BI in Insurance Business Intelligence and Information Management for the insurance industry

Transcription

1 BI in Insurance Business Intelligence and Information Management for the insurance industry Prepared by Cameron Wall, Blair Delzoppo & Sharon Hobart Contents The big issues... 2 The Holy Grail... 2 BI and analytics in insurance... 3 Decision making in insurance... 6 Using BI effectively... 9 In summary Business Intelligence and analytics provides competitive advantage to insurers and has already been employed by industry leaders around the world to cement and grow their position in the marketplace today and into the future. Overall however, the BI maturity of the insurance industry in Australia is low so there is substantial opportunity for those brave enough to tackle it. 1 of 13

2 The big issues The insurance industry has changed dramatically in the past decade with an explosion of competition in the marketplace and the central focus shifting off products and onto customers. Traditional and digital delivery channels must now be integrated to maintain competitive advantage and differentiation is no longer achieved through product offerings. Instead, it is created through customer acquisition and retention. Common sales and marketing buzz words such as customer churn, cross-selling and up-selling have become the life blood of successful insurers. As always, enhancing operational efficiency and effectiveness as well as maintaining productive relationships with governments and regulators remain important as does retaining strong capital positions and achieving good investment returns. By strengthening risk management and embedding sustainability into core insurance processes, and into wider activities, insurers of the future will gain competitive advantage by being able to predict trends and respond to changes in the marketplace. The Holy Grail The Holy Grail for insurers can be summed up as follows Sustainable profitable growth Rapid development and deployment of new products and product features Ability to cater to broad distribution channel opportunities Manage risk effectively Respond to increasingly demanding regulatory and reporting demands Provide more precise pricing levels Research shows Recent independent research* conducted in the Insurance industry highlights the fact that insurers more than anything else want to increase customer acquisition and new business sales while better managing their customer data. The single biggest challenge for almost half of those involved in the research was that they had no single view of their customer; no single source of truth as it were. To develop deeper insights into customers, insurance companies are most likely to turn to technologies such as: data warehouse (MDM), BI tools, CRM and customer analytics. Have a trusted, single customer view. * The State of Customer Insights in Insurance: Discovering New Ways to Grow. An Exclusive UBS TechWeb Research Survey. Peggy Bresnick Kendler, Research conducted December of 13

3 Business value BI and analytics in insurance The state of the BI nation in the Australian Insurance Industry can be summarized as follows: 1. BI Maturity is low 2. Insurers are drowning in data 3. Most have no single, trustworthy customer view 4. Insurers has several data silos, rather one centralized source of information 5. Insurers struggle with defining and finding the right people 6. Often the business and IT departments are working to very different agendas. We ll look at each area in more detail below. 1. BI maturity is low The Australian Insurance industry is placed low on the BI maturity scale (see Figure 1 below). Often, management lacks clarity around the power of BI and doesn t understand that analytics can be used to defend and indeed, grow market positioning. Budget constraints are often a major issue, which again, highlights a serious lack of understanding around the value of BI and analytics. Insurers don t seem to understand that BI transcends intra-organizational boundaries and will provide an enterprise-wide perspective, breaking down the many data silos that exist. Most haven t invested in predictive modelling and analytics technology which can deliver high ROI when used effectively. BI Maturity Scale: Insurance Optimisation Predictive Analytics Ad hoc reporting and Dashboards Profiling and segmentation Forecasting Data complexity 3 of 13

4 2. Drowning in data Data is stored across insurance organizations in siloed IT systems and more often than not, management relies on ad hoc reports that are insufficient to support good business decisions. Spreadsheets still pervade the industry as a major data storage and manipulation source. And, this is often done at desktop level with no quality control, consistency of treatment or integrity. Social media, which could be an incredibly valuable source of information for insurers is barely touched by the industry. Most don t collect data via social media and very few participate in social media at all. 3. No single customer view As is a challenge for many industries, the insurance industry suffers from a lack of visibility around their customers. This proves to be a major stumbling block when trying to better manage customer needs and predict future requirements. In addition to no single view, the data quality is often in question and budget constraints mean that improvements are low priority. 4. Data silos There is still a generally held assumption that decisions are simply too complex or too business unit specific for an automated analytics system to outperform an experienced decision maker. Often referred to as the Apprentice Model, the insurance sector believes that experience and expertise takes years to develop and that once developed, these are the people who have the insight necessary to make the important decisions. These people will always be wary of analytics. If a business unit does decide to adopt analytics, they will invariably hide it from others in the organization. This may be to avoid corporate governance, limit interference or protect resources. Line managers work hard to retain control and keeping information to themselves rather than sharing to improve the organization as a whole. 5. The right people Do they need insurance experience or not? Should they be actuaries or not? Can HR help? To answer these questions, it largely depends on the goals of the BI or analytics program. There is certainly not an abundance of BI professionals with dedicated insurance experience. Having said that, what Insurers should be looking for is BI experience as they are most likely to hold the industry experience within their management ranks already. Can HR help? Probably not without extensive training and learning around what this field really requires. 4 of 13

5 6. Business and IT: Differing agendas In the course of preparing this information, we ve read many papers, studies and documents from experts around the world. It was evident from our research that many insurance business units work around their IT department to collect and collate the data they want to analyses. And, IT departments seem to hold two distinct, quite different roles in insurance organizations: 1. Data stewards 2. Data guardians. The Data Steward is a proactive support role, encouraging data transparency, monitoring quality or lineage, and ultimately partnering the business in resolving issues. The Data Stewards facilitate enterprise-wide data use. Data Guardians on the other hand can be described as information police. They rigorously protect data from misuse, ensuring quality and integrity. In the meantime, IT Data Guardians alienate the business, which then learn to work around them. 5 of 13

6 Decision making in insurance Decision making has gone beyond the Apprentice Model of personal experience. Decisions now require trustworthy information to back them up. Not only that, but decisions must respond to a rapidly changing marketplace. Business analytics is seen by many as the key to survival as it can: Improve customer management Create competitive advantage Improve financial results & operations Better manage risk. Improve customer management Reduce customer churn by helping identify customer behavior as it is common knowledge that by year 3, many insurers have lost their first year customers Improve management of: agents and sales forces by providing clearer direction (through analysis), and actuarial and underwriting functions providing a clearer vision of the current situation and more personalized reporting and data Helps cement relationships with critical corporate customers by providing peace of mind through provable analysis Max New York llife Increased new sales from existing customers from 7% to 20% Improved premium revenue by almost 40% with shorter sales cycles Favourable economy, they predict they will generate 25-30% of all new sales from existing customers Helps insurers with customer and investor satisfaction by boosting decision confidence and better allocating resource to maximize ROI Enables members to access their insurance information (thus saving customer service costs) while assisting management with business performance measurements and future direction. 6 of 13

7 Create competitive advantage Identify new opportunities with existing customers as well as new opportunities for different products and/or services NZAA Direct mail costs <40% Identify the right customers for targeted marketing and improve MROI on campaigns resulting in fewer, hotter leads with higher conversion rates Enables immediate response to quickly changing market forces Reduces the to market time on products by providing the critical information required for product development and marketing Customer response rates have doubled Customer satisfaction improved Call centre expenses down 20% Analyze and exploit weaknesses in competitor activity in a timely manner. Improve financial results and operations Improves effectiveness of business management by identifying inefficiencies and problem areas as early as possible Can be used to manage KPIs against the overall business strategy Improves operational decision making precision and efficiency Enables accurate, timely decisions using provable granular level, enterprise-wide information Delivers high-level performance measurement for business management at every level within the business. Board member of a Tariff Advisory Committee, under Insurance Regulatory Development Authority of India (IRDA) says: BI can improve bottom line by 10-15% when used for determining general and life insurance policies 7 of 13

8 Better manage risk Enhance the performance of underwriting by analyzing claims severity and frequency Better fraud identification Monitor claims through a myriad of categories such as geography, business area, customer, product, type and handler, to better scrutinize for potential fraud Use analytics to more accurately recommend schedule ratings Constantly run sophisticated risk modelling by mining data in the BI system More accurately price risk by working at the individual customer level rather than working on an average. This enables individual prices to increase rather than everyone s average price increasing which could in turn translate into a multi-million dollar reduction in exposure Track and report claim performance as regularly as needed at the click of a button. AXA OYAK (Turkey) 5% of claims were fraudulent Reduced payouts by 5-7% Board OneBeacon (USA) Loss ratio improved by 2-4 points Get 4-10 times ROI on every predictive modelling project undertaken Case Studies appearing on these pages were sourced from: SuccessStories 8 of 13

9 Using BI effectively To effectively employ Business Intelligence in an insurance environment, there are a number of key factors to consider: 1. Have a senior analytics champion 2. Make it an enterprise wide solution 3. Centralize & decentralize 4. Build the right team 5. Remember, BI is a specialty 6. Focus on deployment, not tech 7. A shift in culture 8. Change with change Analytics champion The business must have an active, highly visible analytics champion to drive the project. This gives the project immediate credibility and shows the various business units that management is serious about making it work. Ideally, the analytics champion will be well respected, known for getting the job done and able to communicate and command respect across all aspects of the business. Enterprise wide Ensure the solution you develop links all analytics capabilities and data silos together and focuses on the big picture while delivering analytics to manage the day to day business. Enable operational efficiency with dashboards and scorecards while growing to use predictive analytics for pricing precision. Promote collaboration among business units and among tech and business users. 9 of 13

10 Centralize and decentralize Maintain a balance between the areas of your BI program that you centralize and those that you distribute throughout the business. Too much centralization will start to feel like the aforementioned Data Guardian model. Our recommendation is to centralize the critical factors such as data management activity, governance and MDM (master data management). Centralizing these functions will keep everyone in sync, it will also streamline resource requirements and leverage data budgets more effectively. Complete centralization is undesirable however as the system must support the individual needs of various business units and allow them free access. Supervised decentralization may be the right answer for many. For example the lower level analytics such as dashboards and scorecards are decentralized but the more complex, strategic elements of optimization and predictive analytics are centralized. Another way to think of it is as a franchise. Tools, research and analysis, vendor contract negotiations and purchasing, data QA, and MDM are all centralized. This ultimately reduces costs, maintains a focus on long term requirements, reduces any potential tech stack complexity, improves leveragability of analytics across the business, and helps avoid business units purchasing their own analytic tools which could be suboptimal. IT must continually promote free access and flexibility for business to the BI system to avoid being cut out of the loop. Don t get bogged down in allocating budgets and who ll pay as this will stall the project. Build the right team Make sure management clarifies the objectives before building your BI team. If you are looking to challenge conventional wisdom, look for new ways to do things and generally make noise then look outside the insurance industry; the skill set will be found there. If you want to enhance, improve or change operational and/or performance business decisions, then those with insurance experience may be the best fit. BI is a field unto itself Beware of the all in one approach. BI and analytics solutions are often treated like the answer to all business questions. There is considerable power in the information contained in your BI system but it is critical that you have appropriately trained professionals to interrogate the system. You must ensure that the right people are asking the right questions in the right way. Otherwise, information can be deceptive and lead you in the wrong direction. 10 of 13

11 Focus on deployment, not the tech As with all technology there are new tools, new business models and new data sources constantly emerging in the marketplace. Don t get bogged down in this tech; successful insurers will concentrate on the successful implementation and deployment of business analytics rather than on the actual technology. Cultural shift It is a fundamental cultural shift for much of the insurance industry. Away from the Apprentice Model and towards a culture where thought leadership still holds an important place but is equaled for value by robust trusted analytics. Operational excellence is important and business analytics are centralized and decentralized as best suited to the business. Change with change The insurance industry and indeed local and global business environments change constantly. It is important that your organization s Business Intelligence and analytics adjust predictive models and update the types of data you gather in response to these environmental changes. 11 of 13

12 In summary Business intelligence and analytics is a largely untapped resource in the Australian insurance industry. With significant results being realized locally and abroad by those who ve taken the plunge, there is definitely a strong business case for further investigation. The industry is still undergoing considerable change and the future for many, is uncertain. The power of high end Business Intelligence in terms of predictive analytics and optimization, will help sort successful leaders from the pack. Beware however, the powerful analytics weapon must be wielded by those who know what they re doing. You can t just reassign existing IT resources and expect success. With the right people, the right information and the right questions, insurers will be able to see the future and will then be in a position to go to market with products and services well ahead of their competitors. 12 of 13

13 EY Assurance Tax Transactions Advisory About EY EY is a global leader in assurance, tax, transaction and advisory services. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. For more information about our organization, please visit ey.com Ernst & Young Australia. All Rights Reserved. This communication provides general information which is current at the time of production. The information contained in this communication does not constitute advice and should not be relied on as such. Professional advice should be sought prior to any action being taken in reliance on any of the information. Ernst & Young disclaims all responsibility and liability (including, without limitation, for any direct or indirect or consequential costs, loss or damage or loss of profits) arising from anything done or omitted to be done by any party in reliance, whether wholly or partially, on any of the information. Any party that relies on the information does so at its own risk. Liability limited by a scheme approved under Professional Standards Legislation. eyc3.com ey.com/analytics Contact details: analytics@eyc3.com EYC3 creates intelligent client organizations using data & advanced analytics. Our team of data scientists, analysts, developers, business consultants and industry experts work with clients at all stages of their information evolution. 13 of 13