Overview
A mid-sized property and casualty insurer was losing market share to competitors who could quote policies faster while maintaining profitability. Their traditional underwriting process relied heavily on manual review and outdated actuarial tables that couldn't incorporate modern data sources.
Koyal Tech Solutions developed a machine learning-powered underwriting platform that could assess risk more accurately while dramatically reducing decision time.
The Challenge
- Slow Quote Turnaround
Commercial policies took 5-7 days to quote, causing prospects to purchase from faster competitors.
- Inconsistent Pricing
Different underwriters priced similar risks differently, leading to adverse selection and unpredictable loss experience.
- Limited Data Utilization
Vast amounts of third-party data were available but not being leveraged in underwriting decisions.
- Regulatory Compliance
Models needed to be explainable and compliant with insurance regulations across multiple states.
Our Solution
Unified Data Platform
Built a comprehensive data platform integrating internal policy/claims data with external sources including property records, weather data, credit scores, and business financial information.
Gradient Boosting Models
Developed ensemble ML models using XGBoost for risk scoring, trained on 10 years of historical data with careful attention to avoiding bias and ensuring regulatory compliance.
Dynamic Pricing Engine
Created a real-time pricing engine that combines ML risk scores with business rules, competitive positioning, and profit targets to generate optimal quotes.
Explainable AI Dashboard
Built an underwriter workbench that shows model predictions with explanations, allowing humans to understand and override decisions when appropriate.
Results & Impact
35% Better Accuracy
Risk predictions more closely matched actual loss experience, improving portfolio profitability.
50% Faster Quotes
Most commercial policies now quoted within 24 hours, with many small businesses receiving instant quotes.
$4.5M Loss Improvement
Better risk selection and pricing led to measurable improvement in loss ratios within the first year.
25% Productivity Gain
Underwriters could handle more submissions while focusing their expertise on complex risks.
"The predictive models have given our underwriters superpowers. They can now make faster, more confident decisions backed by data. Our loss ratio has improved while we've grown the book."
