BPCE - 2019 Universal Registration Document

6

RISK REPORT

CREDIT RISKS

Efforts were also focused on overhauling the models used to rate “professional retail” customers and to estimate exposure at default (EAD) for the both the “ individual and professional retail” customer segments. These new models, developed in 2018, were approved by the ECB in 2019. The following table lists the internal credit models used by the Group for risk management purposes and, where authorized by the supervisor, to calculate capital requirements for the Banque Populaire and Caisse d’Epargne networks, Natixis and its subsidiaries, Crédit Foncier and Banque Palatine.

New models were recently added and are in the process of being approved by the ECB. The models in question are PD rating models for “individual retail” customers and LGD estimation models for “individual retail” and “professional retail” customers. The new methodology for PD rating models aims to improve predictive power over customers without payment incidents. The new LGD calculation methodology aims to distinguish losses in the event a customer is downgraded to “disputed” (material loss) from losses in the event a customer is quickly restored to “performing” status (non-material loss stemming primarily from admin costs). These models will be added to the inventory once they have been approved by the ECB.

Number of CCF/EAD (Exposure At

Number of LGD (Loss Given Default) models

Number of PD (Probability of Default) models

Default) models

Exposure class

Description/ Methodology

Description/ Methodology

Description/ Methodology

Portfolio

Portfolio

Expert criteria including quantitative and qualitative variables

Expert criteria including quantitative and qualitative variables/economic and descriptive variables Portfolio with low default risk

Application of regulatory inputs

Sovereigns and affiliates Multilateral development banks Municipalities (communes), departments, regions, social housing agencies, hospitals, etc. OECD or non-OECD banks, brokers/dealers Large corporates (Revenue > €1 billion)

Sovereigns and affiliates

Sovereigns, central governments and central banks

1

1

1

Expert criteria t Portfolio with low default risk

1

Expert criteria/statistical modeling (logistic regression) Portfolio with low default risk

Public sector

10(NA*)

Expert criteria including quantitative and qualitative variables

Application of regulatory inputs

Expert criteria Portfolio with low default risk

Institutions

3

Banks

1

1

Expert criteria including quantitative and qualitative variables, depending on the business sector Portfolio with low default risk Statistical models (logistic regression) or flat scores, on companies publishing parent company or consolidated financial statements, mainly based on balance sheet data depending on the business sector, and banking behavior/history

5

Other contracts (general, pre-export financing, property investment companies, etc.)

Models based on estimated losses, segmented by type of contract and guarantee, or expert criteria Models based on estimates of asset resale conditions, segmented by type of asset financed Models based on estimates of asset resale conditions or future cash flows

Conversion factors, segmented by type of contract

SMEs (Revenue > €3 million)

11(o/w 2 NA)

8(o/w 3 NA)

2(o/w 1 NA)

Corporates

Non-profits and Insurance companies

Expert criteria including quantitative and qualitative variables Portfolio with low default risk

Lease financing

2

1

Specialized lending (real estate, asset pool, aircraft, etc.)

Specialized lending (real estate, asset pool, aircraft, etc.)

Expert criteria based on features of financed goods/projects Portfolio with low default risk

8(o/w 1 NA)

5

606

UNIVERSAL REGISTRATION DOCUMENT 2019 | GROUPE BPCE

www.groupebpce.com