Health
Mobility
By product
Health
Mobility
By industry
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2
2
times
less mistakes at claims forecast
Increases portfolio profitability by getting more profitable clients
Automated Underwriting
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Underwriting is a process of making decision based on statistics, but there is always a human opinion in place. People tend to be cautious and avoid harsh decisions. Meanwhile we know that companies even of the same industry have different usage of medical coverage based on many factors including office locations, transport used, clinic specifics etc.
Thus, we have a solution which helps Insurers to make better and weighted decisions to get higher profit margin on the medical insurance proucts
System features
Modeling and testing
Payouts forecasting modeling and comparison to factual data
Process automation
Steps 1-3, Inflation Forecast, Consumption, Seasonality, etc. Base Price Calculation
Preparing 250+ features to be used in models
Preprocessing
Adding 20+ factors to increase precision of UW calculations
Data Enrichment
Over 15 factors to structure and validate data for further processing
Data cleansing
Via API or other means, rolling out tested models. Deploying the Model Retraining Algorythms
Integration
Result
1,60
Statistics show that the scatter of predicted decisions relative to the median is smaller for the underwriter, i.e. a person acts “according to the average,” trying not to make decisions that are close to the extreme.
Comparison of AI model and Human UW
1,40
1,20
1,00
0,80
0,60
0,40
0,20
-
0
2
4
6
8
10
12
14
16
18
20
Human forecast
Model forecast
Fact
Half as many errors in predicting claims payout
We’ve been given a dataset for 2 months period of medical services reimbursed by one of the big Health Insurers.
Case study
Romania Business case
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