Batch Data Analysis

Find unusual patterns, relationships or transactions

Analysis are conducted across industry shared claims, fraudulent and suspicions data for claims assessment purposes utilising a bulk set of data supplied. For a pro-active analysis, active policy data can be summited to be analysed for potential fraudulent triggers against the RMS database.

The current Astute Risk Mitigation Service (RMS) is utilised to ingest and analyse the data provided by the Client. Furthermore, the data will be correlated to the current RMS industry data as well as triangulated against additional data sets such as the Department of Home Affairs to further strengthen the analysis process.

The correlations aim to identify matches based on various potential fraudulent scenarios on the defined data fields between the FSP’s and thus indicating possible fraud.

Scenarios utilised within the analysis process

Identity Match

Match ID against the ID in the RMS Database to analyse various role players and their relation to the claims paid.

Bank Account details

Identifying multiple parties sharing the same bank account details.

Multiple ID Numbers

Identifying individuals with Multiple ID Numbers.

Cell Phone Numbers

Identifying multiple cell phone numbers for the same role players or the same cell phone numbers shared between several role players.

Event Date vs Claims Date

Identifying scenarios where the death occurred after the claim date.

Inception Date vs Claim Date

Identifying scenarios across the RMS database and the client’s data where the same role players claim dates occurred within 6 months of inception.

Pricing

Detect and identify hidden risks, suspicious events and patterns within vast data fields