To enhance the credit risk management of a prominent bank, Finoptics applied sophisticated updates to their existing models. Our focus was on refining the bank's credit risk assessment capabilities, particularly in measuring Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). We integrated advanced statistical techniques and machine learning to enrich these models. Additionally, by utilizing Tableau, we provided the bank with powerful visualization tools, facilitating improved decision-making and compliance. This strategic upgrade enabled more effective communication of complex credit risk insights, elevating the bank's risk management processes.
Finoptics' AML Model Validation Service is designed to ensure compliance with New York's Part 504 regulations, focusing on the effective functioning and regulatory adherence of banks' transaction monitoring systems. This service includes a thorough evaluation of the model's design, data integrity, and performance, along with a critical emphasis on replicating and executing the transaction monitoring system to meet NYDFS standards. Our approach guarantees that banks can confidently manage AML risks while complying with the specific requirements of New York's financial regulatory framework.
Our client, a major banking institution, required an automated solution for analyzing peer banks' quarterly earnings. We designed a Python-based program to extract and analyze financial data from these reports, utilizing machine learning for advanced analysis. This solution automated data scraping, extraction, and complex calculations, providing the client with immediate, comprehensive insights upon each new report's release. The outcome was a transformative leap in the client's ability to make informed, timely decisions, marked by increased accuracy and efficiency in their data analysis process.
Under U.S. regulations, banks operating in the U.S. are required to conduct annual tuning of their AML models. Finoptics addressed this for a client by analyzing transaction data to update thresholds, reflecting the bank's risk profile for optimized performance. We used both quantitative and qualitative analyses to recalibrate and enhance the AML scenarios and sanctions screening processes. Additionally, we developed a Python-based dashboard for the bank, providing comprehensive visualizations of transaction trends and model adjustments. This approach led to improved accuracy and efficiency in the bank's AML framework, aligning with regulatory standards and enhancing operational effectiveness.