Risk Prediction

The assessment of the solvency of bank client companies is influenced by many events occurring in the industry, and most of them are reflected in open news sources. Assistance to bank analysts in analyzing the heterogeneous risks of a borrowing company according to the news flow consists in automating the process of collecting and analyzing news by creating a solution to tagging and categorizing news based on a given set of risks.

Input Data

  • News Seed on the selected company-borrower
  • A set of risks specified by the bank
  • Markup news according to risks from analysts

Output Data

  • The model estimates the probability of existence for a given risk of the borrower to the preceding News

Main Results

Integration into the business process of the loan portfolio analysis department of the bank:

  • Automation 60% of the staff searching for relevant information
  • Reduced analyst load up to 70% in different risk categories
  • Increase the accuracy of department forecasts by 15%
  • Increasing the number of detailed reports on the portfolio in 2 times
  • Compiling semantic core for 80% of client companies

The final quality of the semantic core selection:

Quality criterion Quality
Name and its variants 75 %
Names of the leaders 70 %
Affiliations 68%
Industry Terms 80 %

Quality of the constructed risk analysis system

Quality criterion Quality
F1-score 97 %