Risk Consulting

Credit Risk expertise for financial players

The Risk Consulting team advises financial players on credit modelling & data management. 

We support banks and financial institutions in the following areas:  

Credit modelling
Integration of ESG criteria into their credit model
Data Management

Client benefits

A combination of our knowledge and market best practices

EthiFinance consultants propose the most suitable approach to define KRIs, providing the most appropriate assessments and recommendations regarding business allocation, portfolio management framework, risk and human resources 

Credit risk measurement & analysis

With a in-depth understanding of regulatory requirements and expertise in statistics, our consultants provide clients with methodological and implementation supports for modelling, stress testing analysis, model validation as well as risk monitoring and maintenance reporting 

ESG integration in credit risk

We support our clients on the ESG risk integration roadmap, providing our knowledge of the methodology and regulations in ESG and credit risk quantification as well as in the ESG database 

Mission on all Basel models (PD, LGD, EAD) and on all asset classes

Use Cases

Group Risk Management

A leading European bank, offering its corporate, institutional and individual customers a wide range of value-added advisory services and financial solutions.  


Modelling of the PD PRO/SCI Basel retail parameter 


To propose a relevant, simplified, sustainable and fully compliant credit risk model. 

  • Construction of the modelling database (RDS) 
  • Risk differentiation: construction of the score model and classification of risk levels                        
  • Risk quantification: calibration of the model                                              
  • Supply of the modelling package. 
Implementation of the Backtest of Private Banking PD

The private bank of a major European bank.


As part of the monitoring of econometric models, the implementation of backtests of all credit risk models is a regulatory requirement. In particular, the PD model for private banking is a so-called expert model due to the very low statistical and quantitative involvement in its construction. Consequently, setting up a quantitative backtest was a challenge.


To meet a regulatory requirement. At the time of this backtest, we also had a second objective, which was to put in place new statistical monitoring indicators in line with regulatory expectations.

  • Construction of the operating database. This database is a consolidation of several pieces of information from different universes to be merged via joins and controls 
  • Adaptation of the database for use in work tools 
  • Construction of indicators or equivalences based on the information available 
  • Implementation of regulatory statistical tests (PSI, HHI, AUC, GINI, binomial test, etc.) 
  • Integration of the validation team’s recommendations 

At the end of the backtest, the analyses confirmed that the PD values already implemented were conservative. As a result, no real action was required. However, a new model was being developed for this scope. 

Contribution to the team

Carrying out this backtesting exercise allowed us to provide the modelling teams with a backtesting report in line with the regulator’s expectations. In addition, the teams have benefited from automated programmes that can be used on other subjects, thereby increasing productivity.

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