Insurance Solutions

Use Case

Policyholder Risk Profiling

Objective 

Classify each policyholder into low, medium or highrisk segments to enable personalized underwriting and proactive risk mitigation.

Instructions

Gather basic demographics: age, gender, marital status, city/region

Fetch employment details: occupation, annual income

Retrieve lifestyle indicators: smoking status, alcohol use

Pull policy metadata: policy type and start date (to calculate tenure)

Collect recent medical history and wellness scores

Outcome

A structured risk profile for every policyholder, including composite risk scores and underlying attribute breakdown.

Impact Compared to Traditional Approach

Speed: Profiles generated in minutes vs. days of manual data wrangling

Accuracy: Consistent, rulebased scoring replaces subjective underwriting judgments

Scalability: Thousands of profiles can be processed overnight, enabling realtime portfolio monitoring

Claims Fraud Detection

Objective

Automatically flag claims with a high likelihood of fraud to reduce investigation workload and financial losses.

Instructions

Retrieve all claims from the past 12 months

Fetch associated policyholder demographics and policy terms (sum insured, coverage dates)

Pull provider details: hospital/clinic names and locations

Extract claim events: submission and settlement timestamps

Gather past claim counts and grouppolicy affiliations per customer

Outcome

A ranked list of suspicious claims with fraudrisk scores and reason codes for each flag.

Impact Compared to Traditional Approach

Efficiency: Investigation team focuses only on top 10% of highrisk claims instead of reviewing every case

Effectiveness: Datadriven anomaly detection uncovers patterns human reviewers may miss

Cost Savings: Early flagging reduces payout on fraudulent claims by up to 30%

Claim Settlement Efficiency Monitoring

Objective 

Identify processing bottlenecks and delays in the claim lifecycle to optimize operations and improve customer satisfaction.

Instructions

Retrieve all claims submitted in the last 6 months

Extract key timestamps: submission, approval, settlement, and closure dates

Fetch policy details: type, insured amount, and duration

Pull provider and handlingagent information

Gather outcome data: approval status and rejection reasons

Collect policyholder demographics for segmentation

Outcome

A dashboard of turnaroundtime metrics by claim type, region, provider, and agent, plus a list of delayed cases with rootcause tags.

Impact Compared to Traditional Approach

Visibility: Realtime KPIs replace periodic, manual status reports

Responsiveness: Operations can address emerging bottlenecks within hours rather than weeks

Customer Experience: Faster resolutions drive measurable improvements in satisfaction scores

Agent Performance Analysis

Objective 

Evaluate insurance agents on sales, renewals and claim ratios to optimize assignments, training and incentives.

Instructions

Retrieve active agent roster with region/branch assignments

Fetch each agent’s policies sold, conversion rates and renewals over the past year

Pull customer profiles and claim frequencies per agent

Gather productcategory mappings for sold policies

Outcome

Agent scorecards with composite performance tiers (High, Medium, Low) and personalized improvement recommendations.

Impact Compared to Traditional Approach

Objectivity: Datadriven scorecards replace anecdotal performance reviews

Targeted Coaching: Identifies specific skill gaps for each agent, speeding up training ROI

Resource Optimization: Aligns top performers with highvalue segments and underperformers with mentorship programs

Policy Recommendation for New Leads

Objective 

Recommend the most suitable insurance products to new prospects, maximizing conversion rates and upsell potential.

Instructions

Extract each lead’s demographics: age, gender, location

Fetch declared income and employment details

Retrieve stated coverage preferences and budget constraints

Pull any past application or rejection history

Enrich lead location with regional segmentation data

Outcome

Personalized top3 policy recommendations per lead, ready for CRM integration and automated outreach.

Impact Compared to Traditional Approach

Relevance: Customers receive tailored offers instead of generic product lists

Conversion: Datadriven matching boosts conversion rates by up to 25%

Speed: Recommendations generated in seconds vs. manual broker analysis

Customer Lifetime Value (CLTV) Prediction

Objective 

Estimate each policyholder’s longterm revenue potential to guide retention strategies and upsell opportunities.

Instructions

Retrieve demographics and policy tenure for each customer

Fetch historical premium payments and renewal patterns

Pull claims frequency and amounts over the past three years

Extract customer interaction channels and satisfaction scores (e.g., NPS)

Gather any policy lapse or missedpayment flags

Outcome

Normalized CLTV scores segmented into High, Medium and Low tiers, with recommendations for each group.

Impact Compared to Traditional Approach

Precision: Predictive modeling outperforms ruleofthumb retention lists

ROI: Focused engagement with highvalue customers increases upsell revenue by 15–20%

Proactivity: Identifies atrisk customers before they lapse, reducing churn by up to 10%