Application of Eis in Insurance Industry

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▪ While you should take full advantage of the computer revolution in smaller processors, do not be naive. One can clearly cut processing costs by moving applications to appropriate PC, PC-LAN and Client/Server modes. But remember that most system costs are not in processing, but in labor, communications, etc. In any event, there remain applications that demand larger computer processor power. For example, in comparing one organization's loss experience with the collective data of its overall industry or when you must link data from many geographically dispersed operations. Flexibility includes having access to all the computer power you may ever need. Be sure your vendor has such an option.

▪ User friendly software and powerful analytical capabilities are opposing features. As one commentator summarized: design software that any idiot can use, and only idiots will use it. The answer is another form of flexibility: allow advanced users to depart from the menu path to invoke complex commands on the analysis.

Identify All Users

The complexity of loss management is that a very wide range of users are involved. They range from the CFO of the organization who is concerned that a catastrophic loss is properly reserved to Operating Managers who are focused on the losses being charged to their plant, etc. And as Figure 3 shows, one cannot limit users to those internal to the organization. The reporting needs of a far larger group should be considered.

Measuring Performance

For the insurance field generally, there is a clear need to define standards for measuring loss performance across industries. These standards will allow companies to make informed decisions about investing in reducing their expenses for various property or casualty losses. A lower cost of risk than the industry average gives that company an economic cost advantage. With the cost of risk reaching five percent (5%) of total revenues in some industries, there is significant potential profit from good risk management.

Reporting/Analysis

Data without effective analysis is a wasted resource. To invest in improving data logically requires an equal commitment to putting such data to work. The challenge is not computer software. Our computer technology today can answer nearly any question. The effective limitation is the expertise to make logical decisions based on interpreting the computer's output. In short, we have not learned to ask the right questions. And, even with the right question, Risk Management/Insurance Departments often lack the time to pursue them. People, not computers, are key to effective analysis.

As insurance is essentially a financial concept, financial administration reporting is a basic output. As the industry moves to proactive approaches to risk management, these other outputs will grow in importance.

Decision Support

Analysis must lead to decision-making. Nothing changes without deliberate action. Here are some specific decision-support applications to address:

▪ Performance Monitors. DR. Deming puts measurement at the center of management. When Risk Managers and their service providers have measurable goals, the information system is the logical place to 'keep score'. The speed in achieving claim settlements, accuracy of loss reserves and the number of claims per employee are three examples. Creativity will lead to many more.

▪ Allocation of Cost. If an organization views losses and related costs as external, unmanageable expenses, then Risk Management's role is limited to insurance buying and bookkeeping. When they are viewed as manageable expense, then the information can be re-cast in understandable operating terms (cost per hour; rate per unit produced, etc.) There is then an opportunity for communicating continuous improvement.

▪ Financial Risk. Large organizations should not purchase excess insurance before calculating what such protection should cost. These calculations, though intricate, can be performed when one has good data and competent risk analysts. While the conservative one, the decisions in both cases should be based on a comparison of the cost of retention versus the cost of transfer.

▪ Benchmarking. If cost of risk is a manageable expense, then management should be comparing its performance with its peers. Such comparisons are simple in theory, but quite complex in practice. Never-the-less, the effort is worth it. The RMIS is the best place to start building the consistent rules that will allow organizations to consistently compare their relative cost of risk.

Good risk management, like good detective work, is never limited to 'by the book' procedures. Creativity and analysis remain at the core of our profession. However, good data and the tools to manipulate them are the silent, but critical, partners of successful risk management and insurance professionals.

Challenges faced by Insurance Industry:

The Insurance industry is facing a challenging situation. In the midst of a global economic slowdown and severe earning pressures, the industry is going through a phase of consolidation and integration. Increased Merger and Acquisition activity has brought about the need for IT departments to unify systems built on diverse platforms. Systems need to be more customer-centric. IT will need to drive and support revenue-producing initiatives, harness knowledge for speedy decision-making and help enhance the effectiveness of channel management. All of these developments throw up complex challenges for IT in Insurance, with the twin focus of providing greater ROI on IT investments while cutting budgets.

There is a need to outsource many traditional activities and integrate new technologies.

Solution provided by NIIT Technologies

The Insurance domain is a core focus for NIIT Technologies. Financial Services Practice specializes in offering solutions to the Insurance industry. Services are offered in areas of:

▪ Management Consulting

▪ Systems Consulting

▪ Software Services

▪ Business Process Outsourcing

▪ Training Services

Lending a substantial advantage to our offerings is DEI (Data Executives International),