Language, Learning and Liability: The AI/ML Trifecta
Working together, AI and ML tools offer banks regulatory benefits in three key areas: language, learning and liability.
Using what are known as natural language processing (NLP) and large language models (LLMs), banks can uncover common patterns in fraudulent transaction requests. In-depth analysis of fraudulent transaction data, meanwhile, can help financial organizations build robust learning models for ML algorithms capable of finding problems as early as possible.
Finally, the use of AI solutions to quickly contrast and compare large volumes of current and historical financial data can help banks reduce potential liability by meeting due diligence requirements.
Core Ways that AI/ML Can Help Financial Firms
It’s one thing to talk about potential AI/ML uses and another for banks to integrate these tools across everyday workflows. In practice, AI and ML can help financial firms better navigate compliance concerns in five core ways:
- Streamline know-your-customer and anti-money laundering frameworks. In financial services, collecting and analyzing the data required to verify customer identities and identify money laundering risk — known as know-your-customer and anti-money laundering frameworks — are integral parts of regulatory compliance. Doing so effectively, however, is both time- and resource-intensive. With generative AI tools, organizations can extract information from both structured and unstructured data sources to create a complete KYC/AML picture.
- Enhance exposure analysis. LLMs allow risk analysts to evaluate massive data sets on securities, investments, and other high-value transactions, in turn making it possible to quickly identify possible exposure points.
- Increase risk factor detection. Portfolio and trading book activity help drive financial ROI, but they also present revenue risk if vulnerabilities are missed or exploited. LLMs can monitor these actions in real time to detect potential risk factors.
- Reduce regulatory gaps. Firms can’t protect what they can’t see. ML algorithms can be trained to evaluate bank networks for possible regulatory gaps, while AI tools can suggest steps for remediation.
- Improve response to changing regulations. Banking regulations are constantly evolving. Consider the recent advancement of Basel III “endgame” proposals, which would require large banks to increase the amount of on-hand capital they hold, use standardized risk models for risk assessment, and account for unrealized gains and losses. NLP and LLM tools can help banks quickly analyze new requirements to ensure nothing is missed and that required actions are summarized.
Emerging regulatory standards and evolving fraud methods require a rapid response from banks to reduce risk and limit liability. AI and ML tools can help financial firms address internal risks, detect potential fraud, and stay ahead of changing compliance expectations.