- With the rise of big data and analytics, organizations have access to vast amounts of customer data that can be used to generate valuable insights and drive business success.
- However, many organizations do not even realize they have this data or the potential value it holds.
- Different types of customer data can be leveraged for monetization, including demographic, behavioral, and transactional data. Doing this in a responsible way requires a clear strategy, ethical considerations, and alignment with customer expectations and regulatory requirements.
Acquiring, managing and using data is central to the insurance business, because risk and customer models drive everything from evaluating market needs and calculating risk, to detecting fraudulent claims.
For decades, insurance companies relied on paper-based forms of data management; some still do. But most of today’s ultra-competitive businesses can’t afford to wait for lengthy, manual processes to prepare and incorporate information.
Their challenge is to move from plodding paper or manually-prepped data to large, quick-moving datasets that generate insights and increase efficiency.
For instance, paper-based manual processing will be able to state where your customers holidayed last year (e.g., a ski resort). But a digital approach using data-driven insights can tell you where they are right now (on a ski slope), what they’re going to do (ski) and a host of other bankable observations. This more profitable approach opens the door to advice (“you don’t have the necessary skills to ski this route”) and just-in-time insurance. Some customer profiling and insurance products require near-real-time stream processing.
Working with data
To assess your ability to handle data (and adapt to change) you’ll need to ask yourself some basic questions:
- Is your data digital?
- Do you know what data you have and can you get the data you need?
- When could you get a new set of data and make it available internally by?
- Are you able to handle data streams and evaluate risk or deliver a 360-degree customer view?
- Can you dispose of data that you don’t need? If yes, how long does it take?
- How much time do you need to ensure the data is used ethically?
- How long would it take to remove specific data and measure the impact on the rest of the organization?
- Are you OK with data traveling at different velocities?
- What would you do if a data source was briefly unavailable?
- How good are you at predicting?
Which of the components do you need to acquire or upgrade, to unlock potential in the data?
More accurate prediction
Although modeling is a nuanced discipline, the Kuhns and Johnson practical definition of predictive modeling — the process of developing a mathematical tool or model that generates an accurate prediction — is as good as any.
Modern data and advanced analytics involve increasingly complex algorithms and methodologies. This intensifies the need for strong validation and model governance, as well as new actuarial tools like predictive modeling, together with a full set of insurance-related modeling principles.
Actuaries will need to adopt a digital core approach, incorporating risk and customer modeling into everyday actuarial practice. And, because data engineering and predictive analytics are continually evolving, best practices will be improved as a matter of course. So, it’s imperative that all concerned keep up with the latest developments.
More accurate prediction
When modeling risk, insurers need to be able to answer the following questions quickly:
- How do you know which data is relevant, and how can this improve your risk models?
- How much effort do you put into identifying new insurable items? And how often do you review your entire portfolio to work out what to grow, shrink, sell or acquire?
- How long would it take to improve an existing risk model, spot the decay of an existing model and react?
- How do you test your models?
- How can you spot new insurable items and drive their maturity?
- How would you spot a part of your portfolio that you ought to sell or acquire? How much time would it take?
- How do you ensure the model is in line with ethical standards?
From a customer perspective, insurers need to ask themselves questions like:
- How do I test my hypotheses? Connection with the customer?
- How could I show more relevant data (in-time pricing computation for comparators)?
- How can I discover my customers’ important events?
- How should I establish channels for automated sales?
- How will I know what my customers value and want to insure?
- How would I spot the changes in things that are important for customers?
Building your own road map
Once you’ve answered the questions, you can begin to plan your journey. All highlighted paths to the future share a first step. The map below shows that whatever your goal is, the foundational work should happen around components grouped in the bottom right-hand corner.
Look at the map and ask yourself, “which components should I upgrade first to help my organization now, and which will be of critical value in the future?”
Data is a fundamental asset for insurers, yet it can still be viewed as a component rather than the core of an insurance proposition. Our recommendation is that the key data components, data acquisition, storage (platform) and analytics (price/risk analytics) are the foundation, and need to be reassessed in the broadest terms of product and proposition development.
Share invaluable insights
This is the third of three blogs looking at the purpose and value of domain-related data to insurers. Several industry experts shared their thoughts, drawing on market experience, tech knowledge and current customer base to draw their conclusions. If you missed the first two, you can read the first blog: Get more business value from your insurance data deluge and the second blog: Calculate insurance risk more precisely and turbocharge your digital customer experience.
DXC is the leading provider of core insurance technology globally, with over 1,900 insurance customers serviced by over 18,500 professionals.
Luxoft, DXC’s Analytics and Engineering business, is the preferred partner for banks undertaking strategic transformation, and is providing fresh insight into insurance sector analytics. To learn more, contact: firstname.lastname@example.org