# Compliance-to-Code: Enhancing Financial Compliance Checking via Code Generation

arXiv: https://arxiv.org/abs/2505.19804 · HTML: https://mlsanity.com/paper/2505.19804/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 12 of 12)

- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [BillSum: A Corpus for Automatic Summarization of US Legislation](https://mlsanity.com/paper/1910.00523/) (2019)
- [MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding](https://mlsanity.com/paper/2301.00876/) (2023)
- [Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law](https://mlsanity.com/paper/2502.17638/) (2025)
- [Better Call GPT, Comparing Large Language Models Against Lawyers](https://mlsanity.com/paper/2401.16212/) (2024)
- [CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking](https://mlsanity.com/paper/2403.02231/) (2024)
- [Bilingual BSARD: Extending Statutory Article Retrieval to Dutch](https://mlsanity.com/paper/2412.07462/) (2024)
- [A Report on Financial Regulations Challenge at COLING 2025](https://mlsanity.com/paper/2412.11159/) (2024)
- [Computational Law: Datasets, Benchmarks, and Ontologies](https://mlsanity.com/paper/2503.04305/) (2025)

## Built on by (most cited first, 4 of 4)

- [DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction](https://mlsanity.com/paper/2509.14507/) (2025)
- [From Statute to Control Flow: Span-Grounded Deontic Trees for Defeasible Scope Parsing](https://mlsanity.com/paper/2606.08932/) (2026)
- [Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152](https://mlsanity.com/paper/2608.16394/) (2026)
- [When Evidence Changes the Subject: Subject-Typed Claim Licensing for Learned Routing](https://mlsanity.com/paper/2609.33505/) (2026)

## Abstract (arXiv)

Nowadays, regulatory compliance has become a cornerstone of corporate governance, ensuring adherence to systematic legal frameworks. At its core, financial regulations often comprise highly intricate provisions, layered logical structures, and numerous exceptions, which inevitably result in labor-intensive or comprehension challenges. To mitigate this, recent Regulatory Technology (RegTech) and Large Language Models (LLMs) have gained significant attention in automating the conversion of regulatory text into executable compliance logic. However, their performance remains suboptimal particularly when applied to Chinese-language financial regulations, due to three key limitations: (1) incomplete domain-specific knowledge representation, (2) insufficient hierarchical reasoning capabilities, and (3) failure to maintain temporal and logical coherence. One promising solution is to develop a domain specific and code-oriented datasets for model training. Existing datasets such as LexGLUE, LegalBench, and CODE-ACCORD are often English-focused, domain-mismatched, or lack fine-grained granularity for compliance code generation. To fill these gaps, we present Compliance-to-Code, the first large-scale Chinese dataset dedicated to financial regulatory compliance. Covering 1,159 annotated clauses from 361 regulations across ten categories, each clause is modularly structured with four logical elements-subject, condition, constraint, and contextual information-along with regulation relations. We provide deterministic Python code mappings, detailed code reasoning, and code explanations to facilitate automated auditing. To demonstrate utility, we present FinCheck: a pipeline for regulation structuring, code generation, and report generation.