{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4R6AMQIYSK5ZPFCDCV2UAY3GKV","short_pith_number":"pith:4R6AMQIY","schema_version":"1.0","canonical_sha256":"e47c06411892bb9794431575406366556814ee309aa535f608cf66b536750218","source":{"kind":"arxiv","id":"2501.10451","version":2},"attestation_state":"computed","paper":{"title":"Automating Credit Card Limit Adjustments Using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Diego Pestana, Enrique Areyan Viqueira","submitted_at":"2025-01-14T17:22:57Z","abstract_excerpt":"Venezuelan banks have historically made credit card limit adjustment decisions manually through committees. However, since the number of credit card holders in Venezuela is expected to increase in the upcoming months due to economic improvements, manual decisions are starting to become unfeasible. In this project, a machine learning model that uses cost-sensitive learning is proposed to automate the task of handing out credit card limit increases. To accomplish this, several neural network and XGBoost models are trained and compared, leveraging Venezolano de Credito's data and using grid searc"},"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":"2501.10451","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-14T17:22:57Z","cross_cats_sorted":[],"title_canon_sha256":"12e63d54a33d52a2f45a90ceac75cbcd2660b0b207a92e44e43a570204bf57a6","abstract_canon_sha256":"eba40587e90d7896decc4679da0960c0eede3196cddd823c75e1422fed51dfb0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:31.322222Z","signature_b64":"BQtP3qz/G32CgmG0NN3b4JZh3nPoRhSaOWTq/iQw5XwLEoskv5QIrkjx2LQHOsnPiAaI5rkIjEZ/MLVgWb/cBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e47c06411892bb9794431575406366556814ee309aa535f608cf66b536750218","last_reissued_at":"2026-07-05T10:51:31.321726Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:31.321726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automating Credit Card Limit Adjustments Using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Diego Pestana, Enrique Areyan Viqueira","submitted_at":"2025-01-14T17:22:57Z","abstract_excerpt":"Venezuelan banks have historically made credit card limit adjustment decisions manually through committees. However, since the number of credit card holders in Venezuela is expected to increase in the upcoming months due to economic improvements, manual decisions are starting to become unfeasible. In this project, a machine learning model that uses cost-sensitive learning is proposed to automate the task of handing out credit card limit increases. To accomplish this, several neural network and XGBoost models are trained and compared, leveraging Venezolano de Credito's data and using grid searc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10451","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/2501.10451/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":"2501.10451","created_at":"2026-07-05T10:51:31.321779+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.10451v2","created_at":"2026-07-05T10:51:31.321779+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10451","created_at":"2026-07-05T10:51:31.321779+00:00"},{"alias_kind":"pith_short_12","alias_value":"4R6AMQIYSK5Z","created_at":"2026-07-05T10:51:31.321779+00:00"},{"alias_kind":"pith_short_16","alias_value":"4R6AMQIYSK5ZPFCD","created_at":"2026-07-05T10:51:31.321779+00:00"},{"alias_kind":"pith_short_8","alias_value":"4R6AMQIY","created_at":"2026-07-05T10:51:31.321779+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/4R6AMQIYSK5ZPFCDCV2UAY3GKV","json":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV.json","graph_json":"https://pith.science/api/pith-number/4R6AMQIYSK5ZPFCDCV2UAY3GKV/graph.json","events_json":"https://pith.science/api/pith-number/4R6AMQIYSK5ZPFCDCV2UAY3GKV/events.json","paper":"https://pith.science/paper/4R6AMQIY"},"agent_actions":{"view_html":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV","download_json":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV.json","view_paper":"https://pith.science/paper/4R6AMQIY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.10451&json=true","fetch_graph":"https://pith.science/api/pith-number/4R6AMQIYSK5ZPFCDCV2UAY3GKV/graph.json","fetch_events":"https://pith.science/api/pith-number/4R6AMQIYSK5ZPFCDCV2UAY3GKV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV/action/storage_attestation","attest_author":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV/action/author_attestation","sign_citation":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV/action/citation_signature","submit_replication":"https://pith.science/pith/4R6AMQIYSK5ZPFCDCV2UAY3GKV/action/replication_record"}},"created_at":"2026-07-05T10:51:31.321779+00:00","updated_at":"2026-07-05T10:51:31.321779+00:00"}