As businesses implement more and more technology within their daily operations, companies find themselves sitting on oceans of valuable consumer data. This information asset is often referred to as “big data”.
Big data offers businesses the potential to leverage the customer information collected by their systems and use this to their competitive advantage by improving the products and/or services that they provide. Large corporations, like Amazon and Google, have been using big data to better understand their customers, their business and their operational processes for many years now.
Companies in the insurance industry have always used data to price risk and assess claims. However, machine learning (AI) technology is now available to rapidly and far more intelligently analyze the vast amounts of data collected and provide insights into product sales and customer experience (which supports increased personalization, faster underwriting, better claims handling, etc.).
Nevertheless, until fairly recently many companies in this sector have, to varying degrees, neglected the importance of the big data they have been capturing, and the fundamental benefits to a business that it can bring.
By now, the majority of insurance companies understand that big data is of fundamental importance to providing a service of quality, and that to maintain a competitive advantage they must capitalize on the potential of existing and new data sources. Insurance companies can use big data in several business-critical areas, including:
Pricing policies based on complex risk assessment procedures have always been a core requirement for insurance companies. The European Insurance and Occupational Pensions Authority (EIOPA) recently highlighted the fact that it is in this area that big data is having the most significant impact on insurers.
For example, in motor insurance, IBM and LexisNexis Risk Solutions, as well as smaller provides like Greater Than, are giving insurers the ability to price each policyholder more accurately by comparing individual driving behavior with a vast pool of shared data to correlate policy risk.
Big data analysis also benefits other types of insurance, including life and health insurance, using trends to assess mortality and healthcare risk often measured by wearable (IoT) technology.
The claims journey, where the insurer assesses the loss or damage to a policyholder or their possessions, to decide on the validity of a claim could historically be a slow one, which often resulted in a poor customer experience whether claim was paid or not.
However, big data offers all insurance providers enhanced analytics to better segment claims, and in some cases fully automate them, greatly improving accuracy, speed and customer experience – where the claim is paid of course!
In terms of fraud prevention and detection, intelligent big data analysis means that insurance companies have a higher probability of catching fraudulent claims before they get paid. In 2019, over 107,000 fraudulent insurance claims worth nearly $1.5 billion were uncovered by insurance companies, reducing the impact of fraud on business profitability.
Telematics in the insurance market is now modifying consumer behavior to reduce risk. Big data analysis by a variety of apps, wearable tech, IoT devices, etc. highlights which policyholders are heading for a claim with their driving (motor), household security (home) or even their lifestyle (health). Then the technology will intervene before a claim occurs by recommending a policyholder adjusts high-risk behavior, providing intelligent risk mitigation based on big data.
Insurance companies have always captured and safely stored large amounts of data, but historically they did not have the technology to store and accurately analyze all their data at speed .
Big data, and the technology to more effectively store and rapidly analyze it, is now a significant transformation driver for the insurance industry and this brings with it a number of challenges.
A modern data strategy, and appropriate governance, for efficient data storage, organization, cleansing, usage and accurate analysis requires specialist skills and cutting-edge technologies. The increasing volume, velocity, and variety of real-time data that is now being generated by mobile and IoT devices, that monitor factors such as behavior in relation to insurance, exponentially increase the data storage and processing challenges as well as making data governance and appropriate usage much more complicated.
Data security is paramount for customer experience and brand image. Insurers have to guard against the increased potential for big data theft and/or fraudulent data access/manipulation on systems, which are becoming more distributed across multiple locations in different geographies.
Cyber security topped the list in a recent Deloittes survey of global insurance companies, in terms of an expected increase in investment to implement “zero trust” principles for access to data and “security by design” for technology.
Insurance industry regulation continues to evolve, further complicating big data management strategies and solutions. As well as a host of pandemic related regulation (market conduct, business interruption, solvency concerns, etc.), regulation for artificial intelligence underwriting, pricing and claims algorithms; climate change, cyber security and data privacy are all expected to increase.
Successful insurers are now building compliant, data-driven organizations to increase the personalization of products, improve and speed up underwriting and claims processes and optimize customer experience. To do this requires, among other things, significant technological change.
A strong IT foundation that is secure and scalable, which allows linkages to multiple internal and external data sets (possibly as microservices) via APIs, and supports advanced analytics and automation capabilities, is critical to a company’s success.
As part of this, migrating to a cloud infrastructure allow insurers to rapidly and cost-effectively implement big data storage, management and analytics and the necessary machine learning and automation tools to support this.
Insurers need to maximize the value of big data and the competitive advantage this brings, without negatively impacting end customer experience or falling foul of compliance regulation. Achieving this requires a seamless technology refresh underpinned by a shift to a modern, rapid delivery with intelligent, specialist testing and quality assurance that utilizes the latest AI and automation tools to support rapid change delivery and validate big data and the AI models used in advanced analytics.
Qualitest is a digital transformation, insurance product and big data quality assurance specialist, working with insurance companies such as: Legal and General, Admiral, Covea, Ageas, Gallagher and many more.
We can offer your business confidence in your technology, your big data and your AI predictive models (for underwriting, fraud; customer renewals, etc.), reducing your business risk by assuring your customer experience is optimal at all times and your solutions are secure and compliant.
