Problem
- Climate change related risk is a significant factor for insurance companies and has to be managed
Climate risk scoring
Customer
Swiss Insurance company
Solution
- Using external weather model data to build climate risk scores for each part of the globe
- Computing wetness, dryness, and sea level rise for each point on the globe to determin flood risk scores and the estimated impact on the insured portfolio
- Processing of billions of records of portfolio and geo data
Impact
- Improved climate risk modeling taking into account various climate change scenarios and providing an improved tool to the underwriters
Exposure analytics
Customer
Leading Swiss insurance company
Problem
- An insurance company struggled with the ability to identify areas overexposed in terms of total insured value overall or per given peril
Solution
- Creation of an application to present a heatmap of insured values (total / per peril) for all area granularity levels allowing to easily spot overexposed areas as well as many filtering options and statistics of filtered items with the possibility to analyse it further.
Impact
- Client can identify overexposed areas and coordinate with underwriters to stop offering insurance for items in those areas to prevent potential loss in case natural catastrophe happens.
DARTS – Time Series forecasting
Customer
open source by Unit8
Problem
- Any quantity varying over time can be represented as a time series: sales numbers, rainfall, stock prices, CO2 emissions, Internet clicks, network traffic, etc. Time series forecasting — the ability to predict the future evolution of a time series— is a key capability in many domains where anticipation is important. Although there exist many models and tools for time series, they are often non-trivial to work with, because they each have their own intricacies and cannot always be used in the same way
Solution
- Darts is our open source library for time series forecasting, attempting to simplify time series processing and forecasting in Python
Impact
- Speed-up the process related to time series forecasting in order to
– Decrease costs
– Improve accuracy
– Reduce manual work
Claim risk scoring
Customer
Leading Swiss insurance company
Problem
- Undetected fraudulent insurance claims may cause a significant blow to the insurance’ companies revenue
Solution
- Implementing fraud scoring KPI’s together with visualization
- Enabling multiple insurance companies to use the API for validating claims of their customers
- Ensuring auditability and idempotency of the results
Impact
- Automated the fraud-flagging process and created the tools for enhanced manual inspection of the most suspicious claims for multiple companies
Financial health watchlist
Customer
Swiss Financial Institution
Problem
- The ability to predict which business partners might be in bad financial health became more complex and volatile during the crisis.
Solution
- Creation of a central gathering and processing platform for the relevant partner data and central risk modelling
- Automated reporting system for the risk evaluation
Impact
- Improved risk management by information centralisation and standardisation