Vacatures in het Actuariaat

What’s in the box? Useful findings on black box models

Bron: Laurant Dupont, on The Actuary - 15 december 2021

Machine learning (ML) models are used across the financial industry for a variety of purposes. To take two examples from the insurance sector, they can be used to replace traditional pricing approaches – using artificial intelligence (AI) to generate premiums, instead of traditional generalised linear models – or as a decision support tool to direct claims handlers towards cases featuring higher levels of complexity. However, these tools are often perceived as ‘black boxes’.

This summer, the ACPR (the French supervisory authority for the banking and insurance sector) held its first TechSprint. The challenge: generating explanations to understand the behaviour of credit risk predictive models based on AI.

Machine learning, artificial intelligence and regulation

Explainability is critical for building trust in processes based on ML and AI more generally, as it facilitates the validation of an algorithm, comprehension of its output, and its monitoring over the course of its lifecycle. Explainability is also a key factor in firms’ selection of appropriate techniques – it empowers boards and senior management to review and sign off models, and ensures AI-driven decisions are transparent enough to explain to customers.

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