The Shift from Intuition to Evidence
Traditional risk selection in Indian commercial insurance has relied heavily on underwriter experience, broker relationships, and market positioning. While institutional knowledge remains valuable, it produces inconsistent results — two underwriters evaluating the same proposal may reach different conclusions based on their individual experience and risk appetite.
Data-driven risk selection replaces this inconsistency with evidence-based decision-making. By analysing historical loss data, external data sources, and real-time risk indicators, insurers can identify which risks are likely to be profitable and which are likely to produce adverse loss experience — before binding coverage.
Data Sources Available to Indian Underwriters
Indian underwriters have access to a growing array of structured data sources. Government databases include MCA filings (financial statements, director details, charges), GST return data (revenue proxies and business activity verification), and the eCourts portal (litigation history). IRDAI's Insurance Information Bureau provides claims frequency and severity benchmarks by industry and geography.
External commercial data sources include CIBIL business credit scores, Dun and Bradstreet business reports, and satellite imagery providers. Emerging data sources include IoT sensor feeds from manufacturing equipment, weather data from IMD, and supply chain risk indices. The challenge is not data availability — it is structuring these diverse inputs into a coherent scoring framework.
Building a Risk Selection Scoring Model
A practical scoring model for Indian commercial lines should incorporate four dimensions: financial risk (credit scores, leverage ratios, revenue trend), operational risk (industry hazard grade, compliance status, loss prevention measures), claims risk (historical loss frequency and severity, both own-book and IIB data), and accumulation risk (geographic concentration, catastrophe exposure).
Assign weights to each dimension based on actuarial analysis of your own portfolio's loss drivers. For example, if financial distress is the strongest predictor of claims in your manufacturing book, weight the financial risk dimension at 35-40%. Validate the model against three to five years of historical data before deployment, and recalibrate annually as new loss experience becomes available.
From Scoring to Decision Rules
A score alone is not useful unless it maps to clear decision rules. Define acceptance bands — for instance, scores above 75 qualify for automatic acceptance at standard rates, scores between 50 and 75 require senior underwriter review with potential loadings, and scores below 50 are declined or referred to the chief underwriter.
Build override controls that require documentation when an underwriter deviates from the model recommendation. Track override frequency and outcomes — if overrides consistently produce worse results than the model, the model is working and overrides should be reduced. If overrides consistently outperform, the model needs recalibration. This feedback loop is essential for continuous improvement.
Implementation Challenges in the Indian Market
Data quality remains the primary obstacle. Indian commercial insurance data is often trapped in PDF proposal forms, handwritten surveyor reports, and unstructured claims files. Before a scoring model can function, this data must be digitised, cleaned, and standardised — a prerequisite investment that many Indian insurers have deferred.
Organisational resistance is the second challenge. Experienced underwriters may perceive data-driven models as a threat to their autonomy. Address this by positioning the model as a decision-support tool that augments rather than replaces underwriter judgement. Involve senior underwriters in model design and calibration to build ownership and trust.
Measuring Impact and Iterating
Track the impact of data-driven risk selection through clear metrics: shift in loss ratio by scored segment, hit ratio improvements (ratio of quoted risks that bind), average premium adequacy, and portfolio mix changes. Compare the performance of model-selected risks against manually selected risks over equivalent exposure periods.
Expect the first version to be imperfect. The value lies in having a structured, measurable framework that can be improved iteratively. Review model performance quarterly, update weights based on emerging loss trends, and expand data inputs as new sources become available. Within two to three underwriting cycles, a well-maintained model should demonstrably outperform purely intuition-based selection.