Big Data in the Insurance Industry April 2014 BLOG POST
Turning Big Data into Smart Data for Bigger Opportunities The proliferation of Internet, social networking and social media activity – all of which generate a vast amount of data – has led to the emergence of Big Data – data that is difficult to capture, curate, manage, process, and transfer within a tolerable elapsed time. About 80% of the world’s information is unstructured, and it is growing 15 times the rate of structured information. Companies are being forced to find newer information management models to harness this unstructured information – this new requirement is being called Big Data. A great volume of data is generated from some of the following sources: • Highly granular data capture by companies – Capturing every transaction and interaction with customers in details • Social networking – Vast amounts of data including; profile information, details on connections, etc. • More and more devices being connected to the Internet – mobile phones, pedometers, electronic package labels, vehicle telematics, etc. Like other industries, the Insurance industry has also woken up to the importance of big data. The need of insurers to collect more data from new and different sources in order to know their customers more intimately can be fulfilled by big data. New sources of data for insurers include proprietary telematics, website behavior and third-party data such as search engine information. However, an increasing amount of this data is unstructured, which requires analytics’ help. Big Data brings some key solutions to the Insurance Industry • Fraud detection – Indentify and categorize suspicious claims more accurately and in earlier claim cycle • Straight through processing (STP) – Claims are routed quickly without suspicion and subrogation opportunities to expedite processing, reduce claim handling costs and improve customer satisfaction • Subrogation – Identify subrogation opportunities more accurately, increase the identification rate and increase average opportunity value • Claim frequency and severity – Reduce claim frequency and severity by analyzing unstructured claim data, improve loss ratio and reserve accuracy
Insurers are still using older methods like analytics and predictive modeling for process improvement and loss mitigation. Big data is yet to become a key priority for most insurers.
Big Data in Insurance
Page 2
Challenges Big data helps organizations make better decisions. However, the biggest challenges are concerned with speed of data creation, increase in types of data and the ability to provide analytics against data. Moreover, finding people with the skills to translate big data into big analytics and big decisions is another major challenge, along with an analytics-based decision-making culture across the organization. Projects on Big Data are not a priority for R&D budget allocation, as few tangible and near-term benefits are identified by insurers – compared to other IT budgets. Economic Benefits of using Big Data in the Insurance Industry Big data analytics can help businesses of all sizes analyze a large amount of unstructured data in very less time. This would not only save time but also bring insights, intelligence and trends never seen before. The UK insurance industry would experience business efficiency benefits as much as:
Big Data adoption rate in the UK insurance industry is around 38% (2011) and would reach about 58% (2017). In 2011, the industry received an economic benefit of ÂŁ517 million, which is expected to reach about ÂŁ4,595 million cumulatively. Big Data would associate more variables, more information to produce greater accuracy of analysis and hence greater revenue generating prospects for insurers. Consumers are to gain by identifying lowest price on offer with price data being readily available. Insurers must first align their business needs before developing a platform to leverage Big Data and Analytics. One of the major risks for insurance companies is to procrastinate and miss the bus. Forward-looking insurance companies should start building their Big Data infrastructure now by building technology platforms that securely integrate (such as Mobility, M2M, Analytics, Cloud, Security); developing a service-centric architecture that supports flexible, responsive, and agile business models and global capabilities; creating social platforms to drive business intelligence; and building new customer channels, and the employees to support them, through partnership with trusted global technology provider(s).
Big Data in Insurance
Page 3