{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:27NLZBUN76OZ4TAORVG6YFXLCJ","short_pith_number":"pith:27NLZBUN","schema_version":"1.0","canonical_sha256":"d7dabc868dff9d9e4c0e8d4dec16eb125871c374125aabcfee85a7fd92b25113","source":{"kind":"arxiv","id":"2507.13685","version":1},"attestation_state":"computed","paper":{"title":"Kolmogorov-Arnold Networks-based GRU and LSTM for Loan Default Early Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anthony Graham Bellotti, Boon Giin Lee, Chang Chuan Goh, Ying Zhang, Yue Yang, Yuxiang Lin, Zihan Su","submitted_at":"2025-07-18T06:20:41Z","abstract_excerpt":"This study addresses a critical challenge in time series anomaly detection: enhancing the predictive capability of loan default models more than three months in advance to enable early identification of default events, helping financial institutions implement preventive measures before risk events materialize. Existing methods have significant drawbacks, such as their lack of accuracy in early predictions and their dependence on training and testing within the same year and specific time frames. These issues limit their practical use, particularly with out-of-time data. To address these, the s"},"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":"2507.13685","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-18T06:20:41Z","cross_cats_sorted":[],"title_canon_sha256":"35aed485a67a3338b4a8f93e51a37a5391fcd6a4f644a9258adfb3ed575d3cf3","abstract_canon_sha256":"c5a24031a0c4794be487ce5c7e998dbe30f7a8f47d6981f41c2037ceb73f2819"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:21.237453Z","signature_b64":"1+DpRY3HFKZLf19x4wVdPx/ED17KKn4UZewxEC7amaR28NyTZWp1/YULVVPXXaNP7l7ztN/FT2Mu/iWI+JvTAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7dabc868dff9d9e4c0e8d4dec16eb125871c374125aabcfee85a7fd92b25113","last_reissued_at":"2026-07-05T11:39:21.236992Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:21.236992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kolmogorov-Arnold Networks-based GRU and LSTM for Loan Default Early Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anthony Graham Bellotti, Boon Giin Lee, Chang Chuan Goh, Ying Zhang, Yue Yang, Yuxiang Lin, Zihan Su","submitted_at":"2025-07-18T06:20:41Z","abstract_excerpt":"This study addresses a critical challenge in time series anomaly detection: enhancing the predictive capability of loan default models more than three months in advance to enable early identification of default events, helping financial institutions implement preventive measures before risk events materialize. Existing methods have significant drawbacks, such as their lack of accuracy in early predictions and their dependence on training and testing within the same year and specific time frames. These issues limit their practical use, particularly with out-of-time data. To address these, the s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13685","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/2507.13685/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2507.13685","created_at":"2026-07-05T11:39:21.237045+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.13685v1","created_at":"2026-07-05T11:39:21.237045+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13685","created_at":"2026-07-05T11:39:21.237045+00:00"},{"alias_kind":"pith_short_12","alias_value":"27NLZBUN76OZ","created_at":"2026-07-05T11:39:21.237045+00:00"},{"alias_kind":"pith_short_16","alias_value":"27NLZBUN76OZ4TAO","created_at":"2026-07-05T11:39:21.237045+00:00"},{"alias_kind":"pith_short_8","alias_value":"27NLZBUN","created_at":"2026-07-05T11:39:21.237045+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/27NLZBUN76OZ4TAORVG6YFXLCJ","json":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ.json","graph_json":"https://pith.science/api/pith-number/27NLZBUN76OZ4TAORVG6YFXLCJ/graph.json","events_json":"https://pith.science/api/pith-number/27NLZBUN76OZ4TAORVG6YFXLCJ/events.json","paper":"https://pith.science/paper/27NLZBUN"},"agent_actions":{"view_html":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ","download_json":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ.json","view_paper":"https://pith.science/paper/27NLZBUN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.13685&json=true","fetch_graph":"https://pith.science/api/pith-number/27NLZBUN76OZ4TAORVG6YFXLCJ/graph.json","fetch_events":"https://pith.science/api/pith-number/27NLZBUN76OZ4TAORVG6YFXLCJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ/action/storage_attestation","attest_author":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ/action/author_attestation","sign_citation":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ/action/citation_signature","submit_replication":"https://pith.science/pith/27NLZBUN76OZ4TAORVG6YFXLCJ/action/replication_record"}},"created_at":"2026-07-05T11:39:21.237045+00:00","updated_at":"2026-07-05T11:39:21.237045+00:00"}