The Future of AI in Risk Management for Financial Institutions
✍️ Opinions
This article has been contributed by Rangarajan Vasudevan, Chief Data Officer, Lentra.
India's financial sector, the driving force behind the nation's economic progress, is dancing on a knife’s edge. Increasing regulatory scrutiny, geopolitical tensions, a volatile global economy, and the ever-looming specter of cyberattacks intertwine to form a complex and constantly shifting risk landscape. The gravity of this scenario is starkly illustrated by the fact that fraud in banking operations alone surged tenfold in 2021-22 compared to a decade ago, reaching an alarming INR 45,598 crore, as reported by the RBI.
Effective risk management is the cornerstone of financial stability. It entails proactively identifying, assessing, and mitigating potential economic losses. Indian financial institutions (FIs) face a multitude of risks. Credit risk, a persistent concern, is exacerbated by the growth of the microfinance sector and the rising non-performing assets in Indian banks. Market risk, brought on by fluctuations in interest rates and stock prices, presents another challenge, as seen in the recent volatility in the Indian market. Operational risk, arising from internal failures like human error, technology glitches, or cyberattacks, is also a growing concern, especially in the digital age.
The Reserve Bank of India (RBI) emphasises the need for strong cybersecurity measures to address this threat. In fact, the apex bank recently started placing limitations on various lenders citing concerns related to IT infrastructure and information security practices. Therefore, a failure to adhere to evolving regulatory standards necessitates continuous adaptation by FIs.
Limitations of Legacy Systems
Enter Artificial Intelligence
How AI Can Revolutionise Risk Management
Hurdles to AI Implementation
The Future of Risk Management
Limitations of Legacy Systems
Traditionally, risk management in Indian FIs heavily relied on manual processes and historical data analysis. While this approach was sufficient in a less volatile environment, its inadequacies are now glaring. An ever-changing regulatory landscape in India, with new laws and regulations being introduced regularly, plus the growing interconnectedness of the global financial system, with transactions and investments spanning multiple countries, has added another layer of complexity.
In addition, the unpredictable nature of natural disasters and pandemics, which can have far-reaching economic implications, further complicates risk management. Legacy systems cannot keep pace with the dynamic nature of risk today.
Nadeesh Kareemdathil
Enter Artificial Intelligence
The limitations of traditional risk management have created fertile ground for AI's transformative power. AI, alongside automation and cloud technologies, is poised to accelerate digital transformation in financial services. AI's secret weapon? Its ability to sift through vast amounts of data from unconventional sources. Financial statements, market trends, social media sentiment, and even weather patterns all contribute to AI's analytical capabilities. This allows for a far more comprehensive and nuanced understanding of risk, identifying potential threats that human analysts, reliant solely on historical data, might miss.
Recent advancements in generative AI further emphasise this urgency. According to EY, modernising core functions and platforms is a top priority for banks aiming to expedite digital transformation, with 58% focusing on this area. Furthermore, 78% of Chief Risk Officers (CROs) prioritise AI implementation—a sign of the industry's growing appetite for this technology.
The benefits extend far beyond individual institutions. A joint study by National Business Research Institute and Narrative Science reveals that 32% of Indian financial service providers already leverage AI for tasks like voice recognition and predictive analytics. Major banks in India are actively employing AI to streamline operations, and a report by Accenture indicates that 83% of Indian bankers believe AI will collaborate with humans in the near future. JP Morgan Chase has developed the Contract Intelligence (COiN) platform, which can analyse legal documents in seconds, extracting key data points – a task that would take humans hundreds of thousands of hours. Not only is AI faster, but it is also demonstrably less prone to errors.
AI's reach is not limited to financial institutions. Regulatory bodies like the Reserve Bank of India can leverage AI to identify systemic risks within the Indian economic system. Real-time risk identification empowers regulators to take preventive measures, like adjusting interest rates, to enhance financial stability.
How AI Can Revolutionise Risk Management
AI's impact on financial risk management goes beyond static risk estimation. By analysing historical data, AI can recommend dynamic portfolio diversification, proactively identify emerging threats, and adjust allocations to mitigate market risk. It can even simulate various economic and market scenarios to stress-test loan portfolios, helping institutions develop more resilient lending policies.
Unlike traditional, static policies based on limited factors, AI and ML models can analyse every possible combination of variables, creating a powerful tool for credit managers. This allows them to simulate different policy settings and see the predicted impact on loan approvals. This data-driven approach empowers them to optimise conversion rates while minimising risk.
The benefits extend beyond credit risk. Enterprises are leveraging generative AI as a virtual regulatory and policy expert. Trained on vast datasets of regulations, company policies, and guidelines, it can answer questions, identify compliance gaps in code, and automate regulatory checks – even providing alerts for potential breaches.
However, with these advancements come policy considerations. Financial institutions must ensure the transparency of AI models' decision-making processes to comply with regulations and maintain trust. Additionally, robust data governance practices are crucial to ensure the quality and security of the extensive datasets that power these robust AI systems.
Hurdles to AI Implementation
The potential of AI in financial risk management is undeniable. However, we need to address some key challenges to fully unlock this potential. One challenge is the constant game of catch-up regulators face. AI is evolving rapidly, and regulations often struggle to keep pace. This creates uncertainty for financial institutions and discourages broader adoption of AI in risk management.
Another hurdle is the lack of standardised practices for developing and deploying AI systems. This inconsistency makes it difficult to ensure fairness, avoid bias, and most importantly, understand how AI reaches its conclusions. Without this transparency, trust is difficult to build.
The financial sector itself faces its own set of challenges. The cost of acquiring, implementing, and maintaining sophisticated AI systems can be significant, especially for smaller institutions. Additionally, the effectiveness of AI hinges on high-quality data. Fragmented datasets and data privacy concerns can create significant roadblocks for institutions looking to leverage AI for risk management.
The Future of Risk Management
Despite these challenges and the ever-increasing volume of data, which presents a challenge for human analysis, it also creates a unique opportunity for AI’s implementation in India's financial sector.
Collaboration is crucial. Industry and regulators must work together to establish clear frameworks for responsible AI development and use in risk management. These frameworks should prioritise best practices, data governance, and Explainable AI (XAI) – tools that help us understand how AI models reach conclusions. This fosters trust and ensures ethical implementation.
Despite AI's automation capabilities, human expertise remains essential for interpreting results, making final decisions, and ensuring ethical applications. Therefore, upskilling the workforce becomes critical. Financial institutions must invest in training existing employees and attracting talent with AI, data science, and risk management expertise. This fosters a culture of human-AI collaboration, where AI amplifies human expertise while human oversight ensures responsible AI use.
By embracing AI as a transformative tool in risk management, the Indian financial sector can navigate the complexities of modern finance with greater confidence. This shift paves the way for a more stable and secure financial future for all stakeholders – from individual depositors to credit-seeking businesses and the broader Indian economy.
Lakshya Singh
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