Data science is reshaping Life & Health insurance, but generating insights alone is not the same as delivering value. While modern tools and AI can produce output quickly, the real challenge lies in translating data into decisions that make a measurable difference.
Effective data science combines technical expertise with critical thinking and a deep understanding of business needs. When working with a reinsurance partner, asking the right questions is essential to ensure insights are not only technically robust, but also relevant, practical, and aligned with strategic objectives.
Here are five questions to guide the conversation:
A strong data science team brings together advanced analytical expertise, deep insurance knowledge, and a global perspective. This includes multidisciplinary capabilities across a broad spectrum such as actuarial science, finance, psychology combined with a clear understanding of geographic market dynamics and product-specific considerations. This enables teams to approach problems from different perspectives, recognizing not only what the data shows, but why those patterns arise within specific contexts.
Beyond technical capability, leading teams apply structured thinking across the model lifecycle, from business problem formulation to data analysis, model development, validation, and implementation. Equally important is the ability to engage with clients, ask the right questions, and translate analytical outputs into practical recommendations tailored to business needs.
Data science should ultimately enhance how insurers price, select, and manage risk. Achieving this requires a collaborative approach grounded in a clear understanding of business objectives, whether improving underwriting efficiency, optimizing pricing and reserving, or strengthening claims management.
By combining modelling expertise with business insight, data scientists can develop robust, tailored solutions. Identifying key risk drivers and translating them into practical outputs such as scores, tools, or decision frameworks enables data science to be embedded into day-to-day workflows, supporting more informed and consistent decision-making.
For example, a data science approach can be applied to the underwriting process that assesses the risk profile of each life insurance application. By leveraging domain knowledge and historical data to identify patterns linked to outcomes such as approval likelihood, or risk classification, low-risk cases can be fast-tracked while higher-risk submissions are flagged for additional review. In this way, a strong data science team can transform raw data into actionable signals that support faster, more consistent, and more efficient underwriting decisions.
The validity of insights depends on both the quality and relevance of the underlying data, as well as a clear and disciplined understanding of business needs.
Ensuring reliability requires a rigorous approach, including assessing data quality, reviewing assumptions, validating results against experience, and testing methodologies. Output should be interpretable and explainable to be effectively used in decision-making. For example, in a policyholder persistency analysis, it is important that policy durations, lapse events, and exposure measures are accurately captured and consistently defined, as inconsistencies can affect conclusions. Validating results against observed experience across cohorts helps confirm that the model is capturing real patterns rather than artefacts of the data.
Relevance comes from working closely with stakeholders to ensure alignment with operational goals and that the analysis is meaningful in a real-world context. In a persistency setting, this means focusing on actionable business questions, such as identifying segments with higher lapse risk or understanding the drivers of lapse, so that results can directly inform retention strategies, product design, or policyholder outreach.
A valuable data science partner should be able to review your existing models and processes objectively, combining technical expertise with business know-how and perspectives gained across markets.
This includes evaluating whether current methods produce reliable and meaningful results by assessing data quality, assumptions, methodology, and model performance, and whether outputs align with business objectives. For example, a review of a client’s underwriting model involves assessing whether key risk drivers are appropriately captured, evaluating model stability across time or segments, and identifying potential biases or gaps in performance.
Benchmarking against alternatives, such as industry best practices, helps identify where approaches are performing well and where improvements may be needed. The goal is to determine whether current methods are fit for purpose, aligned with strategic priorities, and delivering value with an appropriate level of transparency and control.
Data science continues to evolve rapidly, with new techniques and data sources emerging regularly. A disciplined approach is essential to ensure innovation delivers real value rather than responding to short-term trends.
This includes exploring and testing new methods to assess their stability, fairness, and practical applicability before adoption. It is also important to ensure that any approach is transparent and well understood, so it can be effectively integrated into existing processes.
At the same time, safeguards must be applied to ensure compliance with internal standards and regulatory requirements. This is becoming increasingly important as regulators place greater emphasis on transparency, explainability, and traceability in the use of AI and advanced analytics. Insurers need to be able to understand, document, and justify how models are developed, validated and used in decision-making. This extends to the use of external data sources, which must be carefully assessed for relevance, quality, and suitability for the intended purpose. A balanced and knowledgeable approach allows insurers to benefit from innovation while maintaining control over risk and supporting regulatory compliance.
To learn more about how PartnerRe’s data science capabilities can support your business, please contact us. We would be pleased to discuss your specific challenges and explore how data-driven insights can enhance decision-making.
Caroline John-Habib, Senior Analytics Actuary, Life & Health, PartnerRe