{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DFU6VE2ZLGCVFVCKELOTW47WFL","short_pith_number":"pith:DFU6VE2Z","schema_version":"1.0","canonical_sha256":"1969ea9359598552d44a22dd3b73f62aec4b595a72528fb0e803424f38e15cce","source":{"kind":"arxiv","id":"2507.22758","version":1},"attestation_state":"computed","paper":{"title":"MASCA: LLM based-Multi Agents System for Credit Assessment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"cs.CL","authors_text":"Gautam Jajoo, Pranjal A Chitale, Saksham Agarwal","submitted_at":"2025-07-30T15:19:38Z","abstract_excerpt":"Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment remains an underexplored challenge, traditionally dependent on rule-based methods and statistical models. In this paper, we introduce MASCA, an LLM-driven multi-agent system designed to enhance credit evaluation by mirroring real-world decision-making processes. The framework employs a layered architecture where specialized LLM-based agents collaboratively tackle sub-tasks. Additionally, we integrate contrastive learnin"},"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":"2507.22758","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-30T15:19:38Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"857433ed4dcad4ba638255b59b03634755a982c7d668c9f7296c91ee78bbe399","abstract_canon_sha256":"708a0433ec43916eab0de436f09e5ec1d76e25001e8d64cf547eb0d9aa8534fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:48.437375Z","signature_b64":"IknUa9104IZrWVMTEWrGxKD0m404/ywZPY27MnfKuFq6q4g2ry7e6BdtRDBZU3Mi8JzuT8hdZJN0QSg3d7h7BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1969ea9359598552d44a22dd3b73f62aec4b595a72528fb0e803424f38e15cce","last_reissued_at":"2026-07-05T11:45:48.436878Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:48.436878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MASCA: LLM based-Multi Agents System for Credit Assessment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"cs.CL","authors_text":"Gautam Jajoo, Pranjal A Chitale, Saksham Agarwal","submitted_at":"2025-07-30T15:19:38Z","abstract_excerpt":"Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment remains an underexplored challenge, traditionally dependent on rule-based methods and statistical models. In this paper, we introduce MASCA, an LLM-driven multi-agent system designed to enhance credit evaluation by mirroring real-world decision-making processes. The framework employs a layered architecture where specialized LLM-based agents collaboratively tackle sub-tasks. Additionally, we integrate contrastive learnin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.22758","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/2507.22758/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":"2507.22758","created_at":"2026-07-05T11:45:48.436939+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.22758v1","created_at":"2026-07-05T11:45:48.436939+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.22758","created_at":"2026-07-05T11:45:48.436939+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFU6VE2ZLGCV","created_at":"2026-07-05T11:45:48.436939+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFU6VE2ZLGCVFVCK","created_at":"2026-07-05T11:45:48.436939+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFU6VE2Z","created_at":"2026-07-05T11:45:48.436939+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/DFU6VE2ZLGCVFVCKELOTW47WFL","json":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL.json","graph_json":"https://pith.science/api/pith-number/DFU6VE2ZLGCVFVCKELOTW47WFL/graph.json","events_json":"https://pith.science/api/pith-number/DFU6VE2ZLGCVFVCKELOTW47WFL/events.json","paper":"https://pith.science/paper/DFU6VE2Z"},"agent_actions":{"view_html":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL","download_json":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL.json","view_paper":"https://pith.science/paper/DFU6VE2Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.22758&json=true","fetch_graph":"https://pith.science/api/pith-number/DFU6VE2ZLGCVFVCKELOTW47WFL/graph.json","fetch_events":"https://pith.science/api/pith-number/DFU6VE2ZLGCVFVCKELOTW47WFL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL/action/storage_attestation","attest_author":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL/action/author_attestation","sign_citation":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL/action/citation_signature","submit_replication":"https://pith.science/pith/DFU6VE2ZLGCVFVCKELOTW47WFL/action/replication_record"}},"created_at":"2026-07-05T11:45:48.436939+00:00","updated_at":"2026-07-05T11:45:48.436939+00:00"}