Harnessing the Potential of Generative AI in Financial Risk Management

Undoubtedly, Generative AI (Gen AI) has emerged as a transformative force. It’s comparable to other groundbreaking general-purpose technologies (GPTs) like the steam engine, electricity, semiconductors, and the internet, all of which have historically delivered substantial productivity enhancements.

These GPTs typically undergo a similar progression. Initially, there’s a productivity surge driven by a substitution effect. This is later outstripped by an augmentation phase, fueled by downstream innovations. Today, with Gen AI, we’re just at the beginning of this journey.

Currently, Gen AI is in the substitution phase, which often triggers the “Turing Trap” anxiety—an apprehension about machines replacing human jobs. This fear isn’t new; it echoes the anxiety that gripped the Luddites over 200 years ago. They destroyed machinery during the industrial revolution over concerns that such advancements would eliminate their jobs.

Despite these concerns, we’re already reaping productivity rewards. Intelligent automation is helping realize Adam Smith’s “division of labor” in a world where digital and human workers coexist. Digital agents are adept at handling tedious and repetitive tasks, freeing humans to engage in more complex activities requiring judgment, interpretation, and analysis.

To illustrate, consider the credit risk management practices of a large global bank. Typically, these banks establish a credit limit hierarchy with various exposure levels—such as counterparty, asset type, and jurisdiction. Managing the response to any breach of these limits, known as excess management, can become overwhelming. In a sizeable bank, thousands of breaches may occur daily, many of which—about 80%—are technical errors due to incorrect inputs or reconciliation mistakes.

Why is this significant? Treating every breach with equal urgency means skilled professionals spend undue time on non-critical issues. This inefficiency can even freeze credit lines unnecessarily, affecting business operations. Here, digital workers are exceptionally efficient. They can swiftly handle technical breaches by categorizing them and resolving those that don’t need human intervention, allowing human professionals to address more critical breaches effectively.

So far, Gen AI’s substitution benefits have already demonstrated considerable value. The upcoming augmentation phase promises even greater productivity gains, driven by continued innovation.

For financial institutions dealing with geopolitical, economic, and environmental uncertainties, maintaining consistent Economic Value Added (EVA) is a formidable challenge. This involves not only pursuing business growth (“offensive”) but also managing operational costs and risks (“defensive”).

The synergy of Gen AI and intelligent automation offers financial institutions a strategic advantage in tackling both goals. Gen AI aids in content analysis and generation across various forms, unearthing new insights and value. Simultaneously, intelligent automation enhances business process management and robotic process automation, enabling firms to act on these insights.

Yet, the financial risk management sector hasn’t fully tapped into Gen AI’s potential. Traditionally, banks have focused on defensive measures to meet Basel’s regulatory reporting requirements, emphasizing capital adequacy. Despite this, large language models (LLMs) and machine learning have already noticeably reduced risk management overheads through cost-effective simulations and regulatory analysis.

On the offensive front, however, Gen AI’s impact has been modest. Before the 2008 financial crisis, regulations aimed to converge “regulatory” and “economic” capital, encouraging risk metrics like Value-at-Risk (VaR) for cohesive risk management and business strategy alignment.

The financial crisis shifted this focus. Regulators stepped away from capital convergence and sought broader financial stability reforms. Meanwhile, newer finance models—like behavioral finance and mental accounting—gained traction, challenging outdated approaches like the modern portfolio theory (MPT).

Even when MPT principles remain valid, executing them reliably has always been challenging. The assumption that historical asset returns predict future distributions often proves inaccurate, hindered by non-stationarity, dimensionality, and flawed efficiency assumptions.

Here, Gen AI could revolutionize financial risk analytics, reshaping how risk teams collaborate with front-office operations for trading and portfolio optimization. Gen AI facilitates access to diverse and real-time data, extending beyond structured datasets to unstructured sources like market sentiment and social media patterns.

Looking forward, as risk managers integrate deep learning and reinforcement learning, their analyses become more predictive. This progress offers greater support to the front office, enhancing hedging strategies and portfolio models with synthetic data and deep hedging techniques.

Ultimately, this evolution empowers financial institutions to better manage improbable yet high-impact events, moving beyond typical market fluctuations. For a comprehensive discussion on leveraging Gen AI and intelligent automation, feel free to contact us.