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Airmic Technical
3 Data analytics – a maturing discipline
Figure 1: Analytical Capability Maturity
Most risk managers will be used to reporting on claims and business
trends. However, mature risk managers will use the available data
and apply analytics to it to gain a deeper understanding of their
risks, predict when those risks will manifest and make data-driven
proposals on how they can be managed.
Timely, well analysed data can make a persuasive argument for
business model and process change.
There is no question that a mature approach to analytics takes time
and resource. Businesses will need to initially focus on getting their
existing data and data infrastructure in line, before they can move up
the maturity model.
EFFORT TIME COMPLEXITY VALUE
Descriptive
analytics
- THE WHAT
Reporting the loss across
the business
Predictive
analytics
- THE FUTURE
Identifying where future
losses will occur and when.
Spotting 'red flags' which
can indiciate where losses
will turn into major claims
Diagnostic
analytics
- THE WHY
Understanding why rates
of loss differ across the
business
Prescriptive
analytics
- THE ACTION
Agreeing processes or
changes to avoid or reduce
the impact of predicted
losses
The four stages of analytical maturity (Figure 1) describe where
the business is in terms of harnessing data.
