This case study centers on an AI model employed for predictive analytics in the financial services sector.
We were engaged by the client’s Risk Committee, tasked with ensuring the reliability of a machine learning model created and operated by a third-party service provider. This was in preparation for approving a proposal to collaborate with the said service provider in developing financial products for the client’s customer base.
Our objective was to aid the client’s due diligence process concerning the third-party service provider and its machine learning model. Our role involved a targeted assessment of specific processes and controls pertaining to the model’s design, development, and operation. This assessment aimed to help the client assess potential risks related to reputation, regulatory compliance, and operational aspects stemming from the use of the AI system and its outcomes. The findings of our evaluation were incorporated into the client’s new product approval process.
In executing this engagement, we applied internally developed procedures to scrutinize specific aspects of the service provider’s AI control framework. Our focus encompassed:
- Examination of data sources, data quality, and data management.
- Assessment of the modeling and development procedures.
- Evaluation of the model’s operation and the generation of its results.
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By FCCT Editorial Team
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