Better Data Part I: Be specific

  • RiskAs stated above, an insurance company has two sides to its business; a liability side consisting of the financial commitments made to policy holders in the event of a loss and an asset side consisting of the financial investments made with policy dollars. These investments total billions to trillions of dollars and span multiple asset classes.

    From a risk management perspective, the assets of an insurance company need to be managed based on credit, liquidity and market risk in the same manner as any buy or sell side institution.

    From an asset liability management (ALM) perspective, liabilities need to be matched to assets based on duration to ensure that investment are sufficient to meet payout commitments. Liabilities, Positions, Transactions, Derivative Trades and related reference data that includes asset master, party (Counterparties, Issuers, etc.), and product / organisation hierarchies are the common building blocks for these processes and require an enterprise-wide view.

    In order for ALM and Risk management processes to work correctly, these building blocks need to be gathered horizontally from multiple front office trading systems and accounting systems within an organisation, and represented in a standardised way. This allows the data to be resolved to a consistent set of reference information and harmonised across an enterprise.

  • Operations and claims managementClaims history data for both customers and properties is maintained on a legacy basis for both policy costing and claims-based loss provisioning across the industry. Today, advances in data access and analytic technologies / processes allow much more effective management of both the operational and financial dimensions of the claims process. Leading insurers are working to gain an edge by integrating environmental data sources to help predict and model risk more effectively, and drive increased sophistication in both the pricing and evolution of insurance products. This is based on an increasingly complex set of factors that drive understanding of the potential for and magnitude of claims.

    In addition, across the industry, competitive pressures have helped to define non-product-based service dimensions as a source of differentiation in a crowded and increasingly commoditised product landscape. We see national mass advertising campaigns from some P&C providers touting claims management and ‘speed-to-resolution’ as a competitive feature to differentiate in the market. This has led to increasing pressure from consumers on automated claims settlement and payouts. The risk of overpayment or fraud in these circumstances is obvious; rushing to pay claims holds the potential for disaster, while quickly paying legitimate claims will retain a good customer for many years to come.

    The weight falls on the back office to maintain integrity in the process. Employing effective analytics and modelling, insurers can accurately impose limits on automated claims payouts, while efficiently determining which claims activities should be subject to further scrutiny and exception handling. Lastly, by integrating highly individualised mass data (e.g. social media streams), data-literate insurers are able to develop more accurate and effective means of identifying and limiting fraud at each stage of the claims cycle, and focus their efforts to find the largest returns.

  • Marketing and client managementThe evolution of the data landscape available to insurance marketers has also driven a significant transformation in both the approach that the industry takes to attract new customers and the methods that are available for servicing and selling to them. Evolving from a simple segmentation and targeting approach, leading insurers are employing new data tools to access unstructured data, combine it with social and behavioral data and integrate modeling and analytics practices to find and reach customers in a whole new way, often in real time.

    As both a source of data and a marketing-based client service platform, the integration of vast amounts of telematics data based on such factors as driving behaviour, time of day, location and a host of related elements, with psychographic data on likes, habits and preferences, can allow a marketer to develop an extremely granular understanding of the best ways to build products, price them and distribute them to micro-segmented clients in a range of cost effective and efficient communication and sales channels.

    Providers who have traditionally used broker distribution as a proxy for having mass personal knowledge on their clients and target markets are gaining an incentive to develop more sophisticated direct distribution models, substituting technology and data analytics for the ‘personal touch’. The counter-trend can also be seen; as the development of more effective electronic quoting, sales and distribution platforms evolve to provide a clearing house of products, insurance marketers are challenged to get to their target clients faster and more effectively than their competitors.

  • Applications and data complexityInsurance companies have grown over time by acquisition; however, typically this is done without integrating the business processes and systems of the two companies. The prevailing logic was that the same product can be administered by separate systems. As a result, the application and data environment is replete with complexity and redundancy. Not only is there inefficiency in terms of multiple applications and interfaces, but it is also highly likely that information is inconsistent between systems.

    A data strategy is an enabler for an application and data rationalisation programme. Benefits of a rationalisation programme includes reduction of software, hardware and operational costs. Effectively, limiting the number of applications and point-to-point interfaces results in an environment where complexity is reduced, thereby allowing businesses to more readily integrate information and processes in order to introduce new products, as well as change existing ones.

    Business capabilities that are optimised by reducing the number of applications, number of databases and point-to-point interfaces supporting a capability include:

      Product development
      Customer classification
      Sales and distribution channels
      Administering policies and contracts
      Undertaking billing and receivables for policies
      Claims management
      Investment management
      Electronic document management

 

Closing thoughts
The process of the insurance industry, as a whole, adopting advanced techniques and tools to meet the needs of the evolving marketplace is still in its early days. Insurers must work to adapt their traditional strengths in using data to define their business towards tuning their offerings and redefining their business, while simultaneously dealing with the practical issues that a complex data ecosystem brings with it. We hope that this list of real, tangible benefits will act as a strong starting point for making a business case to focus more resources on data governance. In future editions of this data mini-series, we will discuss how companies can begin implementing internal data governance, and how to ensure that analysis is bringing valuable, actionable insights.


Go to the second part of this series.

Find out more about the authors: Michael Engelman and Kevin Paul


GFT data governance downloads:

Data Governance Fact Sheet (download opens automatically)
Data Quality Infographic (download opens automatically)
Big Data White Paper (download opens automatically)

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