{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UYWAHGSTPGWHZETUAIW3QIXBK7","short_pith_number":"pith:UYWAHGST","schema_version":"1.0","canonical_sha256":"a62c039a5379ac7c9274022db822e157d81acb2bb2474aa55f314f9b870e2fdb","source":{"kind":"arxiv","id":"2502.00201","version":2},"attestation_state":"computed","paper":{"title":"Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-fin.ST"],"primary_cat":"cs.LG","authors_text":"Chuanhao Nie, Chuqing Zhao, Yisong Chen, Yixin Xu, Yixin Zhang","submitted_at":"2025-01-31T22:31:50Z","abstract_excerpt":"This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were eval"},"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":"2502.00201","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-31T22:31:50Z","cross_cats_sorted":["cs.AI","q-fin.ST"],"title_canon_sha256":"cb876333b0d87661168dbdc75023e1e1ac35b711a48c6e9fe05843d8e12af776","abstract_canon_sha256":"fdc0d55438b5597404b3bf9e7e97cdb79fcefc1e966df859460ff22d1ce719ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:31.202176Z","signature_b64":"7L+DB3izenyMN3HbsJ2GtY3owa9TzTheGFKFmBCaQiKBadXV6OLEa+WjH34K1H93ByaeNqpQiZp3Q0ffDAh6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a62c039a5379ac7c9274022db822e157d81acb2bb2474aa55f314f9b870e2fdb","last_reissued_at":"2026-07-05T11:45:31.201643Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:31.201643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-fin.ST"],"primary_cat":"cs.LG","authors_text":"Chuanhao Nie, Chuqing Zhao, Yisong Chen, Yixin Xu, Yixin Zhang","submitted_at":"2025-01-31T22:31:50Z","abstract_excerpt":"This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were eval"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00201","kind":"arxiv","version":2},"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/2502.00201/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":"2502.00201","created_at":"2026-07-05T11:45:31.201707+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.00201v2","created_at":"2026-07-05T11:45:31.201707+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00201","created_at":"2026-07-05T11:45:31.201707+00:00"},{"alias_kind":"pith_short_12","alias_value":"UYWAHGSTPGWH","created_at":"2026-07-05T11:45:31.201707+00:00"},{"alias_kind":"pith_short_16","alias_value":"UYWAHGSTPGWHZETU","created_at":"2026-07-05T11:45:31.201707+00:00"},{"alias_kind":"pith_short_8","alias_value":"UYWAHGST","created_at":"2026-07-05T11:45:31.201707+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/UYWAHGSTPGWHZETUAIW3QIXBK7","json":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7.json","graph_json":"https://pith.science/api/pith-number/UYWAHGSTPGWHZETUAIW3QIXBK7/graph.json","events_json":"https://pith.science/api/pith-number/UYWAHGSTPGWHZETUAIW3QIXBK7/events.json","paper":"https://pith.science/paper/UYWAHGST"},"agent_actions":{"view_html":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7","download_json":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7.json","view_paper":"https://pith.science/paper/UYWAHGST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.00201&json=true","fetch_graph":"https://pith.science/api/pith-number/UYWAHGSTPGWHZETUAIW3QIXBK7/graph.json","fetch_events":"https://pith.science/api/pith-number/UYWAHGSTPGWHZETUAIW3QIXBK7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7/action/storage_attestation","attest_author":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7/action/author_attestation","sign_citation":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7/action/citation_signature","submit_replication":"https://pith.science/pith/UYWAHGSTPGWHZETUAIW3QIXBK7/action/replication_record"}},"created_at":"2026-07-05T11:45:31.201707+00:00","updated_at":"2026-07-05T11:45:31.201707+00:00"}