hr | bluebox helps turn data into actionable insights
Data and advanced analytics are transforming the life and health insurance market. Read more about how Hannover Re’s L&H Data Analytics team created hr | bluebox to help clients navigate this evolving landscape. hr | bluebox turns data into actionable insights, improving portfolio understanding and customer behaviour analysis.
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Background
Data touches every part of the insurance value chain. With digitalisation and advanced analytics, L&H insurers are unlocking the potential of vast amounts of data including:
- Product development: Health and wellness solutions use individual data to deliver personalised recommendations and outcomes.
- Distribution: Bancassurance partnerships leverage data to create new cross-selling and up-selling opportunities for CI policies.
- Underwriting: Advanced analytics improve STP rates, reducing manual underwriting and speeding up the customer journey.
Hannover Re’s L&H Data Analytics team created hr | bluebox to help cedants navigate this evolving landscape. hr | bluebox helps to turn data into actionable insights, improving portfolio understanding and customer behaviour analysis.
“Our service creates financial value through a bespoke approach. We offer tailored solutions that respect market individuality. Cedants receive actionable recommendations, supported by explainable AI to identify patterns, trends and drivers behind events. We also suggest prevention strategies.”
Solution and results
hr | bluebox is not just a machine learning tool. It is a project-driven analytics service that helps cedants uncover patterns and trends to make informed strategic decisions.
Example 1: Improving STP rates
In Canada, Hannover Re (Ireland) DAC Canadian Life Branch, and the hr | bluebox Data Analytics teams members collaborated with a local insurer to increase the straight through processing rate on their life insurance underwriting journey and reduce purchase friction.
Using analytical techniques, the project team identified policy segments with higher-than-average acceptance rates (and lower than-average decline rates) when these segments were manually underwritten and provided with standard underwriting terms. This meant no additional risk was identified during the manual underwriting process.
As a result, two new automated underwriting rules were introduced, that covered more than 10% of all manually underwritten applications. Further assessment confirmed these cases could safely follow the automated underwriting route without increasing risk.
The addition of these two new rules was expected to lead to an increase in efficiency and customer satisfaction with no increase in risk.
Example 2: Creating cross-selling opportunities
Hannover Rück SE Malaysia Branch, and the hr | bluebox Data Analytics team members collaborated with an insurer in South-East Asia to find patterns in existing customer data that could reveal new cross selling and up selling opportunities for critical illness policies. The project focused on improving the effectiveness of upsell campaigns by expanding the eligible customer base for guaranteed issue products beyond the traditionally narrow segment of young, healthy customers.
Using advanced data analytics on the client’s existing medical claims data, Hannover Re developed a predictive underwriting model that segmented policyholders into low, medium, and high‑risk groups. This segmentation enabled the insurer to safely extend product eligibility to a broader population and offer higher coverage limits without expecting significant changes to risk levels.
The enhanced targeting approach supported better product design, higher expected take‑up rates, and a more meaningful proposition for customers.
"Explanations were great, especially adding value with ML methods […]"
Client quote
Implementation & process considerations
Data analytics is not a uniform solution.
- To deliver the best outcomes, each project must maintain a clear link to the value delivered for all stakeholders. By forming a joint project team with Hannover Re and our client, hr | bluebox becomes embedded in our client's operating environment, thereby enabling a clear understanding of capabilities, challenges and priorities.
- Regulatory requirements are a critical consideration. All data use must comply with rules on privacy, consent, sharing, transparency and fairness. Achieving the right balance between innovation and regulation can be challenging, but early alignment supports trust with regulators and customers while enabling sustainable, data driven value.
- Data quality is equally important. hr | bluebox guides clients in understanding, cleaning and enhancing data, by drawing on experience across more than ten Hannover Re markets. Once the data is ready, the technical analysis begins. The value lies in selecting and applying suitable techniques, from regression and cohort analysis to advanced machine learning that can uncover hidden patterns and forecast future outcomes with greater accuracy.
How Hannover Re can support you
All of Hannover Re's L&H insurance clients are welcome to access the hr | bluebox global analytics service. Our streamlined approach delivers high-quality insights into your application, portfolio and claims data, using a combination of statistical tools and optimised AI techniques. The personalised interpretation and advice from our analysts are equally as important, and is done in collaboration with our client teams.
Technology will continue to rapidly advance. To be prepared, insurers must be equipped with the right approach to harness their data and generate powerful, actionable insights. The examples in this article represent a small selection of what hr | bluebox can offer. Get in touch with us to explore tailored solutions that best suit your business needs.
Disclaimer:
The information presented in this case study, as well as in any other descriptions of projects or cooperations, is for general informational purposes only and does not constitute legal advice, regulatory guidance, medical advice, or any form of professional advisory service. To avoid any wording that could be interpreted as implying medical effectiveness, this document must not be understood as containing, asserting, or suggesting any medical efficacy claims. Nothing in this document shall be construed as a legally binding offer to enter into any contract or agreement of any kind. All results, performance indicators, and outcome descriptions reflect the specific conditions and parameters of the referenced project(s) or product launch(es). They are not guarantees, promises, or assurances of comparable or future performance in any other context. No warranty is given – whether express or implied – particularly that comparable or similar results can or will be achieved elsewhere. Any potential success of comparable initiatives critically depends on the lawful and compliant ability to contact policyholders, including the validity of any required consent under the applicable legal framework. All third‑party entities mentioned in this document, including insurtech companies, are independent businesses and operate separately and autonomously from Hannover Re. Their services, solutions, or technologies are neither endorsed nor guaranteed by Hannover Re and are subject to their own terms and conditions, unless explicitly stated otherwise. References to third‑party trademarks or brands are made solely for descriptive purposes and remain the property of their respective owners. No affiliation, sponsorship, approval, or endorsement by those owners is implied.Statements in this document regarding benefits, compliance requirements, risks, risk assessments, or business cases are based on our subjective experience, judgment, or interpretation. They do not replace - and must not be relied upon as a substitute for - independent assessment, verification, or due‑diligence activities by customers or other stakeholders. In particular, all aspects relating to data protection requirements (including but not limited to GDPR considerations) and AI‑related regulatory obligations must be independently assessed, verified, and evaluated by interested parties in light of their own legal and operational frameworks. The use of the terms such as “partner” or “partnered” without further qualification solely indicates that we cooperated on specific activities or exchanges with third parties; it does not imply a legal partnership, joint venture, affiliation, or any form of shared corporate structure. Each party acts independently and on its own behalf. Hannover Re and/or affiliated companies of the Hannover Re Group assume no liability for the accuracy, completeness, or future applicability of the information provided in this document. We consider regulatory obligations in AI solutions for our clients and specifically ensure full compliance with the EU AI Act while maintaining a careful and security‑focused approach to deploying and managing AI systems like hr | bluebox.
