Interview with Dr. Yinglin Zhang, General Manager, Life & Health – Data Analytics
"Diving into the world of data opens up a treasure trove of insights and ever-evolving opportunities"
Data is the new gold, and life & health insurers are custodians of vast amounts of it. This presents an excellent opportunity to mine this resource to reexamine risk and how it's managed. Beyond refining marketing decisions, improving forecasting capabilities and harnessing the potential of the Internet of Things, the booming trend is to use data analytics to redefine business strategies. But this comes with challenges, including using data responsibly, ensuring consumer security and privacy, and complying with local regulations. In this in-depth interview with Dr. Yinglin Zhang, we take a closer look at what data can do for insurers – now and in the future.
Why is data and data analytics so important in the life & health insurance industry?
Data and data analytics have always been at the heart of the insurance business. But new opportunities are opening up to transform our industry in many ways through sophisticated analytic approaches combined with technological advances in data collection, storage and processing. And as these new opportunities are explored, the value of data is likely to continue to grow.
What exactly is data analytics for life & health insurance, and what skills are required?
In life & health insurance, data analytics is the use of methods from statistics and computer science applied to collected relevant data with the purpose to describe and predict insurance events and ultimately to support insurance business decisions. It is worth to emphasise that this isn't solely a technological matter and relies heavily on human understanding, domain knowledge, and decisions about the appropriate modelling approach. In contrast to business intelligence, which is limited to understanding historical data and is often an IT topic, data analytics aims to gain insights into unknown future events and has a strong modelling focus. It combines mathematical and statistical modelling to develop methodologies, IT knowledge to provide the infrastructure and process flow, and actuarial knowledge to understand the business context. All three are essential for effective data analytics in life & health insurance.
Are data analytics and artificial intelligence (AI) the same?
Data analytics is often mentioned in the context of AI, but they aren't always synonymous. Data analytics can make use of AI models, but it does not have to. Machine learning models, which are actually extensions of statistical models, introduce additional structural complexity and automated calibration through cross-validation, the so-called 'self-learning process'. Today's technological advances make it possible to collect, store and process vast amounts of data, enabling the processing of complex models. However, increased complexity can come at the expense of explainability, especially with so-called 'black box AI' – machine learning models whose decision-making processes are not transparent to the user. While explainability is critical in the core business of insurance, these black box AI models can still have tremendous potential in areas where explainability is less critical, such as improving the customer experience and increasing process efficiency.
As Head of our Hannover-based Data Analytics department, Yinglin joined Hannover Re in 2022 from the primary insurance sector.
“Data alone does not provide direct value to insurers. It's the professional analysis of the data that unlocks its true value.”
What types of data do life & health insurance companies have access to?
Insurance companies have access to a wide range of data. In addition to asset-related data for capital management, what is specific to the insurance sector is insurance liability data, which can generally be divided into two categories: portfolio data and third-party data. Portfolio data includes policy and policyholder information, such as age, gender, beneficiary details and claim details. Depending on the product, additional medical, lifestyle and financial data may also be available. Third-party data refers to publicly available population statistics or data acquired from specialist external providers. The availability of population statistics depends on the collection of data by the national statistical service, for example through censuses, and on the ability and willingness to make such data available to the public. Data from external specialists is limited by data protection regulations in individual markets.
What are the typical use cases for these two types of data?
Both data sources can be used strategically by life & health insurers. Portfolio data can be used to understand the experience of the insurer's own portfolio, for portfolio management purposes and for future underwriting of similar products in similar markets. Third-party population data can be used as a baseline for mortality or morbidity trends and is particularly important when launching new products in new markets. Where available, more granular third-party data can be used to refine baseline assumptions, for example. In addition, both data sources can be used to improve the overall customer experience throughout the customer journey.
What are the benefits of a professional data analytics approach for insurers?
Data alone does not provide direct value to insurers. It's the professional analysis of the data that unlocks its true value. Let's focus on three key areas: First, in the context of pricing and valuation, data analytics provides sophisticated best-estimate forecasting. This ensures that premiums are set at the right level, and that insurance companies have sufficient reserves to provide optimal cover to policyholders. Second, risk management, which is critical in our highly regulated industry, is enhanced by data analytics for portfolio monitoring and future risk quantification. This enables timely planning and strategic decisions, thereby strengthening financial stability. Finally, in product development, data analytics helps insurers adapt to evolving trends and create innovative solutions, such as pay-as-you-live products based on Internet of Things (IoT) devices like health apps, which are already available in some markets around the world.
How can data protection measures be reconciled with the facilitation of meaningful analytic insights?
Today, everyone is aware of the importance of protecting both business and personal data. It's important to emphasise that insurance professionals, and data analysts in particular, strictly adhere to individuals’ data usage preferences and, of course, operate within the confines of legal regulations. For analytic purposes, analysts use anonymised information that cannot be traced back to an individual policyholder. By identifying patterns, we use it generically to make informed predictions about how biometric trends will develop and how consumers are likely to behave. It's worth noting, by the way, that in addition to meeting data protection requirements, the regulation of AI is gaining significance, as we can see with the EU's introduction of the 'AI Act', a crucial step in the legal and ethical governance of the technology, which will have implications for its use in our industry.
Yinglin, who holds a PhD in Financial Mathematics, brings fluency in four languages and a blend of creativity, rationality, flexibility, and determination to her role.
It was her passion for our company's international working environment and collaborative spirit that brought Yinglin to Hannover Re, where she is committed to fostering a culture of trust and transparency and thrives on the challenge of driving continuous, sustainable improvement.
“The fast pace of the modern world promises to usher in an exciting future in terms of how we use data to drive multi-faceted change in the insurance industry.”
What challenges are life & health insurers facing in relation to data analytics?
The primary challenges revolve around three aspects: the responsible use of data, the demand for flexibility and efficiency, and model explainability for the insurance core business. Ensuring responsible data use involves addressing regulatory compliance, privacy concerns, and avoiding the reinforcement of social bias or inequality. When it comes to flexibility and efficiency, ensuring that data analytics is not hampered by unstructured, poor-quality data and outdated IT infrastructures requires an architecture that balances standardisation and customisation, as well as the timely elimination of technology debt. For the insurance industry's core business, understanding risks and models is essential for business steering and can be achieved through improving traditional statistical models or exploring explainable AI.
How can cedents benefit from Hannover Re's data analytics expertise?
Our dynamic team is made up of highly skilled individuals with strong statistical backgrounds and diverse expertise. We embrace collaboration, think globally and act locally, and prioritise the specific needs of our clients. Cedents benefit from our specialist know-how in modelling and analysing complex risks and niche markets, giving them access to insights they may not have in-house. Our hr | bluebox service exemplifies our collaborative approach and demonstrates how we work hand-in-hand with our clients.
hr | bluebox sounds like an interesting concept. Tell us more about it.
Offering personalised data analytics as a service, hr | bluebox is designed to create financial value through a mutually beneficial approach – a harmonious blend of efficiency for us and effectiveness for our clients. By offering a bespoke service rather than providing a standardised software solution, we honour the individuality of different markets, products and business needs. In fact, we’ve called our service 'bluebox' for a reason: it’s a testament to our commitment to transparency, and a deliberate alternative to the hidden workings of black box solutions. Drawing on in-house expertise from around the world and dedicated research, our focus is on thoroughly understanding the business case and data, and providing cedents with actionable recommendations to solve their specific business problems.
What is the scope of the hr | bluebox service?
The current scope is primarily, but not exclusively, on understanding policyholder behaviour, improving product offerings, supporting underwriting optimisation and providing general technical advice or model validation. Concrete examples include early lapse analysis and pattern recognition for potential cross- and up-selling opportunities. Early lapses often result in net losses that impact profitability, cash flow and business planning. hr | bluebox uses explainable AI to identify patterns, trends and drivers behind these events, and provide recommendations for prevention strategies. When it comes to identifying cross- and up-selling opportunities, which are critical to increasing revenue and deepening customer relationships, hr | bluebox uses predictive modelling to identify policyholders with appropriate risk profiles who are most likely to be interested in additional coverage or higher policy tiers.
What are the most important trends in life & health insurance and data analytics?
While it's impossible to make accurate predictions about the future, several indicators point to potential market developments for life & health insurance, including the availability of new data sources, advances in IT and medical technology, climate change, and socio-demographic shifts associated with evolving lifestyles and heightened social awareness. These trends are influencing the industry in many ways, from product design and pricing assumptions to reshaping the interaction between insurers and policyholders, with an emphasis on insurers' social responsibility. For example, there is a growing demand for products that are digital, easy to access and socially fair. Data analytics plays a key role in all these developments. It not only unlocks the potential of new opportunities, but also helps to mitigate the associated risks.
How can insurers stay competitive in the evolving landscape?
The fast pace of the modern world promises to usher in an exciting future in terms of how we use data to drive multi-faceted change in the insurance industry. To navigate this dynamic landscape, it's crucial not only to keep abreast of market developments, but also to foster a forward-looking mindset and a culture of agility throughout the organisation when it comes to change and challenges – what we at Hannover Re call 'future readiness'. This has proved essential, especially during the recent COVID 19 period. With competition expected to intensify, investment in methodological development, modern infrastructure, and partnerships that complement domain knowledge is essential. As the human element remains central to the effective use of knowledge and technology, talent management and retention should be highly prioritised.
Thank you for the fascinating insights. It's clear that data analytics presents tremendous opportunities ...
Absolutely! Diving into the world of data opens up a treasure trove of insights and ever-evolving opportunities. What makes this role even more fulfilling is the chance to deliver innovative solutions and valuable insights to our clients. Thank you for the opportunity to highlight the profound impact that data analytics can have!
Away from work at Hannover Re, Yinglin teaches at university and relaxes by indulging her creative side, with drawing being one of her favourite activities, providing a serene balance to her dynamic and busy work life.
Your data, our expertise
Embark on a collaborative data analytics journey with Hannover Re: Through the synergy of AI and personalised guidance from our expert analysts, we unlock top-quality insights, enabling you to make informed decisions for tomorrow's opportunities. Let's shape the future of your business together!
Dr. Yinglin Zhang General Manager Life & Health – Data Analytics +49 511 5604-0036 yinglin.zhang@hannover-re.com
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