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Apr 2019

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Data as the fuel for analytics

Source: Asia Insurance Review | Feb 2019

Analytics is an essential tool to help insurers remain competitive but traditional insurers within the Asia-Pacific are struggling to get access to the data in their legacy systems to enable advanced analytics. How can they gain access to this essential ‘fuel’ to power the engine of digital insurance? This report from EY Asia-Pacifics David Chen.
 
 
As the long-term financial prospects of legacy insurers are tested, the future market for insurance will be radically different, more personalised and more targeted. Winning insurers will be those who collect, store and use data better than their competitors, both old and new. 
 
In this regard, a chasm is opening up between modern InsurTechs and incumbent insurers. 
 
InsurTechs versus traditional insurers
Across the region, light-structured InsurTechs, which are digitally native from the ground up, are already running analytics over internal and external data with the intention of securing a competitive advantage over incumbents. Data analytics is important for such insurers to:
  • Personalise insurance products: The explosion of new data sources coupled with the ability to conduct analysis means new entrants are able to provide better, more tailored products to their customers, through the channel they choose, and respond more quickly to changing consumer demands. 
  • Offer more competitive pricing: These products come with dynamic and personalised pricing. Coverage or premiums are adjusted to match a customer’s individual usage patterns and risks, making insurance products more attractive for customers and therefore more profitable for some insurers.
  • Introduce new revenue streams: InsurTechs are also monetising the health and lifestyle data they collect themselves or get from third parties. For example, China’s WeDoctor medical platform offers access to a treasure trove of anonymised, permissioned patient information. The $6bn InsurTech wants to be the Amazon of health care, offering insurers with strong analytics capabilities the ability to profile users and create powerful marketing tools from medical data. Such players want to become platforms that disintermediate current insurers, owning the customer relationship, and relegating incumbents to underwriting and back office functions.
  • Drastically reduce the cost to serve: A technology enabled data and analytics business model will reduce manual effort to serve. Many new insurer entrants are already achieving straight-through processes including automating claims management and underwriting end-to-end – increasing speed, agility and margins versus the incumbents. 
  • Make better decisions: Increased data and the rising maturity of computing power are enabling advanced analytics that support analysis and decisions based on multiple complex models simultaneously and in real time, including predicting the propensity for customers to buy, leave, claim or behave fraudulently. For brokers and agents, data analytics also provides enormous insights, such as average cost to convert a lead to sale, product sales return on investment (ROI), customer-retention statistics and product matching to market or customer. With artificial intelligence (AI) and machine learning capabilities new correlation between data-sets are being discovered to further drive better decisioning.
  • Improve the customer experience: Data analytics-driven insurers can quicker than incumbents with highly automated data-driven processing. Already, with some disruptors, customers can get home or rental insurance approved in 90 seconds – and claims paid within three minutes. In the past, carriers relied on additional questions to tailor a better customer experience. Internal and third-party data combined with analytics will help insurance consultants to become trusted advisers for those in search of the perfect insurance product. Data analysis, and the actionable insights it offers, allows carriers and brokers to identify gaps in customers’ policies and make pertinent suggestions, giving the customer the feeling that the insurer cares about and looks after their physical and financial wellbeing. 
  • Optimise supply chains: Access to meaningful data can help manage contract costs, improve adherence to contract service level agreements (SLAs) and reduce turnaround times. A digital platform that is integrated with the end-to-end insurance process and transactions engines will be an invaluable data source to mine for supply chain optimisation.
 
Replacing old systems
Meanwhile, the existing architecture of incumbent insurers cannot access the data (or handle the volumes) needed to support analytics. Their disparate and more than 20-year-old core systems are unable to pull together basic quality data from across the enterprise – let alone the unstructured data (voice, web and paper scans) required to provide richer insights for management decisions. Many systems were developed before the year 2000 and these systems simply cannot keep up with the modern requirements of digital insurance.
 
Urgent action needed to close the gap
Until recently, many of the region’s insurers believed that major industry change was not needed. They were relying on barriers to digital disruption such as insurance’s complex nature, regulation and high requirements for entry. In some geographies, many insurers, brokers and suppliers within the value chain still have heavily paper-based systems which further hamper automation. 
 
However, disrupters will enter, or be created faster than the industry expects, with a highly attractive proposition for the local tech-savvy population. Digital natives do not necessarily enjoy manually filling out a claims form or application form and submitting to an intermediate agent via a face-to-face contact. They want instant, mobile contact with their insurers and fast, responsive claims management that requires minimal effort on their part. 
 
Incumbents must act now to ride the digital wave that is about to sweep across the sector.
 
How should incumbent insurers respond?
Replacing legacy administration systems can be complex, time consuming and risky. While replacement with cloud-based ecosystems may be the strategic goal of many insurers, it will not address their needs in the next 12 months. To gain access to the ‘fuel’ required to power new business models, incumbent insurers need to start:
  • Developing an advanced data strategy to leverage data better to improve the customer experience and develop new, more personalised product propositions to meet changing customer demands. Looking at data holistically rather than in silo’s across the organization will be important.  
  • Increasing the digitisation of data from both internal and external sources to allow insights to be collected, explored and actioned.
  • Moving to a next-generation platform that forms the foundation for insurance and claims analytics, underpinned by high-performance, cost-effective cloud computing.
  • Adopting more advanced, predictive analytics solutions harnessing AI and machine learning to use data that is more accessible and of a higher quality.
 
Case studies
A large Australian and New Zealand health insurance provider is undergoing a major transformation project to consolidate its data and create a single view of customers, where meaningful insights can be produced to deliver a positive customer experience. 
  • Results: 12% projected increase in its marketing ROI 
 
A major UK insurer is using scoring to accelerate settlement at first notice of loss (FNOL). 
  • Results: Claims cycle reduced by three months 
  • £30m savings over a two-year period
  • 10% increase in recovery rates
 
A leading French automotive insurer has implemented a predictive model using machine learning techniques for claim reserving after FNOL. 
  • Results: 40% decrease in average errors of the claim’s manager estimations for claims under a certain threshold. A 
 
Mr David Chen is EY Asia-Pacific digital insurance leader.
 
Disclaimer: This article contains information in summary form and is therefore intended for general guidance only. It is not intended to be a substitute for detailed research or the exercise of professional judgment. Member firms of the global EY organisation cannot accept responsibility for loss to any person relying on this article.
 
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