BPCE_REGISTRATION_DOCUMENT_2017

3 RISK REPORT Credit risk

Loss given default (LGD) is an economic loss measured by incorporatingall inherentfactors in a transactionas well as the costs incurred during the collection process. LGD estimation models for retail customers are applied specificallyto 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 betweendegreesof loss. The estimationmethodemployedis based on the observationof 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 performingloans and ELBE (1) rates applied to outstandingsin default. Estimates are based on internal collection histories for exposures at default over an extended period. Two safety buffers are then systematicallyadded: the first to cover estimateuncertaintiesand the second to mitigateany economic slowdown effect. GroupeBPCE uses three modelsto estimateEAD. The first estimatesa Credit ConversionFactor (CCF) for off-balancesheet exposures. This modelis automaticallyapplieddependingon the type of product,where the off-balancesheet has a non-zerobalance.A multiplyingfactor is specificallyappliedto thebalancesheetfor accountoverdrafts,wherethe balancesheet has a non-zerobalancebut the off-balancesheet has a zero balance.Furthermore,a standardEAD is appliedfor accountswith creditbalancesandno overdrafts (authorizedor unauthorized). INTERNAL RATINGS-BASED APPROACHES – NON-RETAIL CUSTOMERS Groupe BPCE has comprehensive systems for measuring non-retail customer risks, using either the Foundation IRB or Advanced IRB approachdependingon the networkand the customersegment.These systems can also be used to assess the credit quality of its loan books for better risk supervision. The rating system consists in assigninga score to each counterparty. Given the Group’s cooperative structure, a network of officers is responsible for using the uniqueness of the score to determine the customer’sratingfor the Group.The scoreassignedto a counterpartyis usually suggested by a model, then adjusted and validated by Risk function experts after they perform an individual analysis. The counterpartyratingmodelsare mainlystructuredaccordingto the type of counterparty(corporates,financialinstitutions,public entities, etc.) and size of the company (measured by its annual revenue). When volumesare sufficient(SMEs, ISEs, etc.), the models rely on statistical modeling(logisticregressionmethods)of customerdefaults,combined with qualitative questionnaires.Otherwise, expert criteria are used, consisting of quantitative factors (financial ratios, solvency, etc.) derived from financial data, and qualitative factors assessing the customer’s economic and strategic components. With respect to country risk, the system is based on sovereign ratings and country ratings that limit the ratings that can be given to non-sovereign counterparties.The Non-Retail rating scale is built using the past

Standard& Poor’sratingsto ensurethe directcomparability in termsof riskswith the ratingagencies. LGD models (excludingretail customers)are predominantlyapplied by type of counterparty,type of asset, and whether or not any collateral has been provided. Similar risk categories are then defined, particularly in terms of collections, procedures and type of environment.LGD estimates are assessed on a statistical basis if the number of defaults is high enough (e.g. for the Corporate customers asset class). Past internal data on collections covering the longest possibleperiod are used. If the numberof defaultsis not high enough, external databases and benchmarks are used to determine expert rates (e.g. for banks and sovereigns).Finally,some values are based on stochastic model, for loans in collection. Downturn LGD is checked and safety buffersare added if necessary. Groupe BPCE uses three models to estimate EAD for corporates. The first estimatesa Credit ConversionFactor (CCF) for off-balancesheet exposures.This model is automaticallyapplied dependingon the type of product, where the off-balance sheet has a non-zero balance. A multiplying factor is specifically applied to the balance sheet for account overdrafts, where the balance sheet has a non-zero balance but the off-balance sheet has a zero balance. Otherwise, a standard EAD is applied for accounts with credit balances and no overdrafts (authorizedor unauthorized). STANDARDIZED APPROACH When the Group does not have an internal model authorizedfor use in determiningcapital requirements,they have to be estimatedbased on corresponding inputs under the standardized approach. These inputs are based in particular on the credit assessments (ratings) estimatedby rating agenciesrecognizedby the supervisoras meeting ECAI (External Credit Assessment Institutions) requirements, in particularMoody’s, Standard & Poor’s, Fitch Ratings and the Banque de France forGroupe BPCE. In accordance with Article 138 of regulation No. 575/2013 (the Capital RequirementsRegulationor CRR) on prudential requirements for credit institutionsand investmentfirms, where a counterpartyhas been rated by several rating agencies, the counterparty’s rating is determinedon the basisof the secondhighest rating. When an externalcredit rating directly applicableto a given exposure is requiredand exists for the issuer or for a specificissuanceprogram, the procedures used to determine the weighting are applied in accordance withArticle 139 of the CRR. For fixed-incomesecurities(bonds),short-termexternalratings of the bond take precedenceover external ratings of the issuer. If there are no external ratings for the bond, the issuer’s long-term external rating is taken into accountfor senior debtonly, except in the specific case of exposure to institutions for which the weighting is derived from the credit quality rating of the sovereign country in which it is established.

Expected Loss Best Estimate. (1)

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Registration document 2017