Analytics in Insurance Value Chain
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The document discusses the critical role of analytics in enhancing the insurance value chain amidst industry consolidation, evolving distribution channels, and a focus on customer relationship management. It highlights the importance of business intelligence tools and best practices for leveraging analytics to improve decision-making, client insights, and risk management. The conclusion underscores the need for a cultural shift towards data-driven decision-making to fully capitalize on analytics in the insurance sector.
Analytics in Insurance Value Chain
- 1. www.niit-tech.com NIIT Technologies White Paper Analytics in Insurance Value ChainAnalytics in Insurance Value Chain Surekha Sugandhi Insurance Practice - Solution Architect
- 2. CONTENTS Insurance Industry Overview and Major Trends 3 Business Intelligence and Insurance Value Chain 3 Insurance and Analytics: Current state of the art 4 How can analytics help build insurance value chain? 4 Best Practices for leveraging business analytics in insurance sector 5 Conclusion 6
- 3. industry have exponentially increased the importance and complexity of an effective business intelligence environment. Growing Consolidation: Consolidation is a major force altering the structure of the insurance industry, as insurers seek to create economies of scale and broaden their product portfolios. The aggregated value of mergers and acquisitions was $75.7 billion in 2010, up from $ 41.7 billion in 1999 and a mere $8.5 billion in 1993. Convergence of Financial Services: Mergers and acquisitions involving insurance companies and financial service providers, such as banks have led to the emergence of integrated financial services companies. New Distribution Channels: New distribution channels are fast catching up with traditional insurance agents. These channels, though, not a major threat, are rapidly changing the way insurers and clients interact with each other. Focus on Customer Relationship Management: The only viable strategy for insurers, today, is to focus on customer needs and serve them better. Clients have extremely differentiated needs with different profitability. Hence, an effective CRM strategy is the most vital component of an insurer's overall business strategy. Insurance Industry Overview and Major Trends Insurance industry is totally dependent on the ability to convert raw data into intelligence - about clients, markets, competitors, and business environment. Over the years, data processing technology has progressed phenomenally and tools such as data warehousing, OLAP and data mining that constitute the cornerstone of an effective Business Intelligence (BI) environment are today widely accepted across industries. However, insurance companies have been relatively slow in adopting these tools, primarily because of protective regulations. Now they can no longer afford to be complacent as the Internet, deregulation, consolidation, and convergence of insurance with other financial services are fast changing the basics. The insurance industry is quite diverse in terms of product portfolio offered by different companies. These can be broadly classified into two product lines: Property and Casualty (P&C) and Life Insurance. Life insurance product line can be further sub-divided into life insurance, health insurance and annuity products. Growing consolidation and changes in regulatory framework have forced insurers to add new products to their portfolio. These changes have presented its own unique challenge of leveraging its greatest asset - data. A number of other trends in the insurance Business Intelligence and Insurance Value Chain In the last three decades, insurance companies have acquired significant product development capabilities. However, they failed to truly understand clients’ needs and demands. This led most firms to rather develop products that they could manage than, those their clients required. Moreover, during the last few years, deregulation and growing competition have forced insurance companies to move from traditional product-centric operations to customer-centric operations. 3 Analytics is a two-sided coin. While on one side, it uses descriptive and predictive models to gain valuable knowledge from data, i.e. data analysis, on the other side, it provides insight to recommend action or guide decision making, i.e. communication. Thus, analytics is not much about individual analyses or analysis steps as it is about the entire methodology. Today, there is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. generic text mining to emphasize this broader perspective.
- 4. 4 Insurance and Analytics: Current state of the art Introduction of business intelligence software resulted in the evolution of computing in the insurance industry from a tactical and transaction focus to a strategic and business planning focus. This does not mean that transaction processing has faded from the scene or diminished in importance. Rather, it means insurers still process billions of transactions every day in sales, service, and claims arena. They perform basic data processing and appear competitive only when they efficiently handle large transaction volumes. However, for insurers, efficiency is only one aspect of the winning equation. To compete successfully and profitably, insurers must identify and act on emerging trends, new customer insights, and improve understanding of natural and man-made hazards. In addition, insurers need the ability to spot operational issues and opportunities in real-time to respond proactively. Fortunately, this is possible with two new classes of software known as business intelligence and advanced analytics. Currently, insurers use any of the two software’s with the ability to create dashboards and scorecards, conduct what-if analyses, leverage scenario planning, employ advanced statistical analyses, harness data/text mining, as well as uncover new opportunities from predictive models. These technologies, combined with human experience and insights, are already giving leading insurers advantage in the marketplace. How can analytics help build insurance value chain? Leading organizations use analytics to drive important decisions and progressively build their analytics capability. Assessing the maturity of skills, insurance companies design and technology capability against current and future needs will guide your priorities and planning process. Choose your strategy carefully • Grow client profitability by looking at your own information from a client perspective. Use digital and social media to identify high potential clients, their behaviors and preferences. • Use this information to define client’s experience strategies and implement initiatives that will delight your priority clients and attract new high potential clients. • Continuously monitor and re-evaluate clients’ potential, risk attributes, situation and environment to test on-going validity of segmentation. As circumstances change, this information will help companies in retaining a realistic view of client profitability and risk. So, many additional opportunities exist for insurers to further capitalize on today’s business intelligence and advanced analytics solutions. Figure 1: Policy and Claim Life Cycle *Source: Celent, Forrester, Innovation Group CLAIMSPOLICY Core Solutions Core Solutions Sales Quotations Segmentation Lifecycle Workloads Cancellation Renewals Recoveries Settled Claims Fraud Lifecycle Supply Chain Repairs FNOL
- 5. • Mine data in a risky environment to understand how market and credit events are related and use it for funding plan and for reducing emergency funding at punitive rates. Develop highly relevant and attractive products and service offerings • Use client insight to develop highly relevant and attractive products and product bundles for specific customer segments or individual customers. Along with these products, organizations must develop an effective pricing strategy to maximize delicate risk reward balance. • Harness more sophisticated, risk-based pricing to introduce products that otherwise would have been too risky to develop at the right price. Generate quality leads • Embed intelligence about your clients in your distribution strategy to generate quality leads. This should be performed for clients that have a high propensity to buy and determine the most effective distribution channel that cost effectively captures their business. • Improve your risk culture by profiling employees for mismatches in risk profile required by the role. • Identify and monitor leading risk and profitability indicators across distribution network to detect poor selling practices. It will help to refine your distribution strategy. Track client behavior • Build digital records about your clients, their behaviors and preferences to develop effective loyalty programs and retention strategies. These digital records will make it difficult for other insurers to attract your highly valued clients 5 Best Practices for leveraging business analytics in insurance sector Profitable growth is an elusive goal in today’s increasingly competitive insurance industry. Rapid development and deployment of new products and its features, balancing broader distribution channel opportunities, managing risks across organization, responding to regulatory and reporting agency demands, and providing precise pricing levels require effective decisions to be made with greater accuracy, efficiency and transparency. Personal experience is often insufficient in making consistent, accurate and effective decisions in line with rapidly changing marketplace. Leading organizations are increasingly turning to business analytics for survival. Business analytics solutions are used by insurers to reduce the time required to react to competitive pressure, respond efficiently to market changes, increase effectiveness of business managers in improving financial results and driving value for organization, to more effectively managing risks an enterprise face to improve precision and efficiency of operational decisions. The primary forms of business analytics used by the industry leaders include: Ad Hoc Management Reporting and Dashboards: This business analytics solution use analysis and reporting tools to provide automatic feedback on achievement of key performance criteria. • Analyze customer interactions and channel choices to improve customer service and deliver new service to sale opportunities. This data will also reveal opportunities to reduce cost by eliminating services your clients do not value.
- 6. They are also used to create ad hoc reports using data from a variety of data sources in order to improve management’s ability to make better and faster decisions. Common examples include claim reporting and settlement lag time, call center response times, and achievement of service standards, etc. Profiling and Segmentation: These business analytics solutions involve data mining to determine historic behaviour of a group, or performance of a group of people, risks or transaction types. Common examples include clients by profitability, claim types by severity or frequency, and clients by product preference, etc. Forecasting: This business analytics solution allows an insurer to attempt and determine a time series estimate of what will happen in future based on statistical evaluation of current and historic aggregate data. 6 Conclusion Insurance industry is divided in its adoption of business intelligence environment based on technologies such as data warehousing, OLAP and data mining. Quite a few insurance companies are in advanced stages of their business intelligence initiative; yet there are many oblivious of its benefits. Some insurers have gone for non-scalable temporary solutions, which often fail to leverage the ever-increasing volumes of data. Predictive Analytics: This business analytics solution attempts to predict future behavior or performance based on analysis of historic transactional data, third party data (like loss history, motor vehicle, geo demographic data, credit data, etc.) or derived data often calculated from one or more data elements. The analysis often results in a score or recommended action assigned during the processing of a transaction. Examples include determining the loss ratio relativity of a risk being underwritten, pricing adequacy based on anticipated loss experience, propensity of fraud on a reported claim, etc. Optimization: This business analytics solution focuses on optimization of business decisions usually based on multiple scenarios or multiple predictive analytics models. For insurance, optimization is always constrained optimization. Example includes maximizing response to a direct response campaign constrained by marketing budget. DataInsightRequired Business Value Derived AD HOC Reporting Dashboards Profiling and Segmentation Forecasting Predictive Analytics & Scorecards Optimization Figure 2 : Business Value Derived at each stage Source: www.sas.com By combining analytics expertise with business knowledge, insurance companies can uncover the real cause of toughest problems, and anticipate and identify future opportunities to differentiate and grow business. However, it is not enough to capture, integrate and analyse data. Enterprises must also act on what they find. This requires a culture that is ready to embrace novel and counter-intuitive ideas. Unless leadership sets tone by expecting data-driven decisions and encouraging ‘test and learn’ experimentation, analytics will remain a much talked about subject, rather than a core strategic capability.
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