Consider these trends in the insurance industry, as identified by EMC's financial services research, Insurance Networking News, AAXIS Group Research and Forrester:

  •  18 percent of total claims payments are fraud.
  •  The industry tops $600b in data quality costs.
  •  30 percent of insurance IT time is spent fixing data.
  •  Many insurers observe the inefficiency of direct marketing programs and low conversion rate.
  •  Insurance underwriting leakage grows to 9.7 percent.
  •  The underwriting work force is aging.
  •  New medical claim reporting requirements and Solvency II reporting requirements in Europe have strict requirements.

In addition, consider the new market and competitive forces:

  •  Dramatic growth in data inputs from sensors and other instruments enables monitoring of assets under insurance to provide enhanced risk patterns.
  •  Online commerce and social media increases impact on the marketing and sales of policies.
  •  Pressure has increased to grow market share without sacrificing profitability regained from premium rate increases during the recent market conditions.
  •  Events monitoring and predictive analysis for product, risk and pricing decisions.
  •  Emergence of fresh capital in the industry, due to new sources such as the Bermuda-based companies, is expanding the industry’s capacity and intensifying competition.

Insurance industry is in general facing tough challenges in increased competition from banks and brokers, shrinking price premiums, added complexity and customer demand in product offerings, stricter government regulation, a sluggish economy and an aging workforce. To gain strategic advantage and grow revenues while reducing cost, companies need to exploit commonality across their operations — and data management is a critical success factor. How many customers do I have? Are subsidiaries of our client’s company treated as separate entities or as part of a larger organization? What is my combined risk to each, by industry, region and product?
Accurate, timely answers to these seemingly basic questions are often at the core of the effective use of information for business decisions. However, poor data quality and lack of visibility over critical information has led to the industry’s data bottlenecks — but how do you address this without changing everything?

Insurers have the additional characteristics that they are regulated and data intensive and, therefore, must guard their data well. The industry’s history of mergers and acquisitions has also hindered data quality and access. Furthermore, there are often operational issues at play, such as insurance companies' decentralized and disparate IT landscape, and the limited exposure of measurement tools that are generally confined to a single line of business. Also, typically, these business centers report data internally for their own purposes and independently of each other.

This inevitably creates differences in how data is defined. The benefit of this type of design is that insurers can develop and maintain a clear understanding of the hierarchy and history of their agent and broker networks. However, the drawback is that compliance needs mandated reports by compiling data from multiple systems across the organization, rather than from a single source. At the same time, market developments are intensifying the pressure on insurers’ business intelligence. Sophisticated pricing models driven by predictive analytics are gaining traction in personal and small commercial lines. Also, landmark events such as 9/11, Katrina and Enron have sparked demand for greater precision in the industry’s location intelligence and analysis of loss trends.

Additionally, all customers have rising expectations of their insurers in terms of product availability and the ease of doing business. Property and casualty insurers as well as life insurers, regardless if they underwrite policyholders directly or use brokers as intermediaries, lack high-quality risk assessment data and insight into their customers’ interactions across multiple divisions, resulting in missed revenue opportunities, poor customer service and inflexible compliance reporting.

On the regulatory front, the A.M. Best rating assessment includes a category for exposure accumulation practices — and the Spitzer allegations have resulted in a global push for increased transparency in interactions between brokers and underwriters. Sarbanes-Oxley also brings implications for decision accountability and risk management. The compliance challenges are uniquely complex in that every state in the U.S. maintains its own distinct insurance regulations, and these laws and policies vary widely across different insurance lines of business. Additionally, many states have regulations that govern virtually every aspect of insurance company operations, including the amount of financial reserves a company needs to conserve, how they can market their products, and how much brokers and agents can charge for their services. Moreover, most states impose a wide array of strict reporting requirements by which insurers must regularly document their compliance with the various statutes.

The convergence of these three key driving forces — consumers, competition and compliance — means insurance businesses are under growing pressure to break down their traditional rigid information silos and increase their organizational responsiveness. Let’s look at some of the typical data issues:

  •  Information for customers may be different in the marketing, underwriting, claims and finance applications. This causes customer satisfaction issues and opportunity losses.
  •  With customer and household information spread across several systems, either as a result of legacy systems or systems inherited through mergers and acquisitions, it’s very challenging for insurance companies to gain a complete view of customers and the relationships to other individuals.
  •  Consistent product definition can be particularly vexing. Ideally products on the underwriting side and claim side should be identical, at least at a high level, allowing product-level loss run analysis.
  •  Similarly, an insurer may use brokers for direct policy brokering, reinsurance brokering and claim brokering. Large brokers will be used in all scenarios and may be defined repeatedly and in different ways in each system.
  •  Disparate chart of accounts and financial codes across lines of business make it very difficult to roll up or match balances and transactions for enterprise reporting.
  •  Significant data quality issues and conflicting data and semantics (metadata) exist within and across data sources.

It has also become imperative to have easily accessible reporting and leading-edge analytics for risk quantification, pricing and segmentation analysis. Additionally, insurance companies need to create enterprise-level definitions for accounts and customers to enable more effective product development, customer relations and loss control.
Data management initiatives can help improve and sustain data quality and ease discovery by effectively using and controlling data. Insurance companies need to manage insured’s and claimant’s data across all their accounts and product lines, understand the complex hierarchies and affiliations among customers, producers and carriers, and access client data from multiple systems for daily operations and risk analysis. Through effective data management, you can eliminate errors, become more efficient in business activities, and accelerate critical processes such as new product introductions, service provisioning, cross-sell and up-sell and customer service. You can shorten the timetable to achieve success by formalizing and coordinating the processes around provisioning and analyzing data rather than relying on manual, unreliable and non-repeatable ad hoc processes of the past.

Insurers have several opportunities for enhancement through: re-use of product components, common reference data, common business rules, insured profiles, distribution profiles, streamlined licensing, more efficient e-discovery and subsequent reduction of regulatory cost. You can capitalize on these opportunities by creating a consistent view of your insured, distributors and products data.

Before companies can gain a unified view of their customers, policies, financials, etc., they need to resolve underlying master data conflicts. And by resolving master data conflicts and creating a central, shared repository of ‘the best version of the truth,’ specific applications and business users can then effectively use this master data to achieve a unified view of all their interactions and activities with customers, products, suppliers, etc.

There are many key benefits and business drivers for such enabling information management initiatives.

Enhanced customer support and increased customer loyalty. Focusing support and retention efforts driven by customer insights can easily increase profitability per customer, rather than acquiring new customers.
By consolidating data across internal applications, MDM creates one reliable source of truth of customer data. This can:  

  •  Reduce the rate of plan lapse,
  •  Reduce customer attrition, and
  •  Improve the first call resolution rate.

Financial transparency and performance management. Cut through the complexity and “spreadsheet mania” to gain financial transparency at all levels, and a global financial view based on up-to-date, trustworthy data. Support true cash flow-based planning, detailed what-if modeling, better trend analysis and forecasting decisions, and improved budgeting, reporting and accounting processes. You can also gain financial performance insights across different lines of business. Enable products, claim and underwriting performance analytics through clean, timely, integrated data. Data lineage and harmonization for consistent enterprise performance reporting yields:

  •  Improvements in loss-costs,
  •  Pricing and capacity optimization, and
  •  Facilitation of post-merger integration and restructuring.

Post-merger integration and restructuring. Information management can facilitate post-merger integration and organizational restructuring. It increases flexibility to change the regional structures (with regard to how it designates its core lines of businesses) as well as accurate reporting on how regional lines are doing. Future-proof your data assets for M&A activity and new business models.

Increased profitability and customer’s share of wallet penetration. Create a new customer experience model that includes customer insights and life events. Through the management of this information you can achieve transparency of customer’s life events, household, transactions, product holdings and legal hierarchies, and measure account performance accurately. Because not all customers are the same, it’s important for insurance companies to identify their most profitable customers by creating a complete view of the policyholder’s information, their active policies and relationships to other individuals. Properly managing this data will lead to:

  •  Increased policies per analyst,
  •  Increased premium per policy, and
  •  Ability to price policies by total customer relationship.

Improved sales and marketing effectiveness. Creating a unified and accurate customer view drives business actions that relate specifically to customer segments including offers, life event marketing, discounts and availability of information for the customer. This, in turn, leads to:

  •  Improved prospecting,
  •  Reduced cost of prospect acquisition,
  •  Improved prospect targeting,
  •  Customer lifetime value, life event analytics and increased profitability, and
  •  Improved conversion rates for direct marketing programs.

Channel effectiveness. An easy to use, productive and relevant product/policy offering is a key enabler to whether channel agents would choose to sell your policies. This improves agent performance reporting and accurate commissions.

Operational efficiency. Significant operational gains can be realized through remediation of manual reconciliation and analysis efforts as well as IT rationalization by integrating, deduplicating and rationalizing the data architecture. Examples include:

  •  Simplification of opening a policy – with regard to reduced paperwork and turnaround time,
  •  Automated policy linkage,
  •  Reduced infrastructure cost,
  •  Reduced turnaround time for claims processing, and
  •  Operational improvement in calculating and allocating commissions.

Risk management and visibility. Information management can improve policyholder risk profiling and fraud prevention and align financial risk decisions with overall corporate strategy. Accurate, timely and harmonized data provides trust in information for underwriting to run risk/pricing models, as well as finance to project and reconcile enterprise financials with confidence. Assemble the global corporate hierarchy of counterparties and cross reference credit and equity exposures providing a complete view of financial exposure. This leads to:

  •  Broader visibility of the customer to more accurately gauge risk and price accordingly,
  •  More profitable policies, and
  •  Capital optimization and Solvency II; risk-based claims processing.

To be effective, you must link your data management initiatives to strategic business capabilities including:

  •  Channel management (plan and performance),
  •  Actuarial pricing,
  •  Product economics and performance,
  •  Policyholder profitability analysis,
  •  Enterprise risk management,
  •  Claims performance management,
  •  M&A integration and enterprise-wide reporting, and
  •  Finance management and performance management.

Finally, enterprise data management and governance is no longer a futuristic initiative — it has become an imperative to many strategic initiatives that insurance companies need to deliver on. Across the spectrum, many companies specializing in various types of insurance products have a mandate to undertake the programs that are contingent on the reliability, availability and integration of good data. It will not be long before it becomes a common trend in the insurance industry to tackle its long-standing and complex data issues.

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