Credit Lgd Modelv0.3.3

Build and train an LGD machine learning model using historical default data to quantify credit risk and predict expected loss rates. This supports financial institutions in assessing potential losses from borrower defaults.

Use historical credit data to evaluate and predict credit risk, enabling lenders to make informed decisions on loan underwriting and portfolio management.

Develop quantitative models that combine probability of default (PD) and loss given default (LGD) to compute expected loss, facilitating regulatory compliance and risk-based pricing.

Aggregate individual LGD predictions to monitor overall portfolio credit risk, identify concentration risks, and optimize capital allocation.

Offer LGD model training and risk scoring to banks and fintechs as a subscription-based service. Clients upload their historical data and receive model outputs and insights.

Advise financial institutions on using LGD models to comply with Basel III/IV regulations, optimizing risk-weighted assets and capital requirements.

Tailor LGD models for insurance companies to assess credit risk in their investment portfolios or credit insurance lines.