BPCE - 2019 Universal Registration Document

RISK REPORT

CREDIT RISKS

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

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

Individual customers

7

Professional customers (socioeconomic category differentiated according to certain sectors)

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

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

Residential real estate

3(o/w 1 NA)

3(o/w 1 NH)

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

Loss given default (LGD) is an economic loss measured by incorporating all inherent factors in a transaction as well as the costs incurred during the collection process. LGD estimation models for retail customers are applied specifically to each network. LGD values are first estimated by product, and based on whether or not any collateral has been provided. Other factors may also be considered secondarily, where they can be used to statistically distinguish between degrees of loss. The estimation method employed is based on the observation of marginal collection rates, depending on how long the customer has been in default. The advantage of this method is that it can be directly used to estimate LGD rates applied to performing loans and ELBE (1) rates applied to loans in default. Estimates are based on internal collection histories for exposures at default over an extended period. Two margins of conservatism are then systematically added: the first to cover estimate uncertainties and the second to mitigate any economic slowdown effect. Groupe BPCE uses two models to estimate EAD. The first estimates a Credit Conversion Factor (CCF) for off-balance sheet exposures. This model is automatically applied when off-balance sheet exposures are deemed material ( i.e. exceeding the limits defined for each type of product). The second estimates a flat increase in the balance sheet for non-material off-balance sheet exposures.

6

(1) Expected Loss Best Estimate.

607

UNIVERSAL REGISTRATION DOCUMENT 2019 | GROUPE BPCE