BPCE - 2019 RISK REPORT Pillar III

CREDIT RISKS

RISK MEASUREMENT AND INTERNAL RATINGS

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

Portfolio with low default risk

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

Large corporates (Revenue > €1 billion)

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 Models based on estimated losses, segmented by type of contract and guarantee Models based on estimated losses, segmented by type of contract and guarantee Models based on estimates of asset resale conditions, segmented by type of asset financed Models based on estimated losses, segmented by type of contract

Conversion factors, segmented by type of contract

2(o/w 1 NA)

8(o/w 3 NA)

SMEs (Revenue > €3 million)

11(o/w 2 NA)

5

Corporates

Non-profits and Insurance companies

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

Lease financing

1

2

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 Statistical models (logistic regression) including behavioral and socioeconomic variables, differentiated by customer profile

8(o/w 1 NA)

5

Individual customers

7

Professional customers (socioeconomic category differentiated according to certain sectors)

Conversion factors, segmented by type of contract Conversion factors and flat-rate values, segmented by type of contract

Statistical models (logistic regression) including balance sheet and behavioral variables

3(o/w 1 NH)

Residential real estate

3(o/w 1 NA)

10

Retail

Other individual and professional customers

Statistical models (logistic regression) including behavioral and socioeconomic variables, or project description variables (quota, etc.), differentiated by customer profile

2

2

Residential real estate

5(o/w 2 NA)

Lease financing

2

Conversion factors, segmented by type of contract

Statistical models (logistic regression) including behavioral and socioeconomic variables

Revolving loans

Revolving loans

1

1

1

NA refers to models not yet approved for the determination of capital requirements. *

INTERNAL RATINGS-BASED APPROACHES – RETAIL CUSTOMERS For retail customers, Groupe BPCE has established standardized internal ratings-based methods and centralized ratings applications used to assess the credit quality of its loan books for better risk supervision. For the Banque Populaire and Caisse d’Epargne networks, they are also used to determine capital requirements under the Advanced IRB method.

counterparties. The models are segmented by type of customer, distinguishing between individual and professional customers (with or without balance sheets) and according to products owned. The counterparties in each segment are automatically classified using statistical models (usually logistic regression models) into similar and statistically separate risk categories. Probability of default is estimated for each of these categories, based on the observation of average default rates over the longest period possible. These estimates are systematically adjusted by applying margins of conservatism to cover any

The probability of default of retail customers is modeled by the uncertainties. For comparison purposes, risk reconciliation is Risk division, based in large part on the banking behavior of the carried out between internal ratings and agency ratings.

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RISK REPORT PILLAR III 2019 | GROUPE BPCE