{"id":"AIBD-0865","title":"A BIS paper co-written by a Mercado Libre employee finds that the firm's machine-learning credit score predicted defaults better than bureau scores","summary":"Using 2017 loans in Argentina, BIS economists and a Mercado Libre employee found that the firm's machine-learning credit score predicted defaults with an AUROC of 0.76, against 0.64 for bureau scores.","date_observed":"2019-04-08","date_basis":"source publication","date_precision":"day","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Mercado Libre lends to small merchants on its e-commerce platform through Mercado Crédito. Its internal credit rating uses machine learning on platform data on sales, reputation and industry characteristics. Economists from the Bank for International Settlements, the Financial Stability Board and the Graduate Institute, Geneva, wrote the paper with a Mercado Libre employee. He disclosed his employment and investments in the company, and the data came from Mercado Libre. Using 2017 loan data from Argentina, the authors compared the internal rating with the local credit bureau's ratings. Loss rates ranged from 0.0% to 10.2% across the internal rating's groups and from 0.7% to 2.8% across the bureau's. In default regressions on 7,300 observations, the machine-learning score had an area under the ROC curve of 0.76. Bureau scores reached 0.64 alone and 0.68 with borrower characteristics. About 30% of the portfolio fell in the bureau's high-risk group, which had a 2.8% loss rate. The authors write that it remains to be verified whether the model predicts defaults better over a full business and financial cycle.","category":"aggregate result","unexpected":false,"purpose":"Mercado Libre built its internal credit score to assess merchants on its platform for Mercado Crédito loans from their transaction, reputation and industry data.","system":"Mercado Libre internal credit rating (machine-learning credit score) for Mercado Crédito","model_version":null,"developer":"Mercado Libre","deployer":"Mercado Libre (Mercado Crédito), lending to merchants in Argentina","location":"Argentina","domain":"Small-business lending","segment":null,"benefit_type":"Improved Task Accuracy","work_activities":["4.A.2.a.1","4.A.2.a.4"],"taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"organizational","depth":2,"reason":"The machine-learning score separated defaults better than bureau scores, with an AUROC of 0.76 against 0.64 and 0.68."},{"group":"societal","depth":2,"reason":"About 30% of the loan portfolio went to merchants that the bureau rated high risk, whom the authors describe as otherwise excluded from credit."}],"ai_role":{"status":"assessed","statement":"The paper compares default prediction by the machine-learning score with prediction by bureau scores on the same 7,300 observations, which isolates the model's part in prediction. 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Pablo Zbinden discloses an employment relationship and financial investments in Mercado Libre, and the data came from Mercado Libre.","title":"BigTech and the changing structure of financial intermediation (BIS Working Paper 779)","url":"https://www.bis.org/publ/work779.pdf","date":"2019-04-08","written_by":"makers","quote":"data from Mercado Libre show that credit models using machine learning and data from the e-commerce platform are better at predicting losses than traditional credit bureau ratings"}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].upstream_sources":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"strategic_assessments[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"strategic assessments"},"summary":{"state":"value_present","value":{"kind":"record_fields","fields":{"summary":"Using 2017 loans in Argentina, BIS economists and a Mercado Libre employee found that the firm's machine-learning credit score predicted defaults with an AUROC of 0.76, against 0.64 for bureau scores.","account":"Mercado Libre lends to small merchants on its e-commerce platform through Mercado Crédito. Its internal credit rating uses machine learning on platform data on sales, reputation and industry characteristics. Economists from the Bank for International Settlements, the Financial Stability Board and the Graduate Institute, Geneva, wrote the paper with a Mercado Libre employee. He disclosed his employment and investments in the company, and the data came from Mercado Libre. Using 2017 loan data from Argentina, the authors compared the internal rating with the local credit bureau's ratings. Loss rates ranged from 0.0% to 10.2% across the internal rating's groups and from 0.7% to 2.8% across the bureau's. In default regressions on 7,300 observations, the machine-learning score had an area under the ROC curve of 0.76. Bureau scores reached 0.64 alone and 0.68 with borrower characteristics. About 30% of the portfolio fell in the bureau's high-risk group, which had a 2.8% loss rate. The authors write that it remains to be verified whether the model predicts defaults better over a full business and financial cycle."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["summary","account"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].autonomy_permissions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].component_role":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].deployment_configuration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].inputs_outputs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_name":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].product_name":{"state":"value_present","value":{"kind":"record_fields","fields":{"system":"Mercado Libre internal credit rating (machine-learning credit score) for Mercado Crédito"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["system"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].provider":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"taxonomy_crosswalks[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"title":{"state":"value_present","value":{"kind":"record_fields","fields":{"title":"A BIS paper co-written by a Mercado Libre employee finds that the firm's machine-learning credit score predicted defaults better than bureau scores","headline":"*Mercado Libre credit score* predicted defaults better than the bureau"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["title","headline"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"version":{"state":"value_present","value":{"kind":"identifiers","identifiers":["b56ebf1dbbcc53029cf01bb91a4e3da7afcdc2be1d343990a1f46f287f08b1f8"],"role":"SHA-256 of the complete public record and its assessments"},"provenance":{"kind":"curatorial","basis":"SHA-256 of the public record, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"workflow":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"Mercado Libre lends to small merchants on its e-commerce platform through Mercado Crédito. Its internal credit rating uses machine learning on platform data on sales, reputation and industry characteristics. Economists from the Bank for International Settlements, the Financial Stability Board and the Graduate Institute, Geneva, wrote the paper with a Mercado Libre employee. He disclosed his employment and investments in the company, and the data came from Mercado Libre. Using 2017 loan data from Argentina, the authors compared the internal rating with the local credit bureau's ratings. Loss rates ranged from 0.0% to 10.2% across the internal rating's groups and from 0.7% to 2.8% across the bureau's. In default regressions on 7,300 observations, the machine-learning score had an area under the ROC curve of 0.76. Bureau scores reached 0.64 alone and 0.68 with borrower characteristics. About 30% of the portfolio fell in the bureau's high-risk group, which had a 2.8% loss rate. The authors write that it remains to be verified whether the model predicts defaults better over a full business and financial cycle.","replication":{"produced_by":["The score drew on platform data on transactions, reputation and industry characteristics.","Mercado Libre lent through Mercado Crédito without asking merchants for further documents."],"must_hold":["Merchants must trade on the platform to generate the data that the score uses.","The score's edge over bureau ratings has yet to be shown over a full business and financial cycle."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":29,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":27,"review_not_assessed":62,"not_assessed":0,"complete":false,"by_state":{"value_present":29,"not_reported":0,"unresolved":62,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"b8518df1f418f4716a86f8ecccfb4f4394fa87733e4356d3c8e5e29855151184"}