Dynamic pricing and risk assessment for microinsurance using machine learning and telematics

Project Abstract

Dynamic pricing and risk assessment for microinsurance using machine learning and telematics investigates how advanced analytics and real-time data can transform affordability, accessibility, and resilience in low-income populations. This study develops and evaluates a data-driven framework that integrates telematics-derived risk signals with conventional demographic and behavioral features to estimate individual risk and tailor premium pricing for microinsurance products. The research employs a multi-stage methodology (1) data aggregation from telematics devices, mobile telemetry, weather dashboards, health indicators, and historical claims; (2) feature engineering to capture exposure, persistence, seasonality, fatigue effects, and adverse selection; (3) development of machine learning models—ranging from gradient boosting, random forests, and neural networks to probabilistic survival and time-to-event approaches—to predict claim likelihood, frequency, and severity; (4) design of dynamic pricing algorithms that adjust premiums and coverage limits in near real-time while meeting regulatory constraints and fairness criteria; and (5) a risk-scoring system that translates model outputs into actionable underwriting decisions and customer segmentation for targeted product offers. The abstract also presents an optimization layer that balances profitability with social value, using constraints such as minimal viable margins, maximum premium affordability, and risk-based tiered coverage. The dataset comprises anonymized microinsurance portfolios across diverse geographic settings, including rural and peri-urban communities, enabling cross-market validation and transferability analysis. Evaluation focuses on predictive performance (AUC, calibration, and Brier scores), pricing accuracy, and stress tests under scenarios of data sparsity and telematics noise. The study also incorporates explainability mechanisms, employing SHAP values and local interpretable model-agnostic explanations to ensure transparency for underwriters and beneficiaries. A key contribution is the formulation of a dynamic pricing policy that respects regulatory boundaries, avoids price discrimination, and preserves consumer trust by communicating rationale and protection gaps clearly. The findings demonstrate that incorporating telematics data improves risk discrimination by capturing exposure variability and behavioral patterns that conventional models overlook, leading to more accurate premium signals and reduced incidence of underinsurance. The framework also reveals operational gains from automation in underwriting workflows, faster time-to-quote processes, and resilience against fraud through anomaly detection and provenance tracking. The research addresses ethical considerations, data privacy, and governance by implementing privacy-preserving analytics, consent management, and secure data pipelines. Policy implications are discussed, highlighting potential impacts on inclusion, competitive dynamics in microinsurance markets, and the alignment of pricing practices with social protection objectives. Overall, the work provides a scalable blueprint for deploying machine learning-enhanced microinsurance pricing and risk assessment, offering practitioners a validated approach to expanding coverage, improving risk selection, and sustaining product viability in resource-constrained settings.

Project Overview

What This Project Is About

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A straightforward study that explores how pricing can be adjusted to reflect risk in microinsurance, using machine learning tools and data from devices (telematics) to better estimate what an individual policy should cost and how risky they are to insure. The project looks at small-scale insurance products often used in developing regions and how technology can make pricing fairer and more accurate.

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The Problem It Addresses

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Many microinsurance plans use broad rules that don’t reflect individual differences, leading to unfair pricing or uncovered risk. Limited data, manual processes, and the need for affordable coverage create gaps. This project investigates how to personalize premiums and assess risk more reliably while keeping costs low.

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Objectives of the Project

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1. Understand how telematics data (device-generated information) can inform risk assessment for microinsurance.
2. Explore machine learning methods to predict claims likelihood and appropriate premium levels.
3. Develop a simple pricing framework that balances fairness, profitability, and accessibility.
4. Evaluate potential privacy and ethical considerations in data use.

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What You Will Do Step by Step

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1) Review basic concepts of microinsurance, pricing, and risk; 2) Gather or simulate data including customer attributes and telematics indicators; 3) Build basic models to predict risk and set premiums; 4) Compare traditional pricing with ML-based pricing; 5) Assess feasibility, costs, and privacy implications; 6) Document findings and propose steps for real-world deployment.

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Expected Outcome

The project should deliver a simple, tested pricing approach that uses ML and telematics signals to adjust premiums for microinsurance, plus a concise report on feasibility, benefits, and limitations for real-world use.