{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EN3IX7HIDOTEOC4FX2BFYZP62V","short_pith_number":"pith:EN3IX7HI","canonical_record":{"source":{"id":"1904.11376","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-12T12:16:15Z","cross_cats_sorted":["q-fin.RM","stat.ML"],"title_canon_sha256":"ea22a7ebb888708c1ac1fc5087d9a79e102e679b3e56ac19598c9667a4eede2b","abstract_canon_sha256":"b6f5f3ae0352aa50f981b89439b2981487d86b9436fe0a0a7c8713235f533868"},"schema_version":"1.0"},"canonical_sha256":"23768bfce81ba6470b85be825c65fed55c21c26ef95f11610cb80ff2beb10668","source":{"kind":"arxiv","id":"1904.11376","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.11376","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"arxiv_version","alias_value":"1904.11376v2","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.11376","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"pith_short_12","alias_value":"EN3IX7HIDOTE","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"pith_short_16","alias_value":"EN3IX7HIDOTEOC4F","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"pith_short_8","alias_value":"EN3IX7HI","created_at":"2026-07-05T03:16:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EN3IX7HIDOTEOC4FX2BFYZP62V","target":"record","payload":{"canonical_record":{"source":{"id":"1904.11376","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-12T12:16:15Z","cross_cats_sorted":["q-fin.RM","stat.ML"],"title_canon_sha256":"ea22a7ebb888708c1ac1fc5087d9a79e102e679b3e56ac19598c9667a4eede2b","abstract_canon_sha256":"b6f5f3ae0352aa50f981b89439b2981487d86b9436fe0a0a7c8713235f533868"},"schema_version":"1.0"},"canonical_sha256":"23768bfce81ba6470b85be825c65fed55c21c26ef95f11610cb80ff2beb10668","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:16:59.440054Z","signature_b64":"hByNZfNLs0qu6bF9YDAdZuL2S7jgviqRbb9A5KCwQ9SRX1+rTtlk85BFj37ArkQ7MAyhx0SbBS7jf1AoBdakDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23768bfce81ba6470b85be825c65fed55c21c26ef95f11610cb80ff2beb10668","last_reissued_at":"2026-07-05T03:16:59.439617Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:16:59.439617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.11376","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:16:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"koCsRQWU8VtSJiA042Jm7zYp8ShShaHQWDjxyTP4esNMchumMYkQTpf4vTdFFq3Y0UAuT07LMO1hPw6FoDXaAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T20:57:33.296298Z"},"content_sha256":"56b0caf61170f1445be1f8a12436174bce4e6ffa30adf3ab040d4fc0ae17de0a","schema_version":"1.0","event_id":"sha256:56b0caf61170f1445be1f8a12436174bce4e6ffa30adf3ab040d4fc0ae17de0a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EN3IX7HIDOTEOC4FX2BFYZP62V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Generative Models for Reject Inference in Credit Scoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.RM","stat.ML"],"primary_cat":"q-fin.CP","authors_text":"Kjersti Aas, Michael Kampffmeyer, Robert Jenssen, Rogelio A. Mancisidor","submitted_at":"2019-04-12T12:16:15Z","abstract_excerpt":"Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.11376","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/1904.11376/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:16:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0eZyiu9GvtMQyK16I/YWn+8TLq8M+1EdcnQRYXxPlt0z/Z87Ut+B1rCoAmZ6ObB37vXbj4PQ8RpKvdweYwuHDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T20:57:33.297147Z"},"content_sha256":"1ff90494f7ab6ccc8e3f839ae3565d4743fb3dab4dda4c0be18a09f80e7b1fb3","schema_version":"1.0","event_id":"sha256:1ff90494f7ab6ccc8e3f839ae3565d4743fb3dab4dda4c0be18a09f80e7b1fb3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EN3IX7HIDOTEOC4FX2BFYZP62V/bundle.json","state_url":"https://pith.science/pith/EN3IX7HIDOTEOC4FX2BFYZP62V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EN3IX7HIDOTEOC4FX2BFYZP62V/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-31T20:57:33Z","links":{"resolver":"https://pith.science/pith/EN3IX7HIDOTEOC4FX2BFYZP62V","bundle":"https://pith.science/pith/EN3IX7HIDOTEOC4FX2BFYZP62V/bundle.json","state":"https://pith.science/pith/EN3IX7HIDOTEOC4FX2BFYZP62V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EN3IX7HIDOTEOC4FX2BFYZP62V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EN3IX7HIDOTEOC4FX2BFYZP62V","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b6f5f3ae0352aa50f981b89439b2981487d86b9436fe0a0a7c8713235f533868","cross_cats_sorted":["q-fin.RM","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-12T12:16:15Z","title_canon_sha256":"ea22a7ebb888708c1ac1fc5087d9a79e102e679b3e56ac19598c9667a4eede2b"},"schema_version":"1.0","source":{"id":"1904.11376","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.11376","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"arxiv_version","alias_value":"1904.11376v2","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.11376","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"pith_short_12","alias_value":"EN3IX7HIDOTE","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"pith_short_16","alias_value":"EN3IX7HIDOTEOC4F","created_at":"2026-07-05T03:16:59Z"},{"alias_kind":"pith_short_8","alias_value":"EN3IX7HI","created_at":"2026-07-05T03:16:59Z"}],"graph_snapshots":[{"event_id":"sha256:1ff90494f7ab6ccc8e3f839ae3565d4743fb3dab4dda4c0be18a09f80e7b1fb3","target":"graph","created_at":"2026-07-05T03:16:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1904.11376/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models in","authors_text":"Kjersti Aas, Michael Kampffmeyer, Robert Jenssen, Rogelio A. Mancisidor","cross_cats":["q-fin.RM","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-12T12:16:15Z","title":"Deep Generative Models for Reject Inference in Credit Scoring"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.11376","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:56b0caf61170f1445be1f8a12436174bce4e6ffa30adf3ab040d4fc0ae17de0a","target":"record","created_at":"2026-07-05T03:16:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b6f5f3ae0352aa50f981b89439b2981487d86b9436fe0a0a7c8713235f533868","cross_cats_sorted":["q-fin.RM","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-04-12T12:16:15Z","title_canon_sha256":"ea22a7ebb888708c1ac1fc5087d9a79e102e679b3e56ac19598c9667a4eede2b"},"schema_version":"1.0","source":{"id":"1904.11376","kind":"arxiv","version":2}},"canonical_sha256":"23768bfce81ba6470b85be825c65fed55c21c26ef95f11610cb80ff2beb10668","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23768bfce81ba6470b85be825c65fed55c21c26ef95f11610cb80ff2beb10668","first_computed_at":"2026-07-05T03:16:59.439617Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:16:59.439617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hByNZfNLs0qu6bF9YDAdZuL2S7jgviqRbb9A5KCwQ9SRX1+rTtlk85BFj37ArkQ7MAyhx0SbBS7jf1AoBdakDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:16:59.440054Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.11376","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:56b0caf61170f1445be1f8a12436174bce4e6ffa30adf3ab040d4fc0ae17de0a","sha256:1ff90494f7ab6ccc8e3f839ae3565d4743fb3dab4dda4c0be18a09f80e7b1fb3"],"state_sha256":"2832f67dea76f3bee0373ee92e08dbb73ae1fe09aeaba7bc28d01506f320b66d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RcLyW6+U3pwQ+jcG9BHBm+0wmQCuQ0g2LVhUBxYjjXkYpWKqDClfO7ILJU+do/uJmayBqDf7hu+I4vvvb4OyCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T20:57:33.300949Z","bundle_sha256":"2e237533354bafdc93ec2473c0f0324d4be790e5dfd6175e86cbb52c4707bde3"}}