Data Science for Smarter Risk Assessment
Enhancing underwriting precision and claims prediction with neural network–powered insights
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- End-to-End Predictive Modelling & Evaluation
- Deep Learning, Ensemble & Probabilistic Models
- Feature Engineering & Data Strategy
- Experimentation, Validation & A/B Testing
- Model Interpretability & Governance
- Model Deployment & MLOps Support
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Advanced Classification Models for Claims Risk Stratification
Leveraging neural networks to classify risk and reduce claim-related losses in personal insurance.
Client & Goal Overview
Client Type
National Insurance Provider (Personal & Vehicle Lines)
Challenge
The client struggled with increasingly unbalanced claims risk profiles across their customer base. Traditional rule-based scoring and logistic regression models failed to capture the complex patterns in behavioural and demographic data. The objective was to build a predictive risk classification system using machine learning to flag high-risk policyholders and claims before underwriting or disbursement.
Our Role
We developed, validated, and deployed an ensemble classification system based on deep learning, improving risk stratification while maintaining transparency. We also facilitated cross-functional understanding of model outputs and guided the integration of predictions into key decision workflows.
Project Journey & Results
Project Scope
- Consolidated historical policy, claims, and behavioural data across systems.
- Engineered over 300 feature,s including frequency of coverage changes, claim timing, and geospatial patterns.
- Designed and trained deep neural networks (DNN) with dropout layers and batch normalisation for robust performance.
- Benchmarked performance against traditional models (LogReg, Random Forests, XGBoost).
- Deployed model in a risk scoring API integrated with underwriting systems and claims triage workflows.
- Delivered an explainability layer using SHAP values and LIME for underwriter interpretation.
Technical Stack
- Data Pipeline: Apache Airflow + PostgreSQL
- Feature Engineering: pandas, geopy, tsfresh
- Modelling: TensorFlow, Keras, Scikit-learn
- Explainability: SHAP, LIME
- Deployment: FastAPI microservice hosted on Azure Kubernetes Service
- Monitoring: Prometheus + Grafana for model drift and inference latency
Results
- Increased accuracy of high-risk claim prediction by 27% over baseline models.
- Reduced average time to flag suspicious claims by 40%.
- Enabled risk-based pricing with 12% uplift in premium adjustment precision.
🟧 Reduced fraudulent claim payouts by €1.3M in the first 12 months.
🟧 Achieved F1-score of 0.87 on unseen data across multiple risk segments.
🟧 Established a reusable pipeline now being extended to home insurance products.
Ready to explore Data Science in your organisation?
✓ Discover how predictive models can drive smarter risk, pricing, and targeting decisions. ✓ Book a strategy consultation to assess data readiness and model ROI potential. Have a question or idea?
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