GenAI: Top 5 Use Cases in Investment Banking

Authored by Hema Gandhi, CEO LatentBridge

Generative AI, or GenAI, has become a hot topic in the financial services industry, captivating the attention of banking institutions seeking transformative technologies. It's reshaping the landscape of capital markets in ways we've never seen before.

In this article, I will explore five important use cases of GenAI in investment banking.

Compliance Assistant for Operations Team

The operations team can benefit from GenAI's assistance in compliance efforts. By analysing historical data and external sources, GenAI offers recommendations for KYC and AML policies. It gathers relevant information from sources like KYC/AML events, risk registers, legal cases, and third-party services. It also automates the creation of regulatory and risk disclaimers, ensuring compliance with policies and reducing conduct risks.

Benefits: Mitigating conduct risk, preventing misselling, and providing accurate information in line with compliance guidelines.

Credit Assistant for Credit Officer

The Credit Assistant powered by GenAI empowers Credit Officers to make faster and more data-driven credit decisions. It provides insights for credit scoring and affordability questions by analysing previous credit approval and rejection cases. This helps Credit Officers assess credit risk more effectively and improves the accuracy of calculations involving the probability of default, leading to better-informed lending decisions.

Benefits: Enhanced credit risk assessment, improved accuracy in default probability calculations, and reduced credit-related losses.

Trader Assistant

GenAI acts as a valuable Trader Assistant, helping traders control risks, access real-time position data, and run simulations. By processing natural language queries on BASEL IV policy documents, GenAI provides accurate responses regarding positions, exposure limits, and stress test scenarios. Traders can make informed decisions and proactively respond to market changes.

Benefits: Enabling traders to make informed decisions, respond proactively to market changes, and enhance trading performance.

Collateral Analyst Assistant

The Collateral Analyst Assistant utilises GenAI to provide prompt and precise answers to questions about collateral. By analysing historical data and studying substitution and undercollateralization scenarios, it improves funding and inventory management procedures. The AI-powered assistant responds to inquiries on haircut rates, limits, margins, and funding across product exposure and booking systems, enhancing collateral analysis and decision-making.

Benefits: optimised collateral analysis, reduced funding risks, and improved inventory management efficiency.

Middle Office/CCRO Assistant

GenAI plays a vital role in streamlining operational effectiveness and risk management in the middle office. It offers contextually accurate answers to policy questions, risk checklists, reconciliation problems, and risk-pricing scenario checklists for complex products. The assistant improves decision-making processes and facilitates compliance by leveraging its understanding of rules and financial instruments.

Benefits: Enhanced operational efficiency, improved risk management practises, and facilitated compliance in the middle office.

Navigating potential risks 

However, the capital markets industry faces a significant dilemma when it comes to adopting cloud-native solutions due to concerns surrounding data privacy and the risks associated with handling confidential information.

On the one hand, cloud-native solutions offer numerous benefits, some of which i've talked about above. On the other hand, the sensitive nature of data, including personal and financial information, raises valid concerns about data privacy and security breaches.

To maintain customers' trust and protect sensitive data, the industry must navigate the complex regulatory landscape and ensure compliance with data protection laws. Because it involves relying on outside providers and sharing infrastructure with other businesses, storing sensitive data in the cloud introduces potential vulnerabilities. To reduce the risks associated with unauthorised access, data breaches, and potential leaks, private cloud services with encryption, access controls, and data isolation helps.

Striking a balance between leveraging the benefits of cloud-native solutions and addressing data privacy concerns will be critical as we move towards a world heavily influenced by AI.

Conclusion

 The banking sector is undergoing a profound transformation, fuelled by the revolutionary capabilities of GenAI. While the use cases highlighted above unveil the immense potential of AI-powered assistants in various banking tasks, such as middle office functions, compliance, trading, and collateral analysis, the industry is not without its challenges. Many Financial services institutions have been forthcoming in developing prototypes with a secured sandbox environment and once proven, will be able to double down their investment.  Adopting GenAI technology is poised to become a critical differentiator for financial institutions in an evolving industry landscape.

Note: this article is shared with the permission of the author.