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Sihan Zhou
Enterprise Risk Management, Columbia University, NY, USA
Computer Science & IT

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Accounting-Aware Evidence Retrieval for Institutional Due Diligence of Tokenized Trade Receivable RWA Yuanzheng Chen; Sihan Zhou; Emma Lin
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.542

Abstract

Institutional investors evaluating tokenized real-world asset (RWA) transactions need retrieval systems that can answer short, ambiguous, and legally loaded due-diligence questions with traceable evidence. Trade receivable pools are especially difficult because the same question may require accounting policy, financial metrics, footnote disclosure, legal covenants, insurance language, servicer reporting, or waterfall mechanics. This study implements and evaluates an accounting-aware evidence-retrieval pipeline for tokenized trade receivable RWA due diligence. The main experiment uses the official FinDER benchmark with 5,703 query-evidence-answer triples, 6,121 annotated evidence references, and 5,830 deduplicated evidence passages derived from financial disclosures. The pipeline compares vanilla sparse retrieval, accounting-aware query rewriting, feature reranking, section-aware evidence selection, and calibrated abstention. On the official FinDER evaluation, query rewriting increased Recall@10 from 28.25% to 28.62%, reranking increased Recall@10 to 33.86% and answer-support accuracy to 24.57%, and section-aware evidence selection achieved 34.44% Recall@10, 24.04% nDCG@10, 8.32% EvidencePrecision@3, and 25.23% answer-support accuracy. The accounting-relevant subset, defined as Accounting, Financials, and Footnotes, achieved 37.10% Recall@10 and 26.54% answer-support accuracy. A supplementary stress check using a public receivables purchase agreement and SEC 2026-04 financial statement notes showed that the same retrieval logic can surface schedule, lock-box, GAAP, receivable, and note-disclosure evidence, while also highlighting the need for table extraction and field-level numerical validation. The findings support a narrower deployment claim: accounting-aware RAG can improve evidence discovery and analyst review, but it is not yet suitable for autonomous investment or accounting decision-making
Accounting-Aware Evidence-Constrained Agents for Disclosure, Settlement, and Secondary-Market Risk Monitoring in Tokenized Sihan Zhou; Yuanzheng Chen; Kenny Lee
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.544

Abstract

Tokenized real-world asset (RWA) infrastructure exposes platform operators, investors, and reporting teams to a combined settlement, disclosure, liquidity, and accounting-quality monitoring problem. A tokenized claim can continue to trade while the underlying issuer releases new financial statements, securities fail to deliver in the reference market, or protocol-level liquidity changes in RWA venues. This paper develops an accounting-aware, evidence-constrained agent workflow for risk alerting and source-grounded report generation. The revised experiment replaces the earlier rule-generated monitoring sandbox with external datasets: SEC fails-to-deliver observations, SEC EDGAR XBRL company facts, SEC submissions metadata, Financial PhraseBank sentiment labels, and DefiLlama RWA protocol TVL. The issuer-day panel contains 2,648 surveillance tasks for eight large U.S. issuers from 2024-12-01 through 2026-03-31. Observed settlement stress is defined from external SEC FTD balances rather than from the agent's own rule. Accounting risk is computed from XBRL-derived liquidity, leverage, accrual, and cash-flow indicators. A stronger market-plus-accounting logistic baseline is added alongside single-source baselines and the proposed fusion agent. The machine-learning baseline achieves the strongest F1 score for settlement-stress detection (0.909), while the proposed fusion agent achieves the highest report faithfulness and tool-use correctness (1.000 each) and high recall (0.849). The results support a governance-oriented interpretation: an evidence-constrained agent is most useful not as an opaque high-accuracy classifier, but as an auditable layer that connects settlement evidence, filing metadata, accounting fundamentals, independent sentiment calibration, and RWA protocol liquidity into a reproducible monitoring record.
Co-Authors Emma Lin Kenny Lee Yuanzheng Chen