10 months ago


Thought Leadership

Thought Leadership Progression Towards Intelligent Automation in Insurance Dave Ovenden Global Director of Pricing, Product, Claims and Underwriting, Willis Towers Watson Insurance Consulting and Technology The growing concentration of InsurTech businesses focused on “back office” insurance operations should help precipitate the development of more advanced decision support systems, and ultimately, intelligent automation As Alice and James note in their thought leadership piece, there are a number of potential reasons for the recent shift in focus of many InsurTech businesses towards insurance operations support. In the short term, this shift is likely to bring new ideas and opportunities for extending the use of automation technology beyond robotic process automation (RPA) in order to lower expense ratios. RPA effectively replaces a human with a robot to carry out repetitive tasks, and a number of incumbent insurers have already adopted it to varying degrees. But, by coupling RPA with layers of expert decision algorithms, robust data integration and a real-time decision support platform, it will be possible to deliver consistent, accurate and informed decisions in underwriting, pricing and claims – intelligent automation. Add to that some advanced analytics, machine learning capability (that actually learns rather than just applies rules) and short update cycle times of the type already available with Willis Towers Watson technology, and an insurer has the basis for a true AI system that can improve with time and data. An increasing number of clever and entrepreneurial businesses buzzing around and seeking openings in these areas should continue to push the automation envelope. A lot of the focus in InsurTech to date has been on personal lines, owing largely to the generally more significant volumes of data available and the need for excellence in client experience. Expect that to continue. That said, with high levels of frictional cost, commercial insurance is certainly ripe for some operational transformation. What Might Intelligent Automation Look Like? Take commercial renewals, for example, which are generally neglected when it comes to innovation despite often representing greater than 80% of an insurer’s total business. Premium size, complexity of risk, geography and distribution analytics are among the risk assessment variables that lend themselves to some automation. To contextualize the automation though, it would also need to define expert responses across a number of segments, combining layers of insight around factors such as: • Absolute and relative risk performance of the account, e.g. loss history • Intermediary behavior, e.g. how will the broker react to different price scenarios? • Market norms – what adjustments are possible in the prevailing market? • Large loss potential – an area where we have deployed machine learning algorithms In this way, the expert environment can deploy smart decisions in relation to whether to automate or not, as well as in the actions taken. Application of layered intelligence and rules Illustrative Automated Judgment Layers for Commercial Pricing Renewals Trade Exposure and Complexity Geography (e.g. Cat Strategy) Performance Large Loss Potential (Potentially ML Algorithms) Distribution Analytics Market Environment Deploying the company’s appetite and underwriting rules Filtering across the geographical dimension Absolute and relative metrics, comparing “risks like these” Understanding the relative impact of a policy on portfolio volatility Understanding individual broker behavior Market and demand insight (e.g. ability to increase premium following claims Sophisticated automated footprint with tailored responses for each individual policy Where the policy is referred for underwriting intervention, the data would flow with the referral and create decision/ negotiation support insight 49

Thought Leadership Progression Towards Intelligent Automation in Insurance The Role for InsurTech The question then is what technology is needed to support such goals. Having worked with a number of insurers that are eyeing equivalent opportunities, often as part of developing a wider technology roadmap, we advise them of some essential component layers. • Interface Layer – such as Willis Towers Watson’s Brovada software, to support communication between the Frankenstein monster of legacy systems that often exists in insurers • Decision Framework – to apply and interpret rules within the business, incorporating machine learning elements and capability where it can add value and enhance the quality of decisions • Segmentation Framework – allowing appropriately granular segmentation recognizing different types of business, clients and financial objectives • Real Time Decision Engine – software such as Willis Towers Watson’s Radar Live serves the essential function of very quickly collating and outputting all the analysis taking place in the background to the end recipient, be it man or machine The ability to acquire and integrate data across the layers is also a prerequisite. This includes the potential to augment analysis with third party data enrichment, including unstructured data assets and information from various fintech sources, such as survey data, that can contribute to a better outcome. Eye on the Prize Unsurprisingly, the outcome sought by virtually all insurers is to enhance business models while making substantial cost savings across product portfolios. Opportunities aren’t confined to pure automation. Where, in commercial insurance for example, human intervention is required, the same technical environments and outputs will provide invaluable decision support for technical pricing, negotiation, and portfolio and market benchmarks. Intelligent automation certainly appears to offer fertile ground for cooperation between incumbent insurers and companies with interesting technology, be they established or early stage. Those that can provide or support the ability to better use internal and external data assets, offer novel scoring algorithms, and enable cognitive insights from unstructured data assets should be in demand. Dave Ovenden is the Global Lead for Pricing, Product Management, Claims and Underwriting Consultancy in Willis Towers Watson’s Insurance Consulting and Technology business. Quarterly InsurTech Briefing Q4 2017 50

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