54. When using automated models for creditworthiness assessment and credit decision-making …

When using automated models for creditworthiness assessment and credit decision-making, institutions should understand the models used, and their methodology, input data, assumptions, limitations and outputs, and should have in place:

• a. internal policies and procedures detecting and preventing bias and ensuring the quality of the input data;
• b. measures to ensure the traceability, auditability, and robustness and resilience of the inputs and outputs;
• c. internal policies and procedures ensuring that the quality of the model output is regularly assessed, using measures appropriate to the model’s use, including backtesting the performance of the model;
• d. control mechanisms, model overrides and escalation procedures within the regular credit decision-making framework, including qualitative approaches, qualitative risk assessment tools (including expert judgement and critical analysis) and quantitative limits.

1. Overview

1.1 References

1.2 Identified Requirements

1.3 Related Standards

2. Identified Requirements

3. Related Standards

• EBA - Published by the European Banking Authority at https://www.eba.europa.eu/