ProtoGene Consulting Private Limited

From Complexity to Clarity

Optimization in Action

This case study examines how ProtoGene's innovative optimization engine transformed a leading life insurance company's sales strategy, resulting in significant profit margin increases and improved resource allocation. The solution not only addressed immediate challenges but also laid the groundwork for long-term strategic improvements.

In today's competitive business landscape, organizations face the ongoing challenge of efficiently managing and allocating their resources. This encompasses various aspects of operations, including:

Human capital
Machine capacity
Financial budgets
Inventory management

The ability to optimize these resources is crucial for maximizing profitability and maintaining a competitive edge in the market.

A prominent life insurance company, offers a diverse portfolio of products. The primary challenge revolved around maximising the profit margins (profit – commission) by balancing sales target mix, a critical factor in the sales and finance departments' strategy. In absence of optimal product sales mix, agents tends to focus on RT-001 sales only.

Key Objectives

Minimum 100 policies per month
At least 20% share for each product type
Current overall profit margin: 5.6%
Optimize sales targets for maximum profit

ProtoGene developed a sophisticated optimization engine designed to determine the most effective combination of sales targets while considering multiple business constraints. This innovative approach leveraged advanced algorithms and data analytics to provide actionable insights.

Results and Impact

The implementation of ProtoGene's optimization engine yielded impressive results:

Immediate Impact: A 3.4% increase in profit margins
Projected Outcome: An anticipated 11.11% increase in profit margins under ideal conditions.

Optimizing Motor Insurance Portfolios with ProtoGene's Claim Rating Framework

The motor insurance industry is navigating a challenging landscape characterized by rising claims, competitive pressures, and solvency risks. ProtoGene’s Claim Rating Framework offers a data-driven solution to optimize portfolio performance, reduce claims, and strengthen risk management. By leveraging advanced analytics and Data Mining tools, the framework has delivered measurable results, including:

30% reduction in Incidence Rate
14% reduction in Average Cost per Claim
56% reduction in Average Claim per Policy

This white paper outlines how ProtoGene’s framework empowers insurers to achieve sustainable growth while maintaining profitability.

Industry Challenges

Rising Claims Severity
Operational Inefficiencies
Solvency Pressures
Competitive Market Dynamics

“Traditional approaches focus on portfolio size to absorb losses, but this strategy is no longer sufficient. Insurers need advanced tools to predict and mitigate risks effectively.”

ProtoGene’s Claim Rating Framework

ProtoGene developed theClaim Rating Frameworkto address these challenges by quantifying risk at the time of acquisition and renewal. The framework usesGeneralized Linear Modelingand Data Mining tools to classify policies into risk grades, enabling insurers to:

Optimize Portfolio Mix: Identify and exclude high-risk customers.
Strengthen Underwriting: Implement risk-based pricing and reserve allocation.
Enhance Retention: Focus on retaining profitable customers.

Risk Grading:

Policies are categorized into eight risk grades based on parameters such as:

Vehicle-specific factors (sum insured, age, model, brand).
Geographic location and utility.
Customer relationship (new vs. existing, past performance).

Performance Metrics:

Incidence Rate: Percentage of claims received over the portfolio.
Average Claim per Policy: Average loss distributed across the portfolio.

Results and Impact

ProtoGene’s Claim Rating Framework has delivered significant improvements in portfolio performance:

Risk Segmentation:

Grades 7 and 8, representing only 11% of the portfolio, were identified as high-risk segments with severe loss ratios and higher claim counts.
Excluding these grades reduced claim settlement efforts and improved operational efficiency.

Financial Impact:

30% reduction in Incidence Rate: Fewer claims received across the portfolio.
14% reduction in Average Cost per Claim: Lower payouts for individual claims.
56% reduction in Average Claim per Policy: Improved profitability per policy.

Operational Efficiency:

Enhanced reserve allocation for high-risk accounts.
Streamlined acquisition and retention processes.

ProtoGene’s framework integrates four key components:

Risk Assessment:

Quantifies risk for each policy.

Customer Profiling:

Identifies profitable and high-risk customers.

Claims Analytics:

Analyzes historical claims data to predict future trends.

Portfolio Optimization:

Balances risk and profitability across the portfolio.