{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MPNCMKI6JHDSBFN36UTXL4BTRG","short_pith_number":"pith:MPNCMKI6","schema_version":"1.0","canonical_sha256":"63da26291e49c72095bbf52775f033899d3e27a3042d3186a279e8a72978756d","source":{"kind":"arxiv","id":"2605.22892","version":1},"attestation_state":"computed","paper":{"title":"Is TabPFN the Silver Bullet for Insurance Pricing?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-fin.RM","authors_text":"Bruno Deprez, Tim Verdonck, Wouter Verbeke","submitted_at":"2026-05-21T13:59:06Z","abstract_excerpt":"Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machines as the leading machine learning alternative. Tabular foundation models (TFMs) offer a fundamentally different paradigm. By pre-training on large collections of synthetic datasets, TFMs enable inference on new data through in-context learning, without any dataset-specific fitting or hyperparameter tuning. This paper presents a first empirical evaluation of TabPFN for motor insurance pricing, benchmarking it against GLM and XGBoost on two publicl"},"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":"2605.22892","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.RM","submitted_at":"2026-05-21T13:59:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b2869bd1c3e0c87b73c53b828021789477167ddfa20648a183ea43e95e323efd","abstract_canon_sha256":"3be5d2627fd2ee8456d28fba856d006e90888f778b4dec18c6cb7eaaa0bdacac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:28.953010Z","signature_b64":"Snp+w9DYHBuFL2KYFZo457j4+3b2puxfFrn1SChu+8tJ1d2bLi3oTDIClPrza/kGv6XyJpoD1VXGz6i9NQsfCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63da26291e49c72095bbf52775f033899d3e27a3042d3186a279e8a72978756d","last_reissued_at":"2026-05-25T02:01:28.952590Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:28.952590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is TabPFN the Silver Bullet for Insurance Pricing?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-fin.RM","authors_text":"Bruno Deprez, Tim Verdonck, Wouter Verbeke","submitted_at":"2026-05-21T13:59:06Z","abstract_excerpt":"Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machines as the leading machine learning alternative. Tabular foundation models (TFMs) offer a fundamentally different paradigm. By pre-training on large collections of synthetic datasets, TFMs enable inference on new data through in-context learning, without any dataset-specific fitting or hyperparameter tuning. This paper presents a first empirical evaluation of TabPFN for motor insurance pricing, benchmarking it against GLM and XGBoost on two publicl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22892","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/2605.22892/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":"2605.22892","created_at":"2026-05-25T02:01:28.952648+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.22892v1","created_at":"2026-05-25T02:01:28.952648+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22892","created_at":"2026-05-25T02:01:28.952648+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPNCMKI6JHDS","created_at":"2026-05-25T02:01:28.952648+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPNCMKI6JHDSBFN3","created_at":"2026-05-25T02:01:28.952648+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPNCMKI6","created_at":"2026-05-25T02:01:28.952648+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/MPNCMKI6JHDSBFN36UTXL4BTRG","json":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG.json","graph_json":"https://pith.science/api/pith-number/MPNCMKI6JHDSBFN36UTXL4BTRG/graph.json","events_json":"https://pith.science/api/pith-number/MPNCMKI6JHDSBFN36UTXL4BTRG/events.json","paper":"https://pith.science/paper/MPNCMKI6"},"agent_actions":{"view_html":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG","download_json":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG.json","view_paper":"https://pith.science/paper/MPNCMKI6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.22892&json=true","fetch_graph":"https://pith.science/api/pith-number/MPNCMKI6JHDSBFN36UTXL4BTRG/graph.json","fetch_events":"https://pith.science/api/pith-number/MPNCMKI6JHDSBFN36UTXL4BTRG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG/action/storage_attestation","attest_author":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG/action/author_attestation","sign_citation":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG/action/citation_signature","submit_replication":"https://pith.science/pith/MPNCMKI6JHDSBFN36UTXL4BTRG/action/replication_record"}},"created_at":"2026-05-25T02:01:28.952648+00:00","updated_at":"2026-05-25T02:01:28.952648+00:00"}