The financial services industry has traditionally lagged behind other sectors in adopting new technology. Generative AI – the new tech kid on the block – might just change that. According to a 2024 EY survey, nearly 80% of financial institutions surveyed have used GenAI tech for at least one function or plan to do so over the next 12 months. This is hardly surprising given the anticipated impact this tech can have on the industry – an annual gain of $200 to $340 billion according to McKinsey. This additional revenue comes mainly from AI-driven improvements in productivity. But can GenAI have a similar impact on portfolio risk monitoring and regulatory compliance? As this blog explores, it’s not a question of if, but when and how.
Key GenAI LLM applications and their benefits in risk monitoring and regulatory compliance
Managing portfolio risks effectively and operating within regulatory boundaries are non-negotiables for financial institutions. Without these in place, the banking sector would be like the Wild West. However, these tasks are complex manual activities that happen only periodically. And when they do, they take up a lot of time and resources. GenAI-driven Large Language Models (LLMs) with their ability to swiftly process vast amounts of data can supercharge these processes, making them more efficient, effective, and continuous.
Here are a few key GenAI applications that do just that:
Data integration
An average adult can read about 250 words per minute. In contrast, LLM models can process thousands of data points in seconds. This ability holds tremendous promise for risk managers who handle corporate portfolios that contain hundreds, if not thousands, of businesses. To create a complete risk profile for these businesses, managers must continuously monitor multiple financial and non-financial data sources to identify and rank potential portfolio risks. The sheer number and diverse nature of these data sources make it a challenge for managers to remain up to date. It's a task that becomes even more laborious and error-prone if he or she uses spreadsheets to keep tabs on all this information.
Benefits: An LLM’s capacity to speedily collect and integrate disparate data sources can reduce a risk team’s research time by over 70%, ensuring a dramatic jump in productivity.
Text summarization and entity extraction
It’s one thing to collect information, but quite another to go through it all. Thankfully, GenAI models can do both. It can extract insights from vast amounts of data and automatically create high-level overviews or summaries of this information. This ensures that risk managers remain informed about essential events without getting bogged down by inconsequential details. More importantly, GenAI can be trained to identify and categorize key entities such as companies, individuals, regulatory groups, and other domain-specific data points. It can also map relationships between these entities, helping to uncover hidden connections, dependencies, key regulatory requirements, legislation changes, policy updates, and potential risks.
Benefits: With its ability to summarize text and identify key information, GenAI ensures that all critical risk data points are identified and analyzed. In addition, risk managers get faster access to essential information, leading to more timely and informed decision-making, better fraud detection, as well as accurate and detailed documentation for regulatory reporting.
Sentiment analysis
In today’s connected business ecosystem, sentiments matter. Take the example of Silicon Valley Bank’s failure. Negative sentiment towards the bank played a starring role in triggering its collapse. While it would be a monumental task for a bank to keep tabs on public and investor sentiment toward each and every business in its corporate lending portfolio, GenAI models can do this easily. Such platforms can mine the opinions expressed in news articles, regulatory reports, social media posts, customer reviews, internal documents, and online discussion forums to create a sentiment index that acts as a real-time indicator of company risk. Such data can also be incorporated into existing risk models, an approach that has proven to enhance predictive accuracy greatly.
Benefits: Unstructured data sentiment often acts as a precursor of risks and can be used to build proactive Early Warning Systems and credit-decisioning models. According to McKinsey, doing so can lead to a 20% to 40% decrease in credit-loss rates. GenAI allows risk managers to leverage this by continuously monitoring for negative sentiment trends that could indicate financial instability. They can also use it to align their investment strategies with market sentiment to maximize returns.
How TRaiCE leverages GenAI technology for more efficient risk monitoring
In portfolio risk monitoring, data is gold dust – it’s every risk manager’s weapon in ensuring portfolio profitability, ranking risks accurately, and forming effective mitigation strategies. Accordingly, that is the crux of our platform. While traditional risk monitoring systems focus only on financial data, we go one step further. By combining LLMs and NLP-driven algorithms, we help risk managers incorporate non-financial, regulatory, legal, and other forms of alternate data into their risk calculations.
This data inclusivity ensures holistic risk analysis that paints a more accurate and current picture of a borrowing company’s creditworthiness. To do this, we provide our customers with two risk indices – The TRaiCE Early Warning Risk Index (EWR) and the Business Sentiment Index (BSI). The former incorporates financial and nonfinancial data into its risk calculations and the latter assesses a company’s creditworthiness based on unstructured digital data.
Apart from this, our automated processes guarantee that the risk monitoring process is replicated daily, giving risk managers ongoing monitoring, reporting, and early detection of emerging risks and trends. This way, they never miss any changes to a company’s risk status, no matter how big or small the change.
TRaiCE is a no-code solution. It has a user-friendly dashboard that ranks companies in your portfolio by risk for easy surveillance. Think of it as an automated assistant that does all the hard work of gathering risk data, analyzing it, obtaining insights from it, and then presenting the results in an easy-to-understand and explainable manner.
Of course, GenAI is not without its constraints. We have worked hard to build a platform that balances the risks and rewards of the tech. You can read more on how we accomplish that here.
Conclusion
Leveraging GenAI enables us to rapidly scale and surpass the constraints of manual processing. This leads to more efficient and precise portfolio risk assessments, richer insights, and a more comprehensive understanding of a borrower's creditworthiness. If you would like to know more about our award-winning risk monitoring platform, reach out to us at info@traice.io or schedule a demo to see TRaiCE in action today.
Comments