GenAI for Risk and Compliance in Banking & Financial Services  

Remember the March turmoil from the collapse of Silicon Valley Bank (SVB)? It was a rude awakening for the global banking and financial services industry, stirring regulatory actions to tighten the interest, liquidity, and credit risk management practices. Large banks crimped their lending practices and were ordered to load up reserves to build resilience and cope with economic slumps without slipping into bankruptcy. In 2024, with GenAI instilled into the core of banking and financial operations, the domino effect of new regulations and seismic tech innovations will transform the risk and compliance in banking and financial firms.

Insights from IBM’s 2024 Global Banking and Financial Markets suggest that 8 in 10 financial institutions implement GenAI in at least one use case. The adoption rates are higher in customer engagement and risk and compliance in banking functions. The findings also revealed that 60% of GenAI decision-makers saw higher value across the same areas.

In the rest of this blog, let us explore GenAI as a value-added breakthrough that can change the course of risk and compliance in banking and financial institutions and keep up with recent regulatory obligations and other GRC objectives.

Magic is in the (GenAI) Models

According to Juniper Research’s Generative AI in Banking, banks spent $5.6 billion on GenAI in 2024, which is expected to reach $85.7 billion by 2023!

By GenAI, we are aware of the recent breakthroughs leveraging large learning models (LLMs), a category of GenAI trained to generate textual content. GenAI includes several model types that aid in creating content text such as music, code, images, and other outputs using large computational and storage resources. These models are trained on large datasets to execute tasks like a human brain would by learning, problem-solving, pattern identification, and decision-making.

In the banking and financial services industry, businesses need specific GenAI models trained to address contextually accurate and domain-specific tasks. Risk-controlled and highly centralized environments make regulatory compliance, process transparency, and interpretation paramount.

The right GenAI model that is compatible with the org structure helps unlock value. The opposite increases the risk of inaccurate or illogical information generation, issues in model biases and fairness, and inadequate transparency in model decisions and workflows.

C-suite leaders are tasked to uncover use cases where the GenAI models deliver the most for the business and simultaneously assess their firm’s readiness to adopt GenAI tools. The decision also includes choosing between building their proprietary models or using open-source GenAI models. And planning other priorities such as selecting data architecture, enterprise technology selection, and building software development, data engineering, MLOps engineering, and cybersecurity teams.

Centrally led Archetype to Scale GenAI Use Cases in Banking Risk & Compliance

Banks benefit the most from a centrally led GenAI operating model archetype. It helps teams, stakeholders, and organizational structures stay on top of GenAI approaches, changes, and decisions. Recent McKinsey research on some of the largest financial institutions with assets over $26 trillion in the U.S. and Europe found that 50% of them have a centrally led GenAI model even across their data and analytics.

The research delved into four common organizational archetypes for using GenAI to investigate the potential benefits and challenges. The results revealed that 70% of banks with highly centralized GenAI operating models have been able to move GenAI use cases into production.

For illustration, consider a pyramid-like structure. Using a bottom-up approach, let us understand GenAI applications across operational areas within risk and compliance in banking and financial services and customer support. The sections to the right in red represent areas in banking with the highest GenAI maturity. The grey section on the left represents areas that are testing the GenAI adoption in risk and compliance in banking functions:

• Regulatory Risk and Compliance Assessments: Banking and financial firms can train their GenAI to become experts in regulatory topics to answer user queries regarding evolving regulatory practices and policies. GenAI tools can help compliance teams stay on track with regulatory and operation procedures and leverage it as a code accelerator to flag codes for compliance gaps. It can also automate compliance checks and validation processes and set alerts to warn of potential breaches.
• Managing Cybersecurity Threats: GenAI tools can be leveraged for code generation, rule detection, and secure code development by using natural language processing. They can improve organizations’ defenses with simulation-based training on threats and attack scenarios. They can also double up as risk screening tools by analyzing security-related activities and trends to identify threat patterns and behavior anomalies.
• Flagging Banking Risks: GenAI can be scaled in the customer-facing side of banking to create risk ratings based on customers’ banking behaviors. It can also help analyze transactions to detect suspicious activities. GenAI also powers banking’s credit standards and lending decisions by unlocking insights from credit data to predict repayment default or loss probabilities using models.
• Climate Risks: With the growing importance of corporate sustainability practices and investments, banking and financial institutions can find GenAI useful for ESG reporting, monitoring their climate risk assessments, and visualizing physical risks with high-resolution maps. It can also help automate climate-related data collection and analysis to unlock actionable insights and make recommendations that help meet net-zero objectives.

C-suite Leaders Must Ensure GenAI Lives up to the Hype!

We’ve already examined artificial intelligence for GRC by understanding the capabilities in OpenPages with the Watson platform. In 2024, the agenda for C-suite leaders in risk and compliance in banking is to ensure that GenAI lives up to the hype in boosting productivity and efficiency. As GenAI use cases expand, it is also imperative for banking companies to adopt AI governance to balance value and innovation with compliance.

If your enterprise is looking for strategies and actions to reshape objectives across risk and compliance in banking for 2024, experts at iTech GRC will ensure that optimizing IBM OpenPages is a great start.

Contact our team to learn how your business can embark on a new journey of regulatory compliance with the artificial intelligence for GRC using the AI-enabled IBM OpenPages with Watson.