Insurers globally are preoccupied with customer acquisition and retention, achieving operational excellence, and regulatory compliance and risk management. These imperatives are driving a wide range of investments in technology, with a deep focus on data architectures, data quality, and how data can be used to assess the business operations of the carrier, for product development, claims analytics, compliance and reporting, and fraud detection. Insurers are maturing in their use of business intelligence (BI) and analytics.

• Regulation and compliance, risk management, customer persistency and operational efficiency are all priority focus areas for insurers globally, and are leading to increasing investments in data architectures, BI platforms and analytical tools.

• Web-based dashboards and reporting tools are increasingly being built into or integrated with core insurance applications and services. The consumerization of BI is driving new design and usability, so that the business user can access and customize reports easily, driving up usage. Also, a wider variety of analytic applications, addressing domain-specific needs in insurance, are being introduced to the market by vendors and service providers.

• BI is currently most widely deployed for performance management, but claims analytics is currently an area where many insurers state an intention to invest, and analytics for fraud detection is also an emerging area of opportunity. Going forward, we expect more widespread use of predictive analytics for areas such as risk modeling and pricing as the offerings available continue to improve, as well as customer retention and support of "upsell" opportunities.

• Regulations, such as Solvency II, and risk management in general are forcing insurers to take an enterprisewide view of risk, and an even-wider view of their data.

• Big data and Pattern-Based Strategies are the next frontier. In the future, insurance companies that leverage these disciplines to fully understand their insured and risks will gain competitive advantage.

• BI and analytics is high on the agenda of offerings and messaging of the key ESPs targeting the insurance sector in 2011.

ESPs targeting the insurance sector:

• BI and analytics must be an essential part of your story for the insurance market. Offering key performance indicators (KPIs) and dashboards from previous engagements in the sector as accelerators will get a foot in the door with clients, which may result in bigger projects later on.

BI and analytics vendors targeting the insurance sector:

• Need to stress their credibility in being able to be part of a mandated information infrastructure that meets increasingly stringent regulatory demands for the sector. Leverage new technologies such as in-memory, but prioritize working toward maintaining easy connections with enterprise data stores, metadata and business definitions.

• You need to continue working on usability, both from a graphical interface perspective and through embedding analytics deeper into existing transactional business processes.

Globally, insurers are preoccupied with regulatory compliance and risk management, customer acquisition and retention, and achieving operational excellence. These imperatives are driving a wide range of investments in technology, with a deep focus on data architectures, data quality, and how data can be used to assess the business operations and financial performance of the insurer, for product development, claims analytics, compliance and reporting, and fraud detection. Insurers are maturing in their use of BI platforms and analytics tools, especially as these tools mature and become available with usable interfaces.

BI and analytics is core to several key business concerns of insurers:

• Risk management and compliance — New regulations in many parts of the world are forcing increased IT investment among insurers. The most notable examples are those related to healthcare reform in the U.S. and the establishment of the Federal Insurance office, the Retail Distribution Review in the U.K., and Solvency II in Europe, as well as anti-money-laundering (AML) regulations and International Financial Reporting Standards (IFRS) in many regions. In addition to this increased regulation, insurers are dealing with fraud, which increased during the global financial crisis (GFC), and risks that continue to impact the insurance business, such as climate change and sustainability issues. These combined forces are leading many insurers to take a more holistic view of risk of the enterprise, which is resulting in investment in BI.

• The desire to increase customer persistency — Analysis of all aspects of customer interactions by leveraging the analysis across existing repositories of customer data held in policy and claims systems, marketing and customer support databases. Gartner research shows that many insurers are still struggling to achieve a 360-degree view of their customers, and part of the issue they are grappling with is the completeness or the accuracy of the data they have on customers across their various repositories. The GFC of 2008 and 2009 added to the complexity as acquisitions and consolidation happened in the insurance space, where larger vendors bought smaller players. The complexity of customer data in many sources is driving data integration, master data management (MDM) initiatives and BI.

• Performance management — Common to most organizations, but especially vital to post-GFC financial services organizations is a laserlike focus on operational efficiency and corporate performance. Business users want to know how the organization is tracking against its KPIs (e.g., what is the company's claims ratio, or how efficient is the insurer in its core activities?)

• Combating fraud — Reducing fraud becomes more vital the more competitive and mature an insurance market becomes. There is also evidence that fraud rises during times of recession, which has triggered recent interest in dealing with it. BI and analytics is a key tool in the detection and identification of insurance fraud, especially advanced tools that can analyze both structured and unstructured data in real time.

• Advanced actuarial capabilities: predictive analytics for pricing of risk — Predictive modeling has a significant impact on actuarial tasks and pricing, because it allows users to run models to test pricing of products and assess risks. P&C and life insurers are applying predictive modeling to actuarial data to help with product pricing, testing the effect of pricing changes, as well as to assess the adoption of new products to be launched.

Dashboards, reporting and analytics are being built into many apps as standard for things such as front office (agent or broker systems), insurance-specific CRM, claims management and policy administrative systems. The use of predictive analytics in insurance is also emerging.

The primary emerging trend is that the consumerization of BI is driving usability. Dashboards, reporting and analytics are being built into many apps as standards for things such as front office (agent or broker systems), insurance-specific CRM, claims management and policy administration systems. SAP and Oracle are two application vendors in the insurance sector that are increasingly embedding BI, for upsell/cross-sell opportunities, and to flank competition that lacks dominance in business applications. Insurance core software and business process outsourcing (BPO) provider, Innovation Group, has developed its acquired analytics capability, Insure Analytics, to come out of the box with more than 150 KPIs for claims and a similar number for policy and management reporting. Key to uptake is that these are Web-based and use graphical representations of the pertinent data.

BI and analytics is being marketed by ESPs as the focus of 2011, along with distribution (especially multichannel integration) and core system upgrades; BI and analytics cuts across all these areas. A growing trend is that ESPs are increasingly trying to reuse their domain-specific expertise through packaging it and reselling it as blueprints or as packaged analytic applications. In this way, they are able to address business problems and to access buying centers in the line of business that software vendors often do not have access to, and can sell consulting services around the product. BI and analytics has been identified as a core area of focus without exception by all those ESPs that have briefed Gartner on their insurance go-to-market strategy in 2011. Service providers with a strong focus on the insurance sector that are focusing on BI and analytics for the insurance sector include (but are not limited to) Accenture, Capgemini, Cognizant, CSC, Deloitte, HCL, IBM and Wipro. IBM is especially interesting because of its acquisitions of Clarity, Cognos, OpenPages and SPSS, which it is building into its insurance-specific middleware framework.

Many BI vendors are becoming more "verticalized," both through partnerships and through own initiatives. IBM is increasingly using its services arm to sell its software, and its industry go-to-market plan is built quite strongly on its middleware framework for insurance. SAS Institute recently partnered with Accenture, to co-develop and go to market with Analytics. Deloitte recently bought SaaS vendor, Oco, to get a software platform on which to build packaged content. In the insurance sector, this means more insurance-specific templates for claims analysis, fraud detection, risk management, customer value assessment and distribution performance.

Hence, domain-specific competition is heating up, and the lines are blurring between software, services and hardware.

On the demand side, we see a few longer-term shifts:

• Usability is spreading low-end analytics: Analytics are now being democratized, which means more self-service options are available to the (business) end user, who is less likely to have to wait for an IT-mandated static report to reach them. More tools are available to the business analyst directly, through data discovery tools (QlikTech, Tableau, SAS JMP and Spotfire are famous examples), packaged analytic content (prepackaged data models addressing a specific need), and analytics being worked into all kinds of business applications. While reporting is still by far the most widely used consumption form for BI and analytics, recent survey results indicate a major usage increase for dashboards and ad hoc querying functionality. This means that the mainstream consumer is moving along the continuum of "what has happened" to "why did it happen."

• Advanced analytics is moving from the basement to the boardroom: More advanced analytics modeling is still largely the domain of mathematicians and statisticians, and therefore, is a skills bottleneck. It is also often disconnected from the daily business processes of organizations. But more and more, analytics is a strategic priority in which top executives aim to transform the way companies work through increased use of analytics in everyday decisions. The supply side is acknowledging this trend: Today, there are more options for more advanced analytic scenarios, such as credit scoring and predictive analytics, than ever before. There is increasingly targeted content for the sector through packaged analytics "hiding" the complexity, or the ability to outsource portions of the analytic process to service providers. In advanced analytics for the insurance sector, SAS continues to dominate, but the supply is increasing. IBM is emerging as a serious competitor both from a software perspective (SPSS), and through being able to provide analytics as a service. The open-source statistical programming language "R" is being embedded in various software. Many software vendors and service providers all have the goal of increasing the "analytic bandwidth" for customers, and here the insurance sector is anticipated to be an early adopter.

• Big data and Pattern-Based Strategies — the next frontier: Few organizations, insurance companies or otherwise, have arrived at the end-station in the analytic continuum. However, tapping into the huge and constantly growing data amounts on the Web, through video, blogs, social networks, etc., provides endless opportunity. Insurance companies can mine many facets of their customers to get a more holistic perspective, and also access data in concert with their partners to offer value-added services or even manage outcomes in scenarios such as catastrophe management. Seek-model-adapt methodologies used to find patterns in this data can reduce risk and help anticipate and react to anomalies and opportunities at a very early stage.

Insurers have been investing in reviewing their IT architectures over the last couple of years, and are now starting to look with some urgency at replacing legacy systems. As this takes on momentum in the industry, it becomes a competitive imperative. The situation is that through multiple mergers and acquisitions (M&As), and expansion into new geographic territories, many insurers have multiple policy (and other) systems and databases, and they need to be able to access this data and make sure they have one version of the truth. This is now being mandated by requirements such as Solvency II. Compliance is further challenged by buying centers shifting in organizations when it comes to BI and analytics, where a bigger proportion of the buying is done in the business units. The positive outcome of this phenomenon is that BI/analytics is getting closer to the business problem it is trying to solve, enabling usage to increase. The center of gravity can, therefore, be moved from IT directly into risk teams, for example, enabling them to take on much more complex analytic challenges. The negative is that governance and compliance to data management methodologies has become harder. Areas such as data quality and metadata management are harder to ensure at an enterprise level. This may cause conflicting data points, making a holistic view of a customer more difficult.

On the other side, the IT end, Gartner sees that many customers increasingly see BI as part of a wider stack. Whether IT-mandated BI will gravitate more toward transactional application stacks (benefiting SAP and Oracle) or infrastructure (benefiting Oracle, IBM and Microsoft) remains to be seen. This will make harder for independent BI and analytics specialists to break into the IT department. Often this can cause clashes between specialists in business group silos, who prefer their specific risk solution, and IT, who want analytics to adhere to their IT stack.

Alignment between the business and the IT department continues to be an area of some friction among insurers — particularly midtier and Tier 2 carriers. This is an area of differentiation for ESPs that can bridge both areas, particularly as the business buying center is often the holder of discretionary budget.

The most notable examples of regulations that impact the uptake of BI and analytics in the insurance sector are:

• Healthcare reform (in the U.S.): Proposed changes are extensive, and will have wide-ranging impacts on payer IT systems and management of client/patient data. BI and data management will become a core competency for payers.

• The Retail Distribution Review (RDR — in the U.K.): This will introduce fee-based compensation structures to the U.K. life and pension market and will take effect in 2013. This largely affects distribution, and is expected to drive increased investment in customer analytics to achieve a single view of the customer.

• Solvency II (in the European Union): This regulation comes into force in October 2012. It defines capital and reporting requirements for insurers, requiring a comprehensive and enterprisewide management of risk. It impacts more insurance processes and core systems, and necessitates direct investment in BI and reporting tools.

• AML regulations (in multiple countries): AML tools seek to automatically identify suspicious transactions, accounts and clients, and is essentially a BI discipline.

• International Financial Reporting Standards (IFRS — in multiple countries): IFRS impacts corporate performance management solutions.

Packaged content — in the cloud or on-premises? BI in the cloud has not reached widespread adoption beyond addressing narrow domain-specific problems. But the area of packaged analytics on premises is spreading rapidly. Here SAS, FICO, Oracle and others have specific content tailored for the insurance sector, without the stigma that moving sensitive data outside the firewall has.

In-memory analytics enable proliferation of analytics: As 64-bit computing is becoming mainstream, while hardware storage costs decline, more and more vendors are banking on the possibilities that accessing information in-memory provides. In relation to traditional disk-based approaches, that means much shorter query times than before, and enables increasing disconnect from IT-mandated, centralized information sources. SAP uses in-memory as a key tenet of its strategy, with the hope that information sources can be more closely tied to transactional applications than before, thereby obviating much of the need for online transaction processing (OLTP) database management systems (DBMS) and data warehouses longer term. In-memory is already an enabler for analytics proliferating deeper into silos, for better or worse.

Big data and Pattern-Based Strategy: compliance risk or opportunity? Insurers, like other businesses, need to find ways of tapping into valuable information offered by (and usually hidden among) the ever increasing amounts of data yielded from social networks such as blogs, tweets, status updates, and so on. These data types are often unstructured and increasingly not text (e.g., video, audio and graphical), and storage intensive — thus, "big data." However, on the one hand, there is resistance to take in more information from multiple sources in line with the data governance and transparency initiatives mandated by Solvency II, while on the other hand, competitiveness and consumer-driven modes of technology-enabled interaction will demand it. This dichotomy needs to be overcome, and technologies to mature before the full potential of big data and Pattern-Based Strategy can be realized.

Context-aware computing: Increasingly, the geographic dimension can be added to analytics. This provides endless opportunity, especially for information-driven sectors such as banking, insurance and retail. In the longer term, we expect to see applications for context-aware computing leveraged in areas such as real-time decisioning for claims processing, fraud detection and insurance-premium calculation, in addition to sales and marketing applications and in responding to catastrophic events (e.g., modeling outcomes using location-based information, managing triage with emergency and health services, informing local agents and adjusters, or calculating risk based on proximity to flood zones, tornado zones or forest fires).

Collaborative decision making: This involves auditing who made the decision when BI and social software are increasingly converging, leading to new possibilities. This emerging area, referred to as "collaborative decision making," will enhance the ability to add responsibility to decisions. This can provide accountability to which culpability and reward can be placed. Regulation may be a huge driver in this area. Gartner is aware of numerous provider companies experimenting with this (e.g., SAP, IBM and small companies such as Decision Lens and Lyzasoft).

Table 1 shows Gartner's estimate of the software vendor market size for BI and analytics, and performance management for insurance sector, counting software only. The insurance sector continues to be quite heavily dominated by SAS, SAP and Oracle. With its acquisition of SPSS, Gartner expects to see IBM increase its market share in the sector going forward.

SAS has the broadest and deepest portfolio of packaged analytic applications in the BI software space. The insurance industry is one of the vertical focus areas for the company, representing an estimated 12% of SAS Institute’s total software revenue. Gartner estimates SAS to be the market share leader in the insurance space. Apart from the cross-vertical analytic infrastructure stack (data warehouse, data integration, data quality, BI platform, etc.), SAS has solutions for customer analytics (e.g., predicting the lifetime value of a customer), claims fraud, claims analytics, and product pricing (also predictive analytics capability). SAS is one of the key vendors working on analytic solutions around Solvency II, as it has a strong footprint in EMEA. Here's a list of products SAS offers: Risk Management for Insurance, Firmwide Risk Management for Insurance, Market Risk Management for Insurance, Underwriting Risk Management for Life Insurance, Underwriting Risk Management for P&C Insurance, Insurance Analytics Architecture, Fraud Framework for Insurance, and Customer Analytics for Insurance.

SAP has had one of the strongest BI and information management capabilities since it acquired Business Objects and Sybase. SAP offers insurers an insurance analytics suite and infrastructure, consisting of Enterprise Performance Management, Governance, Risk and Compliance, Information Discovery & Delivery, and Information Management. SAP either has, or is building, analytic applications for Solvency II, claim optimization and planning for P&C insurance. Generally, SAP is focusing its future direction on in-memory processing capabilities, SAP HANA, for which it claims a large pipeline. This holds great promise of analysis on large datasets in the transactional process, which could be very relevant for the insurance sector. However, Version 1 of HANA is still in ramp-up, and offers few real customer references., The Sybase acquisition gives SAP a mobile development platform on which targeted applications can be built by SAP and partners. SAP is currently partnering with IBM (SPSS) to provide predictive capabilities.

Oracle has developed an insurance-specific BI offering, its Oracle Insurance Insight. Oracle believes its competitive differentiators for this offering are its "adaptive data model architecture" — it offers flexible modeling; it has developed the tools to be configurable by business users, and the maintenance model includes ongoing updates of definitions in the underlying insurance process. Oracle does not position itself as a direct competitor to SAS's actuarial capabilities, but rather as a "data source for SAS applications." All of the Oracle insurance dashboards are Web-based and available in a variety of distribution models.

IBM is emerging as an increasingly strong alternative to SAS, SAP and Oracle in the insurance sector market for BI, analytics and financial planning, budgeting and forecasting, with its business analytics software group, and business analytics and optimization team under its Global Services unit able to go-to-market in combination with vertical-specific solutions. It is positioning its ability to help insurers with the cycle of data management, from measurement, through prediction to implementation. It has insurance-specific solutions for product profitability, and Executive Insight for Insurance, Customer Retention and Growth, and Predictive Analytics and Reporting for Claims (now completed and launched). Unlike SAP and Oracle, it has strongly entrenched predictive capabilities through its SPSS acquisition. IBM reports that it is getting significant interest from insurers in its Cognos Consumer Insight product for social media.

In addition to the megavendors in the insurance space, SAS Institute, SAP, Oracle and IBM, there are several ISV and solution providers that are impacting the segment in various ways, gaining traction in niche areas, with a finely targeted insurance-specific focus:

• Microsoft is focusing on leveraging its ubiquity and ease of use by most business users. It has added data-mining functionality in SQL Server, and is a strong competitor in the midmarket. It is now offering a server-based version of Excel that can store multiple terabytes of data, is instantly usable by the business end user, is controllable at the server level, and is auditable. With Silverlight, users can also build their own performance dashboards. But overall, Microsoft is positioning itself to the insurance industry as an infrastructure provider, working with an ecosystem of partners that deliver the tailored solutions, for example, with actuarial package providers to offer Microsoft Grid computing on demand to insurers. Microsoft is packaging its geospatial and search data through Bing and Bing Maps to provide added value to insurers.

• Valen Technologies is a fast-growing, niche provider of predictive analytic solutions solely to the P&C insurance market, using highly specific data conversion, cleaning and modeling services, combined with a SaaS platform, with either subscription or transaction-based pricing options to provide pricing and underwriting (InsureRight workers' compensation underwriting scoring and UnderRight, pricing), auditing (AuditRight), and risk profiling (RateRight). It also offers custom development of models for claims analytics, marketing analytics and fraud detection. Valen benefits from its starting point of focus on business outcomes of insurers and integration with business processes, rather than on the technologies themselves, and also its historical databases of economic and "firmographic" data. With its platform, and consulting capability, it is likely to be a prime target for acquisition by a larger player, looking to build out its industry datasets.

• FICO has Insurance as one of the five key industries it targets, with a range of analytic and scoring capabilities for the sector, with packages for Underwriting, Pricing and Product Management, Claims Processing and Management, Fraud Protection and Wellness Management.

Information Builders has a strong base of roughly 100 insurance customers, especially on the East Coast of the U.S. where the company is based. Customers use Information Builders for production reporting and customer-facing BI scenarios. The company also offers the Insurance Performance Foundation (IPF), containing predefined ETL and data models for P&C, reinsurance and workers compensation insurance funds. The business content contains 40 reports and guided ad hoc templates for executives, underwriting, actuary, claims administration and finance. Its Performance Management Framework solution for insurance contains a metric-based data model, along with predefined KPIs and metrics, to provide a balanced scorecard approach to management of a P&C insurance company.

The vastly increasing data amounts inside and outside the firewall offers significant new challenges and opportunities for Insurance companies. ESPs and vendors should focus on helping overcome those.

Challenges, such as new regulatory pressures in environments of disparate data sources, insufficient data quality and governance, are all areas where companies will struggle. Although insurers have been using reporting capabilities in core software (e.g., policy or claims management software) and performance management dashboards, they will increasingly need to take a more holistic view of their data — both to meet regulatory requirements and to meet business mandates to retain and upsell their customers. As shown in this report, several software vendors are working toward making that vision a reality for customers. Software vendors are providing more and more tactical offerings, but to be credible, should prove that those tactical tools fit well into the overall information infrastructure of companies. Service providers can fill the gaps in content, and also fill the gaps from a governance perspective, as customers will continue to need support in the creation and development of a Business Intelligence Competency Center (BICC). See Gartner's research on best practices for the development of BICCs.

Apart from the obvious challenges mentioned above, endless opportunity lies in those increasingly huge data amounts that can be mined, and give a more multifaceted view of customers, risk, and likely future outcomes. Hence, BI and analytics will increasingly be a core capability and differentiator for insurers. ESPs need to prove that they can make this a reality, and BI and analytics must be an essential part of the go-to-market story in the next decades. Vendors need to continue working on usability both from a graphical interface perspective, "hiding" as much complexity as possible, but also through embedding analytics deeper into existing transactional business processes.