FORMATION IA
IA pour la Souscription et la Gestion des Sinistres en Assurance
Appliquer le machine learning à la notation du risque, la détection de fraude et l'automatisation des sinistres dans un cadre réglementaire.
Ce qu'elle couvre
Ce programme de niveau praticien dote les souscripteurs et les responsables sinistres des compétences nécessaires pour déployer des modèles ML de notation du risque, automatiser les flux documentaires et détecter les fraudes par des techniques de détection d'anomalies. Les participants travaillent sur de vrais jeux de données assurantiels, apprennent à évaluer l'équité des modèles, à interpréter les sorties d'explicabilité et à respecter les contraintes Solvabilité II et RGPD. Le format combine études de cas animées par un formateur, ateliers pratiques en Python et outils cloud, et un projet final sur des données sinistres anonymisées. À l'issue du programme, les équipes sont en mesure d'évaluer, piloter et gouverner des solutions IA dans les fonctions clés de souscription et de gestion des sinistres.
À l'issue, vous saurez
- Build and evaluate a gradient-boosting risk scoring model on structured insurance data using Python
- Design a fraud detection pipeline combining rule-based triggers and unsupervised anomaly detection
- Automate extraction of key fields from claims documents using an NLP pipeline
- Produce a SHAP-based model explanation report suitable for regulatory review under Solvency II
- Identify and mitigate fairness risks in a risk classification model before production deployment
Sujets abordés
- ML-based risk scoring models: gradient boosting, GLMs, and neural networks
- Fraud detection using anomaly detection and graph analytics on claims data
- Document automation: OCR, NLP extraction from policy and claims documents
- Model explainability (SHAP, LIME) for underwriting decision transparency
- Regulatory constraints: Solvency II, GDPR, and EU AI Act implications for insurance AI
- Fairness and bias auditing in risk classification models
- Feature engineering from telematics, IoT, and third-party data sources
- MLOps basics: monitoring model drift in production underwriting pipelines
Modalité
Delivered as a blended programme over 4-6 weeks, combining four live virtual instructor-led sessions (half-day each) with self-paced labs on a cloud sandbox environment pre-loaded with anonymised insurance datasets. Approximately 60% hands-on lab time and 40% instructor-led discussion and case studies. A final capstone project requires teams to present a working AI prototype and a governance memo. Materials include Jupyter notebooks, a regulatory compliance checklist, and a model card template aligned with EU AI Act requirements. In-person delivery at client site is available for groups of 10 or more.
Ce qui fait que ça marche
- Embedding a compliance review checkpoint at every model development stage, not just at deployment
- Involving actuarial and legal teams alongside data scientists from the project kick-off
- Running parallel scoring (AI model alongside existing process) for at least one quarter before full cutover
- Establishing clear ownership for model governance, including scheduled retraining triggers
Erreurs fréquentes
- Treating ML risk models as black boxes and failing to document explainability before submitting to regulators
- Using biased historical claims data without auditing for protected-characteristic proxies, creating discriminatory outcomes
- Skipping model drift monitoring after deployment, leading to silent degradation in risk scoring accuracy
- Automating fraud flags without a human-review escalation path, resulting in wrongful claim denials and complaints
Quand NE PAS suivre cette formation
This programme is not suitable for a team that has not yet digitised its core claims or policy data — if documents still live in paper files or unstructured legacy systems without any data pipeline, foundational data engineering work must come first before AI modelling has any traction.
Fournisseurs à considérer
Sources
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