Assistant Manager, Insurance Risk Business Analytics
Prudential Services Asia
Date: 3 weeks ago
City: Kuala Lumpur
Contract type: Full time

Prudential’s purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people’s career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed.
Organisation Context: Aligned with Prudential’s ambition to be a data-led organization, Group Risk is investing its insurance risk analytics capabilities. Group Risk has already built an analytic tool (Data Insights from Claims Experience or ‘DICE’) that provides standardised, real-time, end-to-end view of a health portfolio that links data across the policy value chain. DICE helps management to systematically identify where shareholder value is lost, when statistically credible anomalies evolve, how profitability of different dimensions evolves, and assess product redesign to ‘surgically’ adjust benefits & limits that are losing shareholder value. The tool is deployed across the Group, whilst also being expanded to encompass non-health claims and persistency experience analytics, actuarial experience investigations and potentially the internal basis for pricing incidence rate assumption generation.
Role Purpose: The role will support the deployment of DICE. Whilst this will be under guidance of the DICE architect, it will involve a high degree of autonomy and self-direction. The role will also contribute to deepening the maturity of DICE leveraging market-leading techniques in actuarial science, data science, and econometrics.
Key Accountabilities
Organisation Context: Aligned with Prudential’s ambition to be a data-led organization, Group Risk is investing its insurance risk analytics capabilities. Group Risk has already built an analytic tool (Data Insights from Claims Experience or ‘DICE’) that provides standardised, real-time, end-to-end view of a health portfolio that links data across the policy value chain. DICE helps management to systematically identify where shareholder value is lost, when statistically credible anomalies evolve, how profitability of different dimensions evolves, and assess product redesign to ‘surgically’ adjust benefits & limits that are losing shareholder value. The tool is deployed across the Group, whilst also being expanded to encompass non-health claims and persistency experience analytics, actuarial experience investigations and potentially the internal basis for pricing incidence rate assumption generation.
Role Purpose: The role will support the deployment of DICE. Whilst this will be under guidance of the DICE architect, it will involve a high degree of autonomy and self-direction. The role will also contribute to deepening the maturity of DICE leveraging market-leading techniques in actuarial science, data science, and econometrics.
Key Accountabilities
- Support the rollout of DICE across markets, including data processing, modelling, and visualization using SQL, Python and PowerBI. Quickly acquire domain knowledge for each market, around data structure, product features, underwriting, distribution, claims management, provider networks and actuarial methodologies.
- Expand tool capabilities by identifying and developing new techniques that provides data-driven insights in health claims, provider management, distribution, and persistency analytics. Rigorously validate existing and emerging methodologies.
- Ideation and generation of insightful analytics to support insurance risk deep dives
- Minimum of 5 years of experience in actuarial experience studies and/or analytics related to health claims, biometric risks, and persistency.
- Strong analytical and problem-solving skills.
- Familiarity with insurance products and governance, and understanding of the insurance risk value chain.
- Proficiency in Python, SQL and PowerBI
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