Fixing data quality issues -

Fixing data quality issues

Fixing data quality issues that are impacting reinsurance programmes

It’s a given that data quality is vital to help contribute to profitable underwriting profits for insurance and reinsurance programmes.

However, this issue is becoming even more important with events that have transpired since the pandemic and with the insurance industry being required to raise premiums in 2023; all as high inflation has really started to bite. Then you also have increasing environmental events which are impacting insurers and reinsurers’ underwriting returns.

Mitigating data challenges

Strong, accurate and timely data can maximise leveraging reinsurance programmes to help achieve the underwriting returns that insurers and reinsurers plan to deliver.

Substandard data not only impacts the ability to achieve these company goals but can lead to reinsurance programmes being over collected with the insurers taking on more claim’s costs or trigger reinstatement premium costs that should not be incurred.

There is a well-known example that is still very relevant. The case involved crop claims being mapped to the wrong (property claims) reinsurance programmes. This arose due to various mitigating factors along the data flow chain to claims i.e., similar loss dates & locations. This error resulted in £10m of crop claims contaminating the property reinsurance programme and collected from the reinsurer, blowing through the layer.

This impacted the insurer as it:

  • Incurred an additional £5m of cost because there was only £5m reinsurance recoverable left in the program.
  • Incurred a reinstatement premium cost to trigger the additional coverage.
  • Had an under-utilised property general reinsurance programme that it had incurred costs to purchase, and the crop reinsurance program had not been fully recovered on.

Improve data quality, time and cost efficiently

With the scale of data used in any reinsurance programme, a combination of technology and people who understand the market is key. Businesses need accurate analysis and quick results to meet the pace of the market and its processes. Key steps include:

  1. Take data feeds from
    • Inwards/claims
    • Reinsurance
    • Reinsurance contracts
  1. Analyse inwards claims data
  2. Investigate the data anomalies and the potential financial impacts
  3. Interrogate the reinsurance contracts and the reinsurance current position
  4. Reconcile the impact of data anomalies
  5. Compile a report with full findings
  6. Correct errors

Speeding up the analysis in reinsurance programmes

We understand the time and resource required to do this, in addition to the day-to-day work, is a lot for any in-house team but with automation of the building an extract of the erroneous records much of the time-consuming work can be alleviated.

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