Insurance Analytics Pathways for 2015 and Beyond Karen Pauli Research Director CEB TowerGroup Scott Horwitz Senior Director FICO © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Agenda ►Welcome ►Business Drivers ►Analytics and Data ►Analytic Spend and Adoption ►Questions 2 © 2014 Fair Isaac Corporation. Confidential. Meet Karen Pauli, CEB TowerGroup Karen is a Research Director in the Insurance practice at TowerGroup. She covers a wide range of topics in property and casualty insurance, specializing in distribution, underwriting, claims, predictive analytics, core systems, and business optimization. 3 © 2014 Fair Isaac Corporation. Confidential. Insurers are looking for every advantage they can get to remain competitive and compliant, and analytics are a key part of their arsenal. 4 © 2014 Fair Isaac Corporation. Confidential. ROADMAP FOR THE PRESENTATION Business Drivers © 2014 CEB. All Rights Reserved. Analytics and Data Analytics Spend & Adoption 5 INSURANCE BUSINESS, STRATEGIC, AND TECHNOLOGY PRIORITIES Business Drivers Strategic Responses • Democratize the operationalization of the voice of the customer • Build a holistic enterprise-wide data strategy • Rationalize IT portfolio to align to agile sourcing strategy • Define standards for favoring agility over precision • Redefine traditional insurance roles and structures • Evolving individual sales and service expectations • Changing distributor business models • Contentious scope and authority of insurance regulators • Global dependence on volatile regional economies • Intensified competition for critical skill sets Top 10 Technology Initiatives for Insurance Life & Annuity and Property & Casualty Create a horizontal enterprise analytics and models layer Intermediate IT and business cloud utilization Facilitate real-time decisioning with collaboration technology Top Life & Annuity Technology Initiatives for Insurance Manufacture risk solutions with integrated actuarial systems © 2014 CEB. All Rights Reserved. Optimize the value of CRM across diverse distribution channels Integrate consumer and distributor portals with back-end technology Leverage increasing variety of core system delivery options Create a device-agnostic mobile infrastructure Top Property & Casualty Technology Initiatives for Insurance Integrate big data streams into day-today operations Expand telematics applications beyond personal auto Source: CEB Analysis 6 INSURANCE BUSINESS, STRATEGIC, AND TECHNOLOGY PRIORITIES Business Drivers Strategic Responses • Evolving individual sales and service expectations • Changing distributor business models • Contentious scope and authority of insurance regulators • Global dependence on volatile regional economies • Intensified competition for critical skill sets Top 10 Technology Initiatives for Insurance Life & Annuity and Property & Casualty Top Life & Annuity Technology Initiatives for Insurance © 2014 CEB. All Rights Reserved. Top Property & Casualty Technology Initiatives for Insurance Source: CEB Analysis 7 EVOLVING INDIVIDUAL SALES AND SERVICE EXPECTATIONS Consumers’ expectations for sales and service are changing, exemplified by a rapid change in channel preferences. Channel Preferences for Using or Accessing Financial Products and Services North American Consumers, 2010 and 2013 53% +8% 45% 34% 30% -4% Agent Online 2010 2013 2010 n = 1,850; 2013 n = 2,713 Source: CEB 2011 and 2013 Customer Experience Surveys © 2014 CEB. All Rights Reserved. 8 CHANGING DISTRIBUTOR BUSINESS MODELS Distributor business models are shifting and show an increase in the specialization of independent agents and brokers. Agency Specialization Percentage of Survey Respondents Reporting an Increase in Specialization, by Revenue Group, 2010 and 2013 80% 73% 72% 64% 56% 68% 64% 56% 51% 44% 39% 25% <$1.25 million $1.25–2.5 million $2.5–5 million $5–10 million 2010 2013 $10–25 million >$25 million Source: IIABA’s 2013 Best Practices Study © 2014 CEB. All Rights Reserved. 9 CONTENTIOUS SCOPE AND AUTHORITY OF INSURANCE REGULATORS Insurers feel the strain of conflicting regulatory bodies in the U.S. and internationally. Global Insurance Regulation Illustrative Example, Solvency Regulation Requirements, U.S. Impact State Federal International RMORSA Risk Management and Own Risk Solvency Assessment Model Act FIO Modernization Report Released December 2013 Solvency II Implementation date: 2016 State law requiring insurers to implement an enterprise risk management framework by January 2015 Includes recommendations for: • Material solvency oversight decisions of a discretionary nature • Improved consistency of solvency oversight at the state level Risk-based approach to capital requirements for insurers, three pillars: 1) Quantitative risk-based capital requirements; 2) System of governance; 3) Supervisory reporting and disclosure of information Source: CEB Analysis © 2014 CEB. All Rights Reserved. 10 COMPETITION FOR CRITICAL SKILL SETS INTENSIFIES A global scarcity of skill sets drives competition amongst all global industries for talent, and significantly impacts insurers’ ability to attract and retain top talent. Q: How concerned are you about the availability of key skills as a business threat? Percentage of Respondents, 2011 and 2012 2011 2012 13% 10% 33% 31% 39% 41% 15% 17% Not at all concerned Not very concerned Somewhat concerned Extremely concerned n = 1,330 Source: PwC 15th and 16th Annual Global CEO Surveys © 2014 CEB. All Rights Reserved. 11 GLOBAL DEPENDENCE ON VOLATILE REGIONAL ECONOMIES International financial markets are now tightly interconnected, and, as economies fluctuate, insurers of all sizes with insured entities and supply chain dependencies spread across the globe face a significant risk management challenge. Fluctuations Across Economies Percent Change in GDP over Corresponding Period of Previous Year, Advanced and Emerging & Developing Economies, 1998–2012 10% 5% 0% -5% 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Advanced Economies Emerging and Developing Countries Source: IMF Economic Interdependence US Impact of 2011 Thailand Floods, Illustrative Thailand Floods, 2011 Total Insured Loss (USD millions): $15,315 Decrease in Thailand manufacturing output due to factory closures Impact on US Business: Technology and Auto Manufacturers & Suppliers Approximately 1,007 (billions THB) in economic losses in manufacturing Hewlett Packard: 3.5%+ decline in 2011 revenue Ford: $80 million loss Source: Insurance Information Institute, Bloomberg, Aon Benfield © 2014 CEB. All Rights Reserved. 12 ROADMAP FOR THE PRESENTATION Business Drivers © 2014 CEB. All Rights Reserved. Analytics and Data Analytics Spend & Adoption 13 MORE INFORMATION, MORE INFORMATION WORK “Big data” is quickly becoming a reality as information volumes grow by 60% annually, and 36% of all work time is devoted to information collection and analysis. Estimated Rise in Global Data Volumes, 2010–2015 Indexed to 100 1,200 1,050 660 600 0 410 100 160 2010 2011 260 2012 2013 2014 2015 Source: CEB analysis. Time Spent Collecting and Analyzing Information Percentage of Total Knowledge Worker Work Time 64% All Other Work 36% Collecting and Analyzing Information n = 4,941 knowledge workers. Source: “All Too Much” The Economist, 27 February 2010; CEB analysis. © 2014 CEB. All Rights Reserved. Drivers of Democratized Decision Making Decisions are made closer to the market (e.g., product design, channel mix). 2 Decisions are more dynamic and varied (e.g., demand forecasts, discounts). 3 Knowledge workers have access to more information and better tools (e.g., customer segmentation and value analysis). 1 Source: CEB analysis. 14 BIGGER DATA, BIGGER NOISE As “big data” gets bigger, it becomes harder, not easier, for employees to extract truly valuable insight from it. High Data Big Data Signal to Noise Ratio Low Yearly Data Low Quarterly Data Daily Data Hourly Data MinuteWise Data Volume of Data/Frequency of Data Observations Source: Taleb, Nassim, “Noise and Signal—Nassim Taleb,” Farham Street Blog, 29 May 2012, http://www.farhamstreetblog.com/2012/05/noise-and-signal-nassim-taleb/. © 2014 CEB. All Rights Reserved. 15 REPORTING AND ANALYTICS MATURITY Only 7% of insurance executives report having a mature and optimized process in place for reporting and analytics. How would you assess your company’s maturity level in reporting and analytics? Percentage of Respondents, 2013 Reporting and Analytics: Reporting refers to the process of converting data into a normalized, structured, and actionable representation. Analytics refers to the systematic processing of data or statistics to produce insights supporting a business decision. 37% 31% 16% 10% 7% Initial (chaotic, ad hoc) Repeatable (a documented process) Defined (defined and confirmed standard process) Managed (process Mature/Optimizing is quantitatively (deliberate process managed with with optimization agreed upon and improvements) metrics) n=257 Source: CEB 2013-2014 FSI Technology Survey © 2014 CEB. All Rights Reserved. 16 Over 50% of executives attribute high importance to all analytics functions, but their confidence in execution is low. IMPORTANCE OF ANALYTICS FUNCTIONS DOES NOT MATCH EXECUTION CONFIDENCE Importance and Confidence in Execution Attributed to Analytics Functions Percentage of Respondents, 2013 Importance 70% 64% 62% 61% Confidence 61% 61% 60% 57% 54% 50% 40% 30% 20% 23% 23% 26% 19% 20% 20% 17% 10% 0% Improving Risk product or management service profitability © 2014 CEB. All Rights Reserved. Forecasting demand for products, services, resources (e.g. pricing analytics) New market Product or Developing a Evaluating identification service corporate or and and market development business unit prioritizing strategy strategy investment development proposals Importance question: How important are each of the following analytics functions to your company’s operations? Confidence question: How much confidence do you have in your company’s ability to perform the following analytics functions? n = 257 Source: CEB 2013-2014 FSI Technology Survey 17 CENTRALIZE MANAGEMENT, NOT INFORMATION Foundational analytic and information management activities benefit from centralization and create sufficiently strong oversight to sustain decentralized information sources. Maximum Impact on Insight IQa of Centralized Models for Information Management 14.0% 13.0% 10.5% 10.4% 6.9% 7.0% 6.7% 0.0% 0.0% Centralized Centralized Analytics Knowledge Team Worker Training Centralized Centralized Centralized Information User Support Information for Analytic Architecture Quality Tools Organizations with a high insight IQ centralize information management and support activities… © 2014 CEB. All Rights Reserved. 0.0% Single Single Data Unstructured Warehouse Information Repository …which enables them to keep the information itself decentralized. n = 4,941 knowledge workers. a: The maximum impact on Insight IQ is calculated by comparing two statistical estimates: the predicted impact when a knowledge worker scores relatively “high” on a driver and the predicted impact when a knowledge worker scores “low” on a driver. The effect of each driver is modeled using a variety of multivariate regressions with controls. 18 Source: CEB 2011 Insight IQ Diagnostic. CREATE A HORIZONTAL ENTERPRISE ANALYTICS AND MODELS LAYER As critical mass in analytics is reached, insurers need to abandon their siloed approach to analytics adoption and integration and aggregate analytics tools into a horizontal layer of analytics and models that are utilized enterprise-wide. Analytics Adoption and Replacement Siloed Approach, Prior Years Historically, insurers had a siloed approach to analytics adoption and integration Marketing and Product Development Distribution and Sales Policy Administration Analytics Tools Analytics Tools Analytics Tools Claims Processing Infrastructure/Support Analytics Tools Analytics Tools Integrated Approach, 2014 In 2014, insurers need to create a horizontal layer of analytics and models tools that are utilized across the enterprise Marketing and Product Development Source: CEB Analysis © 2014 CEB. All Rights Reserved. Distribution and Sales Policy Administration Claims Processing • • • • Infrastructure/ Support Horizontal Enterprise Analytics and Models Layer Predictive Analytics Operational Analytics Consumer and Marketing Analytics Pricing Optimization 19 ROADMAP FOR THE PRESENTATION Business Drivers © 2014 CEB. All Rights Reserved. Analytics and Data Analytics Spend & Adoption 20 SPENDING ON PREDICTIVE ANALYTICS Forty-three percent of insurance firms expect spending on predictive analytics technology to increase in the next 2 years. Expected IT Spend Change by 2015 Percentage of Respondents, 2013 43% 21% 21% 14% Decrease Little or no change Increase Unsure n=67 Source: CEB 2013-2014 FSI Technology Survey © 2014 CEB. All Rights Reserved. 21 STATE OF TECHNOLOGY: PREDICTIVE ANALYTICS Forty-one percent of insurance firms intend to adopt or replace the technology before 2018. State of Technology Current State of Technology Implementation by 2018 Percentage of Respondents, 2013 39% Definitions Does not have it My company does NOT use technology in this area and DOES NOT intend to install the technology by 2018. Adopting Until recently, my company had no technology for this area, but HAS adopted the technology in the past 12 months or WILL by 2018. Have it, no change My company has technology in this area, DID NOT make a major system replacement in the past 12 months, and WILL NOT before 2018. Replacing My company has had technology in this area for over a year, and DID make a major replacement in the past 12 months or WILL make one by 2018. 25% 16% Replacing © 2014 CEB. All Rights Reserved. Adopting Have it, no change 9% 10% Does not have it Unsure n=67 Source: CEB 2013-2014 FSI Technology Survey 22 PREDICTIVE ANALYTICS VERY HIGH OR HIGH VALUE; COMPETITIVE ADVANTAGE Forty percent of insurance firms affirm that predictive analytics technology provides high or very high value to their company, which is primarily due to the innovative new insights these tools provide. Technology Value for Company 40% 27% 24% 9% 0% Very low or low value Somewhat low value Moderate value Somewhat high value Very high or high value Value Drivers 38% 26% 18% 10% 8% 0% Financial return on investment Functionality Process improvement Competitive advantage Ongoing costs Enhancement of and client value maintenance n=67 Source: CEB 2013-2014 FSI Technology Survey © 2014 CEB. All Rights Reserved. 23 PREDICTIVE ANALYTICS MODERATE RISK; INTEGRATION COMPLEXITY Thirty-nine percent of insurance firms affirm that predictive analytics technology poses only moderate risk to their company. Technology Risk for Company 39% 26% 26% 7% 2% Very low risk Low risk Moderate risk High risk Very high risk Risk Drivers 37% Integration complexity 24% 20% 18% Risk of catastrophic failure Information security risk Dependence on specialized resources n=67 Source: CEB 2013-2014 FSI Technology Survey © 2014 CEB. All Rights Reserved. 24 Questions 25 © 2014 Fair Isaac Corporation. Confidential. 26 Thank You! Scott Horwitz [email protected] Phone © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Learn More at FICO World Related Sessions ►Product Showcase: Multichannel Communication Solutions for insurance ►Putting the Brakes on Fraud, Waste and Abuse with SulAmerica Products in Solution Center ►FICO® Identity Resolution Engine Experts at FICO World ►Scott Horwitz ►Nitin Basant White Papers Online ►FICO Gartner Newsletter: New Strategies for Fighting Insurance Fraud Blogs ►www.fico.com/blog 27 © 2014 Fair Isaac Corporation. Confidential. Please rate this session online! Scott Horwitz [email protected] 28 © 2014 Fair Isaac Corporation. Confidential.