Keeping in Step With Strategic Business Objectives in Insurance through Analytics

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Information about Keeping in Step With Strategic Business Objectives in Insurance through...
Business & Mgmt

Published on March 1, 2014

Author: VijaiJohn


March 2014 Keeping in Step with Strategic Objectives through Analytics help every carrier benchmark and improve their performance.  Insurance has driven statistical analysis based on parameters that have been hard to quantify. Risk management has become a highly refined science, and has benefitted carriers and customers alike due to highly accurate technologies that detect fraud, minimize adverse selection, keep prices competitive and defines the risk appetite and tolerance levels in each company.  Unlike Retail, Manufacturing, Hospitality or even banking, Insurance products are not tangible. The payoff comes as a result of a sustained loss. This has meant that the core product in Insurance has been always a financial construct based on a future event, and is therefore the result of complex algorithms that compute pricing, risk, historical loss, fraud, reserving and other factors. Along with technologies that address day to day operations, this core product has been largely a beneficiary of high automation, minimizing human error. Indeed, human error has not been eliminated, and is a big part of operational risk, which is constantly being addressed by ever-changing regulation and selfpolicing. Vijai John Along with Banking, Financial Services and Capital Markets, no other industry may have received the attention of myriad groups of people- administrators, data scientists, strategists, economists, academics, technologists, efficiency gurus, customer experience experts, legal experts and many others- than the Insurance industry. An industry that has always placed its foundation on weighing the possible negative outcomes against the willingness of customers to hedge their risks, Insurance has relied on and refined statistical analysis, customer behavior, fiscal prudence, technology and operations over centuries. This industry has also seen heavy regulation globally, addressing several aspects of its functions and the risks they entail. The industry has also largely undertaken to set up standards for itself through associations that work closely with the regulatory authorities. In this process of adherence to regulation and self-policing, many aspects of this industry have reflected refinement that exceeds that of others. Some key components of this refinement have been brought about via the following channels:  1 Key data relating to operating performance, risk, finance and investments are reported to regulatory authorities, which are in turn shared with private players who analyze such data to derive meaningful insights that For an industry that plays in the DMZ between the fears of customer about sustaining a loss and the reassurance of a universe of policyholders which has historically held up in most time-tested product lines as being a bulwark against total ruin, it may be surprising that Insurance is itself always driven by the bogey of risk. Nearly all technological or operational change in Insurance has been driven by the desire to avert risk. Business process improvements aimed at speed to market, customer retention, pricing accuracy

March 2014 and other outcomes have their basis in minimizing risk. A host of analytics solutions have been aimed at addressing the data that has been gleaned from Insurance operations. Most analytics in Insurance have focused on the following categories of operations:   customer data, an understanding of win-back activities for customer who are about to lapse, focusing on the lifetime value of customers and reducing the cost of servicing customers can mean improved retention and lower loss ratios. Distribution: Whether via push (agents and brokers) or pull (digital) strategies, distribution has relied on accuracy and speed in analytics. Social Media: This source of data has strengthened real time analytics and enabled sophisticated hypothesis in specific use cases. INVESTMENTS:  ATTRACTING, RETAINING, BENCHMARKING AND RATIONALIZING CUSTOMERS:     2 Customer Segmentation: Advanced customer analytics can help identify overlooked customer segments or redefine the risk profiles of customers. Identifying the right customers: Not all customers are equal. Use predictive analytics in Marketing to direct sales efforts to only customers within a defined risk tolerance level. Analyzing marketing campaigns: Analytics and reporting tools provide a clear picture of marketing campaigns across the enterprise, response rates, types of responses and conversion rates. Cross and up sell: While cross selling and upselling have always needed more than just customer analytics, this aspect of sales has caught the attention of service providers and technology vendors powerfully. Besides the ready and timely availability of product and   Portfolio Management and Optimization: From pricing an asset in relation to its market risk to optimization a portfolio of assets, analytical models that use available data have proven invaluable to Insurance companies. Minimizing Risk of Liquidation: Analytical tools have made it easier to posit growth and expenses associated with an asset and therefore schedule asset sales and payments appropriately. Financial and Probability Modeling: Mergers and Acquisitions, Capital budgeting, capital-intensive projects or investments have all been enabled via financial modeling. Although traditionally based on spreadsheets, new tools have made these tasks quicker and more accurate. STAFF FUNCTIONS OR SHARED SERVICES:  Human Resources: Talent Analytics has been making news. There have been several surprises relating to employee performance, retention and lapses based on data points that had not been thought of earlier.

March 2014   Corporate IT: IT Performance Management has come of age. This function has been slow in changing in many enterprises due to the myriad responsibilities that a CIO faces. Slowly but surely many IT organizations are adopting the best practices around program management, outcome-based tracking of services, adoption of ITIL and other guides to streamline performance. All of these are strongly backed by reporting tools that have transformed organizations. Corporate Finance: Financial close, budgeting, forecasting and financial statement analysis have all benefitted from analytical tools that have been designed to make the process faster and accurate.    OPERATIONAL RISK:    3 Product Development: Analytics have been crucial from design through development and deployment of new products, their success hinging on an accurate assessment of needs and speed to market. Price Optimization: Besides pricing, price optimization analytics has been useful in deriving negotiating points with customers and prospects through changes to cover and price discounts. Underwriting: Recent advances have enabled underwriters to focus closely on policyholder characteristics which had been overlooked in the past. A recent report focused on how the 2009 recession had forced many people into making choices that would be considered less than ideal for an underwriter to issue a homeowners policy. However, due to the nature of the circumstances, possibly compounded by a job loss, such an incident may have been an aberration. Underwriters have turned to several methods, including analytical tools that  provide more nuanced insights to identify creditworthiness. Telematics: Analytics based on driving habits and patterns have enabled carriers to design usage-based insurance policies. Catastrophe: The incidence of highly impactful catastrophes in the first decade of the 21st Century has given fresh impetus to CAT modeling. Many carriers have now turned to risk by peril models for homeowners and commercial property insurance. Fraud Fighting: Analytics in fraud detection has also gained ground in the recent years. Largely, carriers have fought fraud mainly due to the diligence of experienced adjustors who could recognize patterns clearly. Much of this knowledge has now been transferred to analytical frameworks which are constantly improving. Reserve Development: Historic data, claims analytics and predictive modeling based on multiple parameters have made it possible to predict probabilities for loss more accurately. Not surprisingly, an examination of the stated strategic objectives of several Insurance carriers has reflected improvements in the above categories. One of the largest personal lines carriers in the US states its operational objectives as follows:      Customer focus Operational excellence Enterprise risk and return Sustainable growth Capital management While such statements are qualitative in nature, a closer look at activities that are filtered down as a result of these reveals that analytics drive changes in these enterprises.

March 2014 When considering a project at a carrier or planning to sell to a carrier, it is useful to keep a high-level goal in mind. Customer Retention could be the goal for any number of customer focused activities. This approach from the top presumes that tactical goals can wait if they do not substantially impact the clearly metric-driven objectives that the enterprise would embrace. To improve customer retention any of the following, more tactical activities would help.    Contact Center Efficiency Optimization Customer Experience Management by tracking NPS scores and identifying pitfalls in the customer engagement process Improved technology that provides greater visibility into customers for agents, sales, service and support personnel However, without a determined effort to track data back to the outcome of Customer Retention, these efforts will remain tactical. Indeed, for an executive to justify the business case for such engagements would mean an understanding of ultimate financial gains and a significant minimization of risk. Improving speed and accuracy in underwriting has obvious benefits in a competitive market. Improvements in process, technology or operations can often create a differentiator in this area. As with other areas, this too is a candidate for benchmarking for risk and return. In Insurance a distinction of functions into cost and profit centers is often simplistic, as cost centers are often overlooked revenue drivers. The availability of data from the industry and the systematic measurement of a carrier’s internal data should mean that the industry must benefit from a solid strategic perspective this provides. However, this is far from being the norm. 4 Our analysis of reported data reveals specific opportunities to improve in carrier’s operating performance. A ranking of product lines by underwriting risk reveals the risk appetites of carriers based on their product portfolios and their exposure to specific products. Geographic analysis (state-wise) of carriers’ key loss and expense ratios illustrates their ongoing performance, and may point to the prevalence of fraud and the efficacy of a carrier’s fraud detection efforts. Multivariate analysis based on different parameters validates these assumptions. While a carrier may have sophisticated tools for fraud detection, an understanding of industry trends strengthens this effort, and may help uncover fraud at the inception of a policy as well as fault-lines in loss adjustment, underwriting practices and marketing. Similarly, our analysis of underwriting performance of over 250 carrier groups in homeowners insurance reveals categories of carriers experiencing different levels of loss ratios. Besides fraud, this may reveal an opportunity for automating part of the underwriting process. This data led our Property and Casualty solutions team to create a solution to automate homeowners insurance, cutting down on the time and increasing pricing accuracy based on data that is now available via high resolution pictures and feeds from other sources relating to home details. The solution, structured on a rules-based framework, is among a new breed of solutions seeking to upend the manual process that has been the norm for long. The Insurance industry, as already mentioned, is self-policing. The improvements that are being made are in response to changing threats and new opportunities that are presenting themselves rapidly. Due to the availability of enormous amounts of data as well as new technologies it has become possible to manipulate this data into outcomes that are derived from strategic objectives. An

March 2014 architecture that begins from these outcomes can go a long way towards deriving value from tactical initiatives in technology and operations. For more information, please contact: Vijai John iGATE (630)335-1808 5

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