Description
Projects: Lifesys Product Launch, data anonymization, Combined Billing Automation, Fraud Detection, Real-Time Policy Automation in Salesforce
- Created a Customer Lifetime Value model to evaluate the long-term profitability of policyholders acquired through different sales channels, agents, and offices, allowing marketing teams to optimize their budget allocation.
- Deployed proactive retention models using predictive modelling in Databricks for annuity products, leveraging customer data & behavioral insights to identify customers at risk of non-renewal/lapse, improving policy renewal rates by 8%
- Performed A/B testing for insurance marketing campaigns to optimize conversion rates & reduce CAC, using statistical tests (Levene t-test, Welch, & Mann-Whitney U) to evaluate and recommend future strategies.
- Pioneered data-driven churn prediction strategies using Tableau, SQL & Python, reducing policy lapses by 12%, commended for elevating customer retention, fueling revenue, and product recommendations.
- Developed, fine-tuned, & audited a random forest classifier trained on 1 million claims data (CPT) using Python to identify potential fraud & suspicious activities, which led to a 17% improvement in carrier’s loss ratio.
- Optimized insurance KPIs, (e.g., Claims Processing Time, Lapse Ratio, Renewal Rate, CLV), leveraging expertise in new business, claims, commission, billing, and collaboration with actuaries, underwriters, agents, and other stakeholders.
- Conducted stakeholder interviews to gather comprehensive business requirements, creating Business requirement documents, use cases using JIRA, and data maps to facilitate the development of new products.
