticals, including customised research, advisory, and growth and innovation.

CRISIL assesses the creditworthiness of companies and financial instruments, providing essential insights for investors and stakeholders. Additionally, it offers research, risk advisory, and infrastructure consulting services to support informed decision-making in the financial sector.

How CRISIL Uses AI

In today’s uncertain business environment, data and technology-driven risk management systems play a crucial role in helping organisations, especially in the BFSI sector, tackle geopolitical and economic risks proactively. Understanding risks across business functions requires a deep grasp of industry dynamics, market positioning, cost structures, and competition. Conventional risk models struggle with real-time data integration from diverse sources, making it essential to prioritise innovative risk analytics systems.

“This is where AI algorithms prove invaluable in processing extensive real-time data, revealing patterns, anomalies, and correlations within economic and geopolitical landscapes, helping financial institutions to anticipate potential risks, analyze market trends, political instability, and trade policies,” explained Arora.

Data-powered systems support sophisticated risk modeling and scenario planning, offering insights into the economic and geopolitical impact on portfolios and business strategies. They also streamline regulatory compliance through automated reporting and real-time updates. Identifying risks in real time enables swift mitigation strategies, preventing financial losses during economic or geopolitical shifts.

For example, CRISIL’s Advanced Risk Analytics System assesses the potential financial impact of any change in the economic scenario on a company using real-time data, even before quarterly results are published. 

Arora told AIM that the system relies on models built from a deep understanding of industries and years of data collected across 200 industries. The models simulate the value-chain linkages of each business process. When supplied with relevant data on economic and industrial activity, the system reflects the impact on company financials, aiding in informed decision-making.

Fintech’s Role in Financial Inclusion in India

“Fintech companies have significantly contributed to genuine financial inclusion in India by leveraging the government’s ‘India Stack,’ a digital infrastructure comprising digital identity, payment capabilities, and data storage,” said Arora.

This foundation facilitated the rise of fintechs, particularly in digital authentication, payments, and lending. Initiatives like Jan Dhan and technologies such as Open Credit Enablement Network (OCEN) and Account Aggregators (AA) further broadened financial access, especially for the unbanked sections, resulting in social and economic benefits.

The democratisation of data allowed non-banking financial companies (NBFCs) to expand into remote areas cost-effectively, optimising operational costs and reducing customer acquisition expenses. “Through innovations like AI and cloud computing, fintechs streamlined lending processes, minimising turnaround times, and enhancing onboarding experiences using India Stack’s APIs,” he added. 

These companies capitalised on AI to gather unstructured data for informed credit decisions, collaborating with entities like account aggregators and credit bureaus. They utilised digital collection data for customised payment schedules, incorporating early warning systems for post-disbursal loan monitoring. However, as fintech evolves, regulatory compliance and ethical considerations gain prominence.

He further explained that how regulators, recognising fintech’s potential, balance accessibility with risks and have intervened to ensure security, privacy, and consumer protection. The RBI’s Payments Vision 2025 and interoperability framework have propelled India towards becoming a global payment hub. Initiatives like the Payments Infrastructure Development Fund and innovations by the National Payments Corporation of India support rural digital payment infrastructure.

“As the fintech landscape evolves, emerging trends indicate a supportive regulatory landscape, continued digital adoption for financial inclusion, product hyper-personalisation, customer-centric fintech business models, embedded finance integrating non-financial services, and increased insurance penetration in tier 2 and 3 cities, “ concluded Vora. These trends reflect the ongoing evolution and impact of fintech in reshaping India’s financial landscape.

Read more: How Deutsche Bank is Riding the Generative AI Wave