Enhancing the Effectiveness of P&C Claims Organisations in the Digital Era

By Bertrand Lavayssiere, Managing Partner at International Financial Management Consultancy, zeb

New levers to control indemnity spend

A combination of a change in customer behaviour, influenced by their experiences in other industries, as well as advances in technology,has seen the introduction of new levers to enhance the performance of claims organisations. The traditional trade-off between efficiency, effectiveness, and customer satisfaction can be mitigated through technology, and the subsequent transformation of a claims business today promises significant benefits. Our accumulated client experiences have shown, for example, that the combination of chat bots and Artificial Intelligence (AI) driven claims triage can allow for cost reductions of around 24% through a steep increase in straight-through processing, all the while enabling real-time customer service and better accessibility.

With ample opportunities available to enhance claims efficiency, effectiveness and customer satisfaction, the control of indemnity spend will remain at the core of UK P&C insurers’ claims transformations, as up to 80% of average total premiums[i]are still being consumed by claims cost. This benchmarked against its European counterparts, the level of indemnity spend in the UK is quite astonishing.Coming in at a staggering £9.2 billion in 2016, the UK insurance market currently suffers from one of the highest claims costs, with UK insurers overall spending 63% more towards total claims comparatively. As the main reasons for this are to be found in the variety of participants within the structure of the UK claims market, as well as the high level of –mainly 3^rd party – fraud, it raises the question of how much of these costs can be controlled by an individual insurer? In a robust system, where commissions and inducements are a large contributor to overall profitability, it is hard for one party to decide to break that mould and become, as some would say, more ethical and customer-focused.However, with the Financial Conduct Authority currently looking closely into the UK Private Motor Claims Market,these inefficiencies are likely to change in the near future. In order to prepare for the changes ahead, insurers need to internally optimise the effectiveness of claims organisations and focus on leakage reduction even more.

As fraud alone costs the industry an estimated £3.4 billion per year, around £50 per premium,[ii]and as we have seen, incumbents suffer from claims leakage of well above 20%, it is irrefutable that accurate assessments of claims severity and validity are a crucial lever for optimising the economic performance for many insurers.Against this backdrop, new technological levers are increasingly addressing the need for more accurate claims assessments and better fraud detection, thus fashioning new key competitive factors in the industry. Indeed, our accumulated experience shows the leverage of technology along the claims process can help insurers to increase the accuracy of indemnity spend by 20-30% through better fraud detection, better control of costs incurred at fulfilment stage, as well as a reduction in legal actions:

Opportunities along the claims value chain

FNOL. Today, FNOLs are mostly received over the phone and the evidence is then submitted in various unstructured formats, such as letters, pictures, and emails. Unfortunately, more often than not, insurers still store the information in an unstructured way, making it difficult to search through and link the collected claims data. As a consequence, claims adjusters will have to form decisions subjectively based on their training and experience, using past claims in their practice groups as reference. The reliance on manual claims intake and assessment, however, renders human error, subjectivity, and,ultimately, claims leakage unavoidable. To tackle that problem, claims information needs to be translated and stored in a structured way – for example, by means of deploying digital FNOL tools, translating letters with the help of optical character recognition, or recording, transcribing, and storing phone calls. Once claims data is prepared for digital processing, an array of opportunities to increase effectiveness will arise, most notably, the automation of fraud detection. Here, Machine-Learning (ML) algorithms are becoming increasingly powerful at detecting otherwise obscure patterns that human claims handlers might not, especially given the increasing quantity of data available beyond the basic claims information, with external data sources opening up possibilities to garner more and better insights into consumer behaviour and profile.Indeed, applications to cross-reference different databases in order to filter out claimants that are prone to fraudulent behaviour are proving to be especially useful. Other applications of deep learning algorithms in fraud detection range from lexical analysis for ‘hit words’ and photo alteration detection, to facial recognition and voice analysis. The automation of fraud detection is especially important in order to secure straight-through processing rates without losing control of indemnity spend.

Loss assessment.Similar to the automated detection of fraud at FNOL stage, larger available volumes of (external) data paired with ML-algorithms can be increasingly leveraged to analyse and cross-reference claims information in all formats, including pictures and videos,and to ultimately generate an initial estimation of claims costs. Solutions for this, however, are often still premature, and the accuracy of ML-driven assessments depends on the level of training and the amount of data fed into the machine. Therefore, the deployment of ML-driven estimations will be restricted to serve as a support for manual processes at the beginning. For example, they could be leveraged in order to provide an objective reference as a basis for claims handlers’ manual assessments or could be forwarded to repair shops as a cost estimate, who could then add or adjust costs. However, we believe that ML-systems will be able to outperform human adjusters in the near future, rendering their deployment a key competitive factor.

Fulfilment and repair.Especially in the UK, the interactions with a vast range of suppliers and entities, e.g. third-party administrators, repair shops, engineers, materials suppliers, pharmaceutical reimbursement managers, and defence counsels, result in avoidable expenses due to costly referral systems.For example, zeb research shows that UK insurers currently pay on average up to 50% more for vehicle repairs compared to French insurers.A possibility to increase competitive price pressure on suppliers would be to introduce repair service auction systems.However, these have occasionally been met with strong resistance, as providers felt they were directly agitating the competitive balance within their trade. With digital, real-time tracking of transactions, insurers can still benefit from the transparent cost control and integrated price comparison, without explicitly provoking the competitive equilibrium between repair providers.These solutions do not only comprise comprehensive supplier management portals that allow for enhanced information sharing(e.g. digital transfer of documents, functionality to track vendor information and repair progress, integration with supplier scheduling tools), but also functionality to support the control of vendor performance (e.g. tracking of performance and costs, scoring mechanisms to rate and rank vendors). Whilst the digitalisation of vendor relationships allows for an increase in accuracy through reduced frictions in communication and collaboration, the digitalisation of invoices can significantly improve the understanding of cost structures and the speed of validation, with first solution providers also offering integrated databases of historical invoices, fees, and costs of spare parts in order to help insurers better assess invoices.

Settlement and closure.As improvement of claims leakage protects customers against underpayment, it will in turn result in a reduction of litigation potential and by that, reduce litigation costs. What’s more, insights garnered from data-driven claims handling can be leveraged to automate reserve setting processes and increase control; with a greater degree of automation allowing for multiple benefits, for example running the full reserving process flexibly whenever required, removing human subjectivity from the process and easily updating statistical models (e.g. reflecting variables like changed jurisdictions, wage adjustments, changes in the mix of business).Likewise, enhanced predictive models could potentially help in detecting otherwise obscure trends and correlations across underwriting or accident years.

Taking advantage of the opportunity

Overall, the application of technologies in assessing and processing claims opens up the opportunity to reduce leakage and increase the control of indemnity spend without adding complexity for claimants to the claims process, mitigating the traditional trade-off between claims effectiveness and customer-satisfaction.In order to leverage these opportunities, the challenge remains to digitise claims information and to start applying intelligence to the process of claims assessment and fraud detection. However, even a small improvement to the control of claims indemnity expenses can have a significant impact on an insurer’s end result.The void between As-Is models and saving potentials is far too great to ignore,rendering the need to review claims procedures and assessing them for optimisation potentials paramount for future profitability.