Researchers combine gradient boosting, graph features, SHAP explanations, and bounded LLM agents to build auditable fraud detection that scales with transaction volume while remaining reviewable fo...
Researchers propose robust financial statement fraud detection framework using LLMs that addresses overfitting in prior work by testing generalization across companies and time periods, advancing f...
Researcher proposes verifiable event timeline for autonomous commerce agents with tamper-evident auditability and fraud detection, addressing gaps in AP2 and ACP protocols.
Researchers propose using misinformation detection and explanation techniques to assist financial auditors in identifying material misstatements in balance sheets, income statements, and cash flow ...
Researchers propose DRQN-CMDP, a deep reinforcement learning approach using recurrent neural networks to adaptively schedule audits of off-chain data while maintaining security miss-rate constraint...
Leni production agent achieves higher reliability through verification loops with lightweight task-specialized models, evaluated across spreadsheet and accounting benchmarks.
Academic protocol (PHP-AIO) identifies four systemic risks—tacit knowledge loss, resilience reduction, regulatory exposure, and institutional capital degradation—that standard ROI misses when autom...
PA-DSL handles realistic audit scenarios where human labels contain errors and only some items receive expert review, improving automated classifier accuracy in accounting data labeling.
Research paper argues that AI agent failures stem from weak context engineering—poor instructions, tools, and guardrails—not agent architecture alone, with implications for reliable accounting auto...
Researchers propose TRACE, a typed schema for recording AI agent reasoning traces to ensure auditability and accountability in accounting workflows, addressing a critical gap in making AI agent dec...
New method compiles standard operating procedures into machine-readable pseudo-code for LLM agents, improving compliance by up to 16 points across six models—critical for safety-critical accounting...
PREF-Gate framework applies provenance-constrained evidence fusion to relational fraud detection, addressing validity issues in neighborhood risk assessment for financial graph analysis.
Research paper presents constraint-aware hierarchical search method for fine-grained classification under regulatory rules, addressing customs tariff, export control, and equipment coding tasks whe...
GRID, a new grammar-constrained decoding engine, ensures LLM-generated SQL is syntactically valid, role-aware, and audit-compliant—addressing enterprise security and governance requirements for dat...
DocMaster leverages hierarchical document structures and LLMs to analyze complex financial reports and technical documents, preserving formatting context that traditional chunking methods discard.
Researchers propose harness-engineering framework that transforms prototype LLM applications into traceable, auditable agent architectures with deterministic behavior, source boundaries, and reprod...
MindBridge argues that traditional audit sampling—the practice of reviewing representative transaction samples to infer population health—becomes obsolete when autonomous finance systems enable 100...
Technical deep-dive on four failure modes where AI agents lose write operations without logging errors—a critical reliability gap for accounting systems handling financial transactions.
TaxCalcBench, a new open-source evaluation framework, measures whether AI agents can accurately complete tax filing tasks—addressing a critical gap in benchmarking AI for accounting workflows.
IPA survey finds 70%+ of accounting firms boosting AI budgets, but evidence of ROI remains sparse—highlighting disconnect between investment confidence and proven outcomes.