The Insight Experience Analysis applications provide the continuous monitoring of policyholder and claimant behaviour against a variety of assumptions allowing for more accurate and flexible pricing, negotiation of reinsurance and information for management and regulators.

The data demands of experience studies mean that the data warehouse infrastructure is the natural environment for such an application. But that isn’t the whole story; Insight Enterprise is designed to support temporal queries so events and exposures are calculated easily from the same data. It also means that the system is stable without delaying loads for experience to emerge.

Studies use a monthly seriatim methodology; this gives the ability to study over any time period with detailed accuracy. Insight Enterprise includes a wide range of measures including actual events, expected events, A/E, Exposure, Standard Deviation or rates and A/E; by benefit and count. These measures can be based on any standard, modified or custom table, or even composite basis constructed by applying specific tables to products depending on product.

Measures are only part of the story; the ability to slice and dice results makes the difference between simply reporting experience and analyzing it. Experience studies can be sliced and diced by any policy or claim attribute.

Flexible date definitions include the ability to define study period by selecting dates, or from the study period and the study end date; automatically updating reports as new data is loaded.

To manage such a wide range of features, attributes are managed in hierarchies and dimension sets; making it easy for users to find the information they need. And with all the calculations carried out in real time, any combination of measures and attributes is possible, giving an almost endless possibility for reports, including trending and filtering to focus on particular areas of experience.

Experience study applications are complemented by predefined Key Performance Indicators (KPIs) such as actual to expected rates, with drill down indicators for years 1 and 1 to 15. These KPIs can be easily embedded in a SharePoint dashboard.

Complementary analysis includes

• Policy Movement, for supporting movement analysis and drill-down to policy, and
• Earnings-by-Source, quantifying the impact of mortality on earnings.

Policy Studies

Policy Studies provide persistency, mortality, withdrawal and termination rates with actual to expected ratios against a range of standard tables. Studies are available by the full set of policy attributes and over any study period. Analytics can be sliced and diced in real time to investigate the source of the behavior under investigation, and experience amounts can drill-through to individual policy records. Trending over time and standard deviations provide additional confidence to support decisions.

Mortality studies support multiple lives, nth to die, obviously joint life first or second to die are most common, and with the breadth of data can handle just about any age and percent ratings.

Claim Studies

Claim Studies provide incidence, termination, transition and incurred rates, together with related metrics such as actual to expected ratios, continuation rates and discounted claim costs. Analytics can be sliced and diced by the full set of policy and claim attributes, with drill-through on amounts to individual records.

Reinsurance Analysis

Where appropriate, reinsurance analysis adds a dimension that provides drill down to show analytics on a direct, ceded and retained basis; ceded business can be further examined by reinsurer and treaty.