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The FLRE uses an evolutionary algorithm (EA) provided within ROADS for automated parameter tuning.
Since a chromosome defines a complete configuration of the FLRE, at each generation of the EA every chromosome in the current population initializes a separate instance of the FLRE, as shown in figure 8.
Elements above the main diagonal show frequencies of misclassifications in which the FLRE was too strict and assigned the applications to a higher than needed rate class.
If the summarizers have entered a new case, it is extracted from the database, the appropriate FLRE is instantiated and the case is evaluated.
The FLRE was implemented entirely in Java 1.3.1 so that it can run in both UNIX and Microsoft-based environments without requiring recoding.
The FLRE was designed and developed by four engineers over a period of six months.
A new program was written to query the database automatically for new cases at a timed interval and to instantiate appropriate instances of the FLRE as required.
The FLRE was developed using a design for six sigma (DFSS) approach including optimization based on evolutionary algorithms.
Regular underwriter auditing of the FLRE is also critical to ensure that the engine is correctly classifying policies over time.
When we deployed the FLRE for term life insurance underwriting, we created an offline quality assurance (QA) process to support the auditing process.
This includes the data entered into the engine, the individual rules that are fired for each instance of the FLRE, and each FLRE decision.
If a change is made to the underwriting guidelines, the maintenance team can also deploy changes to the FLRE between generations.
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- FLQ terrorist
- Flrst Order Stochastic Dominance