PAW Financial Expert Round 2: AI for Insurance & Risk Management


Wednesday, June 16, 2021


11:15 am



1. B2B2C Fraud Prediction with Granular Accounting Data as a Valuable Predictor at CRIFBÜRGEL (Sebastian Schnelle and Andreas Kulpa)

Companies need machine learning models that predict B2C and B2B payment defaults with higher selectivity than traditional scoring methods. Get an insight about the results of the CRIFBÜRGEL model using gradient boosting technique and the requirements to implement non-parametric algorithms. Applied to the prediction of B2B payment defaults, due to a lack of data no increased selectivity could be detected. Sebastian and Andreas will show you which new data source for accounting data will fix this problem.

2. Predictive Modeling in Quote & Buy – From Idea to Production (Nina Meinel)

Quote & buy processes in finance/insurance are heavily trying to maximise conversion by managing leads. Integrating predictive analytics into the flow increases the conversion rate and therefore influences companies targets. The journey starting with a rough idea, getting accepted as data science and implementing a productive model is shown during the presentation. A few deep dives are taken into areas as model evaluation, and key take-aways from MVP to a productive model with focus on data.

3. Leveraging Unstructured Data in Insurance (Raymond van Es)

Description: Insurance companies are used to work with structured data, for instance to build their pricing and underwriting models. In analyzing their unstructured data they are just scratching the surface. There is a big potential in this area because insurers have a lot of unstructured data at their disposal. They have vast amounts of textual data (policies, policy conditions, clauses, customer correspondence), image data (pictures of insured objects or claims) and speech data (call center conversations). In this session I will go into some of the use cases and methods for analyzing these unstructured data with a focus on text and speech.

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