We help insurance companies fight fraud, calculate the number of losses and different probabilities.
Insurance portfolio audit
Multivariate portfolio analysis, identification of positive and negative aspects
Insurance company scoring
Models to predict customer loss, loss size, and the likelihood of buying and renewing a policy.
Patients death prediction for hospital
  • build models deaths 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
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 changes suggestions, analytic reports)
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 an small volume of images
Data science YouTube channel
More than 50 research videos (data science, frontend, backend, 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