AI for Finance

This paper explores the operational and compliance risks associated with critical End-User Computing tools such as spreadsheets, databases and scripts widely used in actuarial, finance and risk functions. It proposes a pragmatic, risk-based framework to transform undocumented “shadow tools” into documented and auditable assets, while showing how Generative AI can accelerate documentation and support regulatory compliance.
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TRAIL (TRusted AI Labs) is at the forefront of advancing Trusted AI. We are excited to embark on this journey towards building trustworthy and responsible AI solutions.

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How to assess credit risk models in a financially meaningful way ?

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This presentation discusses the rising use of machine learning, particularly tree-based models, in non-life insurance

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Cost of Explainability in AI: An Example with Credit Scoring Models
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A Reacfin|Dataiku E-book about Building a Standardized Data Analytics Workflows
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