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Recently, the number of models used in financial institutions has increased exponentially, particularly in the field of credit risk, such is the case of scoring models in admission, monitoring and recovery, machine learning models, IRB parameters, capital, correlations, stress testing and the recent IFRS 9 parameters, among many others.

This proliferation of models has benefits such as automation, efficiency and speed in decision making. However, they also have drawbacks, due to decisions made by the wrong models or used inappropriately.


Model risk, in the United States, is defined as the set of possible adverse consequences derived from decisions based on results and incorrect reports of models, or from their inappropriate use. The European regulator defines it as the risk related to the underestimation of own funds, for example, due to the use of the IRB.


The objectives of the course are the following:


  • Explain the definition and scope of the model risk, the best practices in terms of management, control, governance validation and quantification of the same. Know how COVID-19 impacts credit risk models and the model risk itself.

  • Explain the pioneering model risk directive SR 11-7 in the US, the recent internal model review directive, TRIM, in the European Union, EU, and other important model risk and validation directives such as the estimation directive of PD and LGD and treatment of EBA default exposures.

  • Explain the use of artificial intelligence for model validation.

  • Techniques are shown to achieve the automation of the Construction and Calibration of Models through Artificial Intelligence.

  • Indicate the best practices for validation of credit risk models of financial institutions.

  • Show model risk quantification techniques in credit scoring models, PD, LGD and EAD parameters and regulatory and economic capital.

  • Explore credit scoring validation techniques, and others such as discriminant power, stability tests and backtesting.

  • Offer a very significant number of econometric and machine learning methodologies to develop credit scoring, PD, LGD and EAD models under the IRB and IFRS9 approaches.

  • Explain methodologies to develop models of economic capital and stress testing.

  • Present validation techniques for economic and regulatory capital models.

  • Show a significant number of validation techniques for econometric models and time series used in stress testing.

  • Modeling the stress testing of the PD, LGD and transition matrices of consumer and corporate portfolios.

  • Show innovative stress testing validation techniques.

  • Explain and detect model risk in stress testing.



This program is aimed at managers, analysts and credit risk consultants. Particularly, to professionals of model risk, model validation and model auditing. For a better understanding of the topics, it is recommended that the participant have knowledge of statistics. The course contains exercises in SAS, R and Excel.

Testimoniales Internacionales

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Price: 6.900 €


  • Europe: Mon-Fri, CEST 16-20 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h






Level: Intermediate-Advanced


Duration: 30 h



Presentations PDF

Exercises in R, Python, SAS and Excel


Model Risk for Credit Risk


Model Risk for Credit Risk


Modular Agenda 

Anchor 12



Module 1: Risk Management Model and Quantification


  • Model Risk Definitions

  • model risk management

  • Model Definition

  • Sources of Model Risk

    • ​Dating

    • Estimate

    • Use

  • Inventory of risk models

  • control methodology

  • Process and technology management

  • governance

  • Model lifecycle management

  • Model risk quantification

  • Quantitative risk management cycle model

  • source identification

  • Model risk mitigation

  • Model documentation

  • Model validation

  • Profile of model risk teams in financial institutions

  • Structure and organization chart

  • Main team activities

  • How to make an inventory of models?

  • COVID-19

    • Impact of COVID-19 on credit risk

    • Impact of COVID-19 on model risk

    • Main flaws in credit risk models

    • Generation of Post-COVID-19 credit risk models

  • Case study 1: European bank model risk

  • Case study 2: model risk in credit risk models

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