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Insurance
We help insurance companies fight fraud, calculate the number of losses and different probabilities.
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Insurance portfolio audit
Multivariate portfolio analysis, identification of positive and negative aspects
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Using Geodata in Predictive Insurance Models
Algorithms for constructing forecast values of frequency and the average loss in insurance.
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Insurance company scoring
Models to predict customer loss, loss size, and the likelihood of buying and renewing a policy.
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Patients death prediction for hospital
build model's death prediction (XGBoost, LGBM, optuna, XGBSE, pycox)
helps to make life longer
Graph analytics for insurance company
graph in python networks
fraudulent customers were found in graph
results transferred to neo4j
neo4j was developed to production mode
graphSage was part of the project
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Face search by photo
Content search by actor photos, search by tags.
SQL and OLAP cubes development
we have built SQL databases and OLAP cubes
developed analytics based on OLAP technology (insurance portfolio audit, price change suggestions, analytic reports)
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Insolver
Auto ML library for quick creation and implementation of models
Detector, segmentator and set of rules for determining damage
A model for searching for structural damage on a small volume of images
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Data science You Tube channel
More than 50 research videos (data science, front-end, back-end, product management)
Training course for students and interns of the company
developed and launched the educational course “Machine Learning in Business”
trained over 150 students
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