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Table 12 Set of regulatory texts analysed, classified by purpose of the model

From: Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction

Capital
"Credit Portfolios and Risk-Weighted Assets: Analysis of European Banks". Trucharte et al. (2015). Estabilidad Financiera No 29. Banco de España
“Studies on the Validation of Internal Rating Systems”. BIS Working Paper No 14. Heitfield (2005)
“ The Internal Ratings-Based Approach. Supporting document of the New Basel Accord”. BIS (2001)
“Implementation and Validation of Basel II Advanced Approaches in Spain”. Banco de España (2006)
“On the specification of the assessment methodology for competent authorities regarding compliance of an institution with the requirements to use the IRB Approach in accordance with Articles 144(2), 173(3) and 180(3)(b) of Regulation (EU) No 575/2013”. EBA (2016a)
“Guidelines on PD estimation, LGD estimation and the treatment of defaulted exposures”. EBA (2017a)
“Calculation of RWA for credit risk”. BIS (2020)
“EBA Report on IRB modelling practices. Impact assessment for the GLs on PD, LGD and the treatment of defaulted exposures based on the IRB survey results”. EBA (2017b)
“ECB Guide to internal models”. ECB (2019)
“Provisioning models vs Prudential models”. García Céspedes (2019)
“Discussion paper on draft regulatory technical standards on prudent valuation, under Article 100 of the draft Capital Requirements Regulation (CRR)”. EBA (2013)
“Guidelines for the estimation of LGD appropriate for an economic downturn (‘Downturn LGD estimation’)”. EBA (2019c)
Credit scoring
“Draft Guidelines on loan origination and monitoring”. EBA (2019b)
“Report on automation in financial advice”. European Supervisory Authorities (2016b)
“Guidelines on creditworthiness assessment”. EBA (2015)
“On the Decision of the European Banking Authority specifying the benchmark rate under Annex II to Directive 2014/17/EU (Mortgage Credit Directive)”. EBA (2016b)
“Report on innovative uses of consumer data by financial institutions”. EBA (2017c)
“Guide to assessments of fintech credit institution licence applications”. ECB (2018)
Provisioning
“Applying the expected credit loss model to trade receivables using a provision matrix”. Deloitte (2018)
“COMMISSION REGULATION (EU) 2016/2067 of 22 November 2016 amending Regulation (EC) No 1126/2008 adopting certain international accounting standards in accordance with Regulation (EC) No 1606/2002 of the European Parliament and of the Council as regards International Financial Reporting Standard 9” EC (2016)
“In depth IRFS 9 impairment: how to include multiple forward-looking scenarios”. PwC (2017)
“Financial Stability Consequences of the Expected Credit Loss Model in IFRS 9”. Sánchez Serrano (2018)
“The implementation of IFRS 9 impairment requirements by banks” Deloitte (2016)
“Provisioning models vs Prudential models”. García Céspedes (2019)
“Guidelines on management of non-performing and forborne exposures”. EBA (2018a)
“Guidelines on credit institutions’ credit risk management practices and accounting for expected credit losses”. EBA (2017d)
Common area
“IIF Machine Learning Recommendations for Policymakers”. IIF (2019a)
“Explainability in Predictive Modelling”. IIF (2018)
“Bias and Ethical Implications in Machine Learning”. IIF (2019b)
“Ethics Guidelines for Trustworthy AI”. High-Level Expert Group on Artificial Intelligence set up by the European Commission (2019)
“Third-party dependencies in cloud services. Considerations on financial stability implications”. Financial Stability Board (2019)
“Joint Committee Discussion Paper on the Use of Big Data by Financial Institutions”. European Supervisory Authorities (2016a)
“Governance of Artificial Intelligence in Finance”. Dupont et al. (2020)
“Guidelines on ICT Risk Assessment under the Supervisory Review and Evaluation process (SREP)”. EBA (2017e)
“EBA Report on Big Data and Data Analytics”. EBA (2020)
“EBA Report on the Prudential Risks and Opportunities Arising for Institutions from Fintech”. EBA (2018b)