{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:OPALWW45BLAJZRE6BV4T2RUF2W","short_pith_number":"pith:OPALWW45","canonical_record":{"source":{"id":"2102.05784","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2021-02-11T00:10:56Z","cross_cats_sorted":[],"title_canon_sha256":"a37c99d12f73be86d3e4024506a01ae2a2f0394b1ca156ba6c96488f9a73a314","abstract_canon_sha256":"359b59850441b322c9d236c6950940862f9fbcdf847951cab705b93aee73e141"},"schema_version":"1.0"},"canonical_sha256":"73c0bb5b9d0ac09cc49e0d793d4685d5893d4a7553748417a599b8480357449c","source":{"kind":"arxiv","id":"2102.05784","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.05784","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"arxiv_version","alias_value":"2102.05784v1","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.05784","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_12","alias_value":"OPALWW45BLAJ","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_16","alias_value":"OPALWW45BLAJZRE6","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_8","alias_value":"OPALWW45","created_at":"2026-07-05T02:14:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:OPALWW45BLAJZRE6BV4T2RUF2W","target":"record","payload":{"canonical_record":{"source":{"id":"2102.05784","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2021-02-11T00:10:56Z","cross_cats_sorted":[],"title_canon_sha256":"a37c99d12f73be86d3e4024506a01ae2a2f0394b1ca156ba6c96488f9a73a314","abstract_canon_sha256":"359b59850441b322c9d236c6950940862f9fbcdf847951cab705b93aee73e141"},"schema_version":"1.0"},"canonical_sha256":"73c0bb5b9d0ac09cc49e0d793d4685d5893d4a7553748417a599b8480357449c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:14:34.463731Z","signature_b64":"qblnmNHyyUAI9tCARlx3DXHzYG7pQ0fwJ4VMFrnYApEpn/2JrpxtJmK3S3e772FP8m+rZDUNjDozEX0XeTC2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73c0bb5b9d0ac09cc49e0d793d4685d5893d4a7553748417a599b8480357449c","last_reissued_at":"2026-07-05T02:14:34.463271Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:14:34.463271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.05784","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:14:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wBNWSYtIwTNNJPaXBldcPpOk+nqTh3StMK4AUTEZxZFtAqnsg7vIncn2t8XgDqIp4JcNYnDW1rCYLecVgqRdDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:54:11.778934Z"},"content_sha256":"eee94ff5bce1d21af27369d673a761b200c02724db3a6aa23265d2ae06f74613","schema_version":"1.0","event_id":"sha256:eee94ff5bce1d21af27369d673a761b200c02724db3a6aa23265d2ae06f74613"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:OPALWW45BLAJZRE6BV4T2RUF2W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Representations in P&C Actuarial Science with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Christopher Blier-Wong, Etienne Marceau, H\\'el\\`ene Cossette, Jean-Thomas Baillargeon, Luc Lamontagne","submitted_at":"2021-02-11T00:10:56Z","abstract_excerpt":"Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many emerging data sources (text, images, sensors) may complement traditional data to provide better insights to predict the future losses in an insurance contract. This paper presents some of these emerging data sources and presents a unified framework for actuaries to incorporate these in existing ratemaking models. Our approach stems from representation learning, whose goal is to create representations of raw data. A u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.05784","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/2102.05784/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":"2026-07-05T02:14:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nojZaGXZNr1p1grh57MPk8sxNzvp7hgaOx8W3VlLsxbWFGRnj/R1cMdDSokg+yVjfjFRLRtXZnxn/i68onTnCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:54:11.779429Z"},"content_sha256":"0350af4ee9ebf260c17d5a12d5fe45651ac3d598de45abde054f207b6a2b3082","schema_version":"1.0","event_id":"sha256:0350af4ee9ebf260c17d5a12d5fe45651ac3d598de45abde054f207b6a2b3082"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OPALWW45BLAJZRE6BV4T2RUF2W/bundle.json","state_url":"https://pith.science/pith/OPALWW45BLAJZRE6BV4T2RUF2W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OPALWW45BLAJZRE6BV4T2RUF2W/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":"2026-08-04T19:54:11Z","links":{"resolver":"https://pith.science/pith/OPALWW45BLAJZRE6BV4T2RUF2W","bundle":"https://pith.science/pith/OPALWW45BLAJZRE6BV4T2RUF2W/bundle.json","state":"https://pith.science/pith/OPALWW45BLAJZRE6BV4T2RUF2W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OPALWW45BLAJZRE6BV4T2RUF2W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:OPALWW45BLAJZRE6BV4T2RUF2W","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":"359b59850441b322c9d236c6950940862f9fbcdf847951cab705b93aee73e141","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2021-02-11T00:10:56Z","title_canon_sha256":"a37c99d12f73be86d3e4024506a01ae2a2f0394b1ca156ba6c96488f9a73a314"},"schema_version":"1.0","source":{"id":"2102.05784","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.05784","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"arxiv_version","alias_value":"2102.05784v1","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.05784","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_12","alias_value":"OPALWW45BLAJ","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_16","alias_value":"OPALWW45BLAJZRE6","created_at":"2026-07-05T02:14:34Z"},{"alias_kind":"pith_short_8","alias_value":"OPALWW45","created_at":"2026-07-05T02:14:34Z"}],"graph_snapshots":[{"event_id":"sha256:0350af4ee9ebf260c17d5a12d5fe45651ac3d598de45abde054f207b6a2b3082","target":"graph","created_at":"2026-07-05T02:14:34Z","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/2102.05784/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many emerging data sources (text, images, sensors) may complement traditional data to provide better insights to predict the future losses in an insurance contract. This paper presents some of these emerging data sources and presents a unified framework for actuaries to incorporate these in existing ratemaking models. Our approach stems from representation learning, whose goal is to create representations of raw data. A u","authors_text":"Christopher Blier-Wong, Etienne Marceau, H\\'el\\`ene Cossette, Jean-Thomas Baillargeon, Luc Lamontagne","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2021-02-11T00:10:56Z","title":"Rethinking Representations in P&C Actuarial Science with Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.05784","kind":"arxiv","version":1},"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:eee94ff5bce1d21af27369d673a761b200c02724db3a6aa23265d2ae06f74613","target":"record","created_at":"2026-07-05T02:14:34Z","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":"359b59850441b322c9d236c6950940862f9fbcdf847951cab705b93aee73e141","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2021-02-11T00:10:56Z","title_canon_sha256":"a37c99d12f73be86d3e4024506a01ae2a2f0394b1ca156ba6c96488f9a73a314"},"schema_version":"1.0","source":{"id":"2102.05784","kind":"arxiv","version":1}},"canonical_sha256":"73c0bb5b9d0ac09cc49e0d793d4685d5893d4a7553748417a599b8480357449c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73c0bb5b9d0ac09cc49e0d793d4685d5893d4a7553748417a599b8480357449c","first_computed_at":"2026-07-05T02:14:34.463271Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:14:34.463271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qblnmNHyyUAI9tCARlx3DXHzYG7pQ0fwJ4VMFrnYApEpn/2JrpxtJmK3S3e772FP8m+rZDUNjDozEX0XeTC2AA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:14:34.463731Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.05784","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eee94ff5bce1d21af27369d673a761b200c02724db3a6aa23265d2ae06f74613","sha256:0350af4ee9ebf260c17d5a12d5fe45651ac3d598de45abde054f207b6a2b3082"],"state_sha256":"07d0043d163469af4547c18a8457552b05ea2b556cc3a0078ca2e3fd1398ad19"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QEnTfSe2VdY1khR0CuC9rt+mVnTCimluEYthKtscAKegLEshixvDZNV+WgPJYUUw4Tqz5Gbt/OZNlrfbN6E5CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T19:54:11.783580Z","bundle_sha256":"34c42ff9b4c97a7cb63da518e13762693310fa92c09087f562b38aadcbe3f0a5"}}