Practical experience and theoretical background allow us to properly represent various types of heterogeneous data into ready to use machine learning data sets. We perfect the art of feature engineering for time-series data, financial transactions, spatiotemporal information, behavioral patterns and many more. A high-quality risk scoring model is one of the key success factors in risk management. Our PhD level data scientists in Machine Learning can train and properly validate a Risk Scoring Model that will have a comprehensive view of the insured.
Case studies
All case studiesRelated projects
Insurance · Machine Learning
Telematics-based driver scoring for usage-based insurance
Per tripdriving score and personal discount
A deep learning model that analyses telematic data from drivers’ phones to detect driving behaviour, score each trip and calculate personalised discounts and safe-driving recommendations.
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Credit scoring model for an SME lending company
800+ features from 14 data sources
A machine learning model that predicts borrower default and fully automates underwriting, improving loan portfolio quality and cutting decision time from hours to a fraction of a minute.
Risk estimation model for a medical insurance company
Outperformed statistical baseline models
A neural network model, trained on five consecutive years of historical medical data, that estimates the risk of economic loss so a niche health insurer can optimise its pricing policies.
Player churn prediction for an online gaming platform
11.3% churn rate after new retention strategies
A machine learning model that learns player behaviour during the game and predicts the probability of churn over a given time horizon, so the platform can apply the most relevant retention strategy.
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