48 Finance Monthly. Bank i ng & F i nanc i a l Se r v i ce s Streamlining regulatory change management In the banking context, risk management and regulatory compliance are closely aligned. Banking employees have to manually monitor updates of thousands of regulatory documents, which involves visiting the websites of regulatory authorities and sifting through countless policy documents. This process is not only extremely resource-intensive but also errorprone and overall ineffective. Machine learning can be used to automate a major part of the regulatory change management process. For example, Compliance. ai, a Silicon Valley startup founded in 2016, provides an ML-driven platform that helps banks keep up with regulatory changes. Using NLP and machine learning, the Compliance.ai platform automatically sources all relevant regulatory content from financial authorities, whitepapers, and news media. Importantly, when significant regulatory changes have taken place, the tool immediately alerts compliance officers. Bank of Marin, a commercial bank that primarily operates in the Bay Area and has over $2 billion in assets, turned to compliance.ai to streamline its regulatory change management processes. Now Bank of Marin employees has access to all relevant regulatory content in one place, which significantly simplifies regulatory change management. As reported by compliance.ai, the Bank of Marin got returns on its investments in a short time by decreasing the number of resources for compliance. Optimising stress testing After the 2008 financial market crash, financial authorities substantially strengthened reporting requirements to ensure that banks can withstand significant economic downturns. This is why financial institutions need to routinely define and report their solvency to stay compliant, and banks with $50 billion or more in assets have to have their risk management teams conduct stress tests. While undoubtedly important, these risk assessment processes often require hundreds of field experts and inordinate amounts of hours to complete. Moreover, finding relationships between various economic variables in relationship to banks’ performance is an increasingly complex and resource-intensive task. Machine learning can help compliance experts to identify which exact combinations of values can cause risks, increasing reporting accuracy and decreasing the time it takes to complete these tests. This is exactly why Citi, one of the world’s largest financial institutions that operate in 98 countries, joined forces with Symphony AyasdiAI to develop an ML-driven risk forecasting model. Citi’s ML-driven stress testing solution Prior tomachine learning adoption, Citi had a hard time passing the annual Comprehensive Capital Analysis and Review (CCAR)
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