Data and data management is not a new proposition or challenge for insurers. Insurers have traditionally accumulated massive volumes of data, but haven’t used it as part of key decision making throughout the organisation. The use of big data can propel competitive advantage, engender customer loyalty and fulfil the increasingly stringent regulatory and compliance requirements. There appears to be a view within the industry that if small internal data is not currently utilised to full effect, then why use big data taken from external sources?
Recognition is needed that data, from both internal and external sources, will deliver the greatest value and meaningful insights, using this data to make management decisions across the organisation will create competitive advantage and business differentiation.
Insurance companies can unlock the power of data to:
1. Boost customer retention and acquisition – Data can be utilised to achieve greater customer understanding and insight. Information should be gathered about a customer at every touch-point and by using analytics, personalised solutions can be provided based on a deep understanding of customer’s needs.
2. Develop better product propositions and understand risk – Through the proliferation of external third party data and use of the latest modern technology, insurers can gain a better ability to predict and understand customer behaviour and many different types of risk more precisely. This will allow the ability to price policies more accurately and develop products that are more profitable.
3. Improve fraud detection – Performing analysis with big data can accelerate the detection of fraudulent activity and patterns. Analytics can now be provided in real-time, checking for fraud before a policy is approved.
4. Extend capabilities and market reach – With greater data, underwriters may be more willing to write policies for those they have previously turned away. Also products can now be delivered cheaply to markets which previously could not be served due to the ability to cost-effectively analyse risk through the use of data.