Every model is built using best-in-class global practices
01
Define Objectives & Scope
We start with a focused discussion to align on business goals, model use cases (credit scoring, PD, LGD, EAD), portfolio specifics and regulatory requirements. Clear objectives ensure the model delivers measurable impact
02
Data preparation
Data preparation is the most complex, critical, and time-consuming stage of credit risk model development. To build a reliable predictive model, the company must already have sufficient historical data.
We agree upfront on the required data format, structure, and fields, and your team prepares the extracts accordingly. On our side, we perform a thorough and detailed data quality assessment—completeness, consistency, outliers, missing values, and target definition — before moving to modeling
03
Model development
We build robust and explainable credit risk models using proven techniques such as logistic regression, decision trees and gradient boosting. Models are tested for predictive power, stability and regulatory compliance
04
Results delivering
You receive a complete, production-ready package: clean datasets, model code, detailed documentation and validation support. We also assist with audit, regulator review and implementation
