{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CTSB7GAT6U4HU347MC6OUBVNYW","short_pith_number":"pith:CTSB7GAT","schema_version":"1.0","canonical_sha256":"14e41f9813f5387a6f9f60bcea06adc58add9252e363004f4c944e6418df6109","source":{"kind":"arxiv","id":"2605.18147","version":1},"attestation_state":"computed","paper":{"title":"Foundation Models for Credit Risk Prediction: A Game Changer?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andreas Goethals, Bart Baesens, Christophe Mues, Cristi\\'an Bravo, David Martens, Maria Oskarsd\\'ottir, Seppe vanden Broucke, Simon De Vos, Stefan Lessmann, Tim Verdonck, Victor Medina-Olivares, Wouter Verbeke","submitted_at":"2026-05-18T09:52:48Z","abstract_excerpt":"Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses. Extensive research has introduced new modeling techniques, complemented by large-scale benchmarking studies consolidating the state-of-the-art. Today, quasi-standards such as gradient-boosting models paired with SHAP explainers have emerged, yet continuous improvement of risk models remains a top priority. Concurrently, rapid advancements in AI, most notably large language models, have disrupted predictive modeling paradigms. Foundation mo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.18147","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-18T09:52:48Z","cross_cats_sorted":[],"title_canon_sha256":"268bc9a5196598fe1fab99876ca677aef93517f764afb1da72fc32079b8a61aa","abstract_canon_sha256":"fb3f1132268baca980a78861d450d3e20b87d0d99d62aacb5ae78f2d6f481f35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:48.339232Z","signature_b64":"Y+3/un43ow4OFB17RiW6vwCpZnrpxueuLx/A48ep3cIWnlKEsgURqwZYxLcm7umDZqmgsSmwCEj3QjXEcMRVBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14e41f9813f5387a6f9f60bcea06adc58add9252e363004f4c944e6418df6109","last_reissued_at":"2026-05-20T00:05:48.338629Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:48.338629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Foundation Models for Credit Risk Prediction: A Game Changer?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andreas Goethals, Bart Baesens, Christophe Mues, Cristi\\'an Bravo, David Martens, Maria Oskarsd\\'ottir, Seppe vanden Broucke, Simon De Vos, Stefan Lessmann, Tim Verdonck, Victor Medina-Olivares, Wouter Verbeke","submitted_at":"2026-05-18T09:52:48Z","abstract_excerpt":"Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses. Extensive research has introduced new modeling techniques, complemented by large-scale benchmarking studies consolidating the state-of-the-art. Today, quasi-standards such as gradient-boosting models paired with SHAP explainers have emerged, yet continuous improvement of risk models remains a top priority. Concurrently, rapid advancements in AI, most notably large language models, have disrupted predictive modeling paradigms. Foundation mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18147","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.18147/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T23:41:59.098827Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:35.376462Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"f4321906b82695d8ca5f9c8ffd522a3e903bf1bebc966f93490269922d38af0b"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.18147","created_at":"2026-05-20T00:05:48.338720+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18147v1","created_at":"2026-05-20T00:05:48.338720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18147","created_at":"2026-05-20T00:05:48.338720+00:00"},{"alias_kind":"pith_short_12","alias_value":"CTSB7GAT6U4H","created_at":"2026-05-20T00:05:48.338720+00:00"},{"alias_kind":"pith_short_16","alias_value":"CTSB7GAT6U4HU347","created_at":"2026-05-20T00:05:48.338720+00:00"},{"alias_kind":"pith_short_8","alias_value":"CTSB7GAT","created_at":"2026-05-20T00:05:48.338720+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW","json":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW.json","graph_json":"https://pith.science/api/pith-number/CTSB7GAT6U4HU347MC6OUBVNYW/graph.json","events_json":"https://pith.science/api/pith-number/CTSB7GAT6U4HU347MC6OUBVNYW/events.json","paper":"https://pith.science/paper/CTSB7GAT"},"agent_actions":{"view_html":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW","download_json":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW.json","view_paper":"https://pith.science/paper/CTSB7GAT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18147&json=true","fetch_graph":"https://pith.science/api/pith-number/CTSB7GAT6U4HU347MC6OUBVNYW/graph.json","fetch_events":"https://pith.science/api/pith-number/CTSB7GAT6U4HU347MC6OUBVNYW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW/action/storage_attestation","attest_author":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW/action/author_attestation","sign_citation":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW/action/citation_signature","submit_replication":"https://pith.science/pith/CTSB7GAT6U4HU347MC6OUBVNYW/action/replication_record"}},"created_at":"2026-05-20T00:05:48.338720+00:00","updated_at":"2026-05-20T00:05:48.338720+00:00"}