Implementing the Insurance Distribution Directive: the role of analytics

Implementing the Insurance Distribution Directive: the role of analytics

In October a new directive will change the focus of insurers from products to customers.

The Insurance Distribution Directive (IDD) comes into force in October this year. It is expected to have a wide-ranging impact on the insurance industry, because it fundamentally changes the focus of insurers from products to customers. This requires a radical rethink about how insurers do business. And as companies from around the world, and all industry sectors, are finding, a genuinely customer-centric approach is only really possible by harnessing the power of advanced analytics.

Assessing the impact

IDD is EU legislation that regulates how insurance products are designed and sold. It affects both insurance companies themselves, and intermediaries who sell insurance products, such as brokers. In particular, it sets out what information should be given to customers before they agree on an insurance contract, and provides a code of conduct for business, and rules about transparency. It also clarifies cross-border business rules. Like many EU regulations, the Directive is designed to both improve the protection for consumers, and also make competition fairer, by creating a ‘level playing field’ in insurance.

The Insurance Distribution Directive is designed to both improve the protection for consumers, and also make competition fairer, by creating a ‘level playing field’ in insurance. Learn more by listening to this webinar in Italian.


The main impact of the directive is likely to be in three areas: customer centrality, processes and technologies, and people. The regulations emphasise the importance of focusing on customers, and their characteristics and needs. This applies to both creating and distributing products. It also, however, applies after sales and in monitoring customers. It is very useful to measure the degree of satisfaction, and seek out customer feedback, because this can both help to address any immediate issues with products, and also be used to improve the development and distribution processes for all your products.

Customer-centrality, in turn, will drive changes to factors internal to insurers, including processes and technologies and people. Improving the customer focus is likely to lead to ongoing improvements in communication between company, agents and customers. This, in turn, will provide more information that can be used to improve processes and technologies — and therefore provide a better customer service and improved products, in a virtuous circle. People are clearly an essential and fundamental element in the path to the adoption of the directive. It is, after all, people who interact with customers, design products and improve processes. They are therefore central to improving the efficiency of collaboration and communication with customers, and therefore improving the customer focus.

All three elements are part of the change and must be considered together.

Delivering the directive

Analytics is essential to delivering a customer-centric organisation. What, though, does that mean in practice? It is easiest to think about this over the analytics life cycle, covering Data, Discover and Deploy.

First, data. As far as a cycle can be said to start in one particular place, the analytics life cycle starts with data. To be customer-centric, you need to understand your customers, and to do that, you need data. You will already be collecting some, but you are likely to need more, and particularly from different types of sources, including both structured and unstructured data. Data may be obtained from a wide range of sources, including formal questionnaires, such as customer satisfaction surveys and gamification questionnaires, external suppliers and unstructured data from customer interactions with call centres, feedback from agents and analysis of complaints. Although more data is likely to be good, it is also worth considering which data will be most useful, and focusing on that.

The second stage of the analytics life cycle, Discover, is the exploration of the data, to enable you to discover insights. Machine learning and analytical models are essential for extracting value in this way, because they allow you to profile your customers effectively, and also provide simulations and scenario analysis as you test new products.

Finally, the deployment stage is the point where you apply your insights to the business, and use them to improve decisions. This requires tools to help with decisions, whether for staff in the call centre or via the web channel. The tools need to integrate business rules and the output from analytical models to ensure that the best possible response is given to customers in real time. These tools include the ability to make a ‘next best offer’ in response to the customer reaction.

A new partnership

Delivering a customer-centric and data-driven organisation requires both tools and people. Analytics models cannot deliver on insights alone, and neither can people manage without the insights from analytics. Insurers need to develop effective partnerships between people and processes, to ensure that customers are at the heart of their business.


About Author

Alena Tsishchanka

Insurance Practice Leader SEMEA at SAS

Alena Tsishchanka is Insurance Practice Leader in SAS South EMEA region. She has her Master's Degree in Business Economics and Quantitative Methods for Finance and another Master’s degree in Linguistics. In SAS she has been working for 8 years as Presales on Insurance and Banking Accounts responsible for generating, promoting, implementing and monitoring innovative business analytics solutions within Insurance and Banking industry in Italy, Europe, Middle East and South Africa. She is collaborating with SAS Customers and Partners in Insurance Industry on analyzing business processes with the goal to find out main trends and issues and introduce innovative technologies, providing functional and technical advice on how SAS Data Mining, Machine Learning and AI, Data Visualization and Data Management applications could bring real business value to the companies.

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