Data Exploration
Data exploration is a self-service interactive reporting capability that helps
organizations to explore or slice and dice the data to find hidden insights into the
data. It extends basic reporting functionality, by using visualization techniques such
as heat maps, correlation matrix and word clouds to visualize the data.
6. Analytics
Risk managers are increasingly being asked to back up their perspective with hard data. Data
that identifies new or emerging risks. Data that quantifies a risk exposure. And data that helps
predict a future risk -- before it becomes real. To meet these objectives risk managers are
implementing an array of analytics techniques including data exploration, predictive analytics,
and geospatial; all being driven by the advancement of AI.
Geospatial Analytics
Geospatial analytics is growing trends in risk management in that it enables
organizations to easily visualize trends using geographical maps. Properties and
claims data can be overlaid with data on weather pattens, wildfires, flood plains, civil
unrest and many more. Representations like these can reveal historical shifts, risk
aggregations and as well as shifts underway today allowing for real-time and
proactive risk management.
Predictive Analytics
Predictive analytics is the use of data and statistical algorithms to identify the
likelihood of future outcomes based on historical data and combining with third
party and business data. Many organizations are using predictive analytics in claims
to detect fraud, identify litigation propensity, discover subrogation claims and
accurately assess case reserves.
MAKING SENSE OF RMIS | 8
