Financial Text-to-SQL

• Financial text-to-SQL is a specialized field that translates natural language finance queries into SQL, addressing complex schema structures and strict regulatory requirements.
• Recent research employs domain-specific techniques like cluster-based schema linking, intermediary representations, and reinforcement learning to boost query accuracy in financial environments.
• Benchmarks such as FINCH and BookSQL highlight challenges in compositional reasoning and schema ambiguity, spurring innovations in modular, cost-efficient model architectures.

Financial text-to-SQL is the area concerned with translating natural language questions, expressed in the context of finance, accounting, or banking, into executable SQL queries over structured financial databases. This task is characterized by the intricacy of financial schema, strict correctness requirements due to regulatory and business constraints, and the high impact of even small translation errors. Recent research has transitioned from generic cross-domain solutions to techniques and benchmarks that specifically address the unique challenges of financial data environments.

1. Data Resources and Benchmarks

While traditional text-to-SQL benchmarks such as Spider and WikiSQL span multiple domains, there has been a critical need for robust, finance-specific datasets due to the complexity and regulatory significance of financial data.

• FINCH (Singh et al., 2 Oct 2025) is a large-scale financial text-to-SQL dataset comprising 292 tables and 75,725 NL–SQL pairs, created by curating financial-relevant databases from Spider, BIRD, BULL, and BookSQL. FINCH covers banking, transaction processing, loans, insurance, and e-commerce. It includes schema structures designed to mirror realistic financial databases, with complexities such as nested queries, rollups, and compliance-oriented joins.
• BookSQL (Kumar et al., 12 Jun 2024) is an accounting-focused resource with 100,000 NL–SQL pairs and a realistic, seven-table accounting schema (master transaction, chart of accounts, customers, vendors, employees, product/service, payment method). It emphasizes double-entry logic, intricate joins, and time-based queries, exposing generalization weaknesses in models trained on cross-domain data.
• BULL (Zhang et al., 19 Jan 2024) and datasets referenced in other works such as FIN SQL (Zhang et al., 19 Jan 2024) and BANK-Financials (Li et al., 26 Feb 2024), provide multi-lingual and cross-business financial benchmarks, emphasizing wide tables, ambiguous column naming, and complexity.
• The resulting performance gap, as measured on these domain-specific datasets, highlights a substantial challenge for state-of-the-art models: e.g., RESDSQL achieves 51.5% Exact Match Accuracy on BookSQL, compared to 80.5% on Spider, and model accuracy drops sharply as query complexity increases (Kumar et al., 12 Jun 2024Singh et al., 2 Oct 2025).

2. Model Architectures and Specialized Techniques

Financial text-to-SQL requires models that overcome unique schema ambiguities, domain-specific terminology, and the need for compositional reasoning. Approaches include:

• Schema Representation and Linking: Techniques such as cluster-based schema retrieval (CRED-SQL (Duan et al., 18 Aug 2025)) create adaptive, semantically-driven clusters for column and table disambiguation, extremely relevant in finance where similarly named attributes (e.g., "amount," "balance," "transaction_date") are widespread.
• Intermediary Representations: Execution Description Language (EDL) (Duan et al., 18 Aug 2025) provides a human-readable, operator-based intermediate step; this bridges NLQs and SQL, reducing semantic drift by expressing query logic in controlled natural language before SQL generation. Two-stage generation (NLQ→EDL→SQL) improves interpretability and debugging.
• Syntactic Enhancements: Graph-based encoders augmented with syntactic dependencies (S²SQL (Hui et al., 2022)) model the structural relationships within questions, capturing dependencies critical for parsing complex financial instructions. Such syntactic injection, combined with decoupling constraints on edge embedding diversity, outperforms prior graph-based models (S²SQL + RoBERTa: 71.4% on Spider Dev vs. RAT + RoBERTa: 69.7%).
• Reinforcement Learning: CogniSQL-R1-Zero (Gajjar et al., 8 Jul 2025) uses RL with lightweight execution-based rewards (including format, correctness, and brevity) to directly align model objectives with the production of executable and correct SQL—demonstrating state-of-the-art execution accuracy (ca. 59.97% single-sample, up to 69.68% best-of-6 on BIRD).
• Retrieval-Augmented Generation (RAG): Systems like Datrics Text2SQL (Gladkykh et al., 3 Apr 2025) and DFIN-SQL (Volvovsky et al., 1 Mar 2024) combine vector-based retrieval of schema documentation and example queries with LLM-based generation, improving performance in ambiguous schemas.

3. Evaluation Methodologies and Domain-Specific Metrics

Traditional metrics—Exact Match (EM), Execution Accuracy (EX), Component Matching (CM)—are often inadequate in financial contexts due to the prevalence of minor but immaterial discrepancies (e.g., rounding errors, column order), and the presence of high-stakes, compositional queries.

• FINCH Score (Singh et al., 2 Oct 2025) is a finance-oriented metric integrating component-wise structural similarity with execution accuracy, and includes tolerance for minor numeric discrepancies:

$$\text{Score}(\hat{q}, q^*) = [S(\hat{q}, q^*)]^{\beta} \cdot (\delta + (1-\delta) \cdot e(\hat{q}, q^*))$$

where $S(\hat{q}, q^*)$ is the weighted sum of component similarities and $e(\hat{q}, q^*)$ is an execution similarity that soft-penalizes small differences.

• Tree Similarity of Editing Distance (TSED) (Song et al., 2023) defines similarity as the normalized tree edit distance between the ASTs of generated and gold queries:

$$\text{TSED} = \delta / \max(\text{nodes}(T_{\text{pred}}), \text{nodes}(T_{\text{gold}}))$$

Proven to correlate tightly with execution match, TSED does not require live database execution, providing a privacy-friendly alternative for regulated environments.

• GPT-Judge (Cheng et al., 14 Jul 2025) offers a three-pronged evaluation—Execution Evaluation (EXE), Query-SQL Evaluation (QSE), SQL-SQL Evaluation (SSE)—enabling nuanced, LLM-assisted scoring of generated queries for correctness and intent-match, even when gold annotations or production environments are not available.

4. Cost, Efficiency, and Deployment

Due to the volume and velocity of financial data, scalability and operational efficiency are critical:

• Complexity-Aware Routing: EllieSQL (Zhu et al., 28 Mar 2025) assigns queries of varying complexity to pipelines balanced for cost and accuracy, using the Token Elasticity of Performance (TEP) metric to quantify gains per additional token spent:

$$TEP_G = \frac{(EX_G - EX_B)/EX_B}{(\bar{T}_G - \bar{T}_B)/\bar{T}_B}$$

Routing yields >40% reduction in token use and a >2x boost in TEP compared to single-pipeline baselines.

• Lightweight Inference: The N-rep consistency framework (Dönder et al., 20 May 2025) dispenses with reasoning-heavy (Chain-of-Thought) or fine-tuned models in favor of generating multiple schema representations, dramatically reducing per-query costs ($0.039 per query vs.$0.46 in CoT-based approaches) while maintaining execution accuracy on par with more expensive solutions.
• Multi-Agent and Modular Systems: Pipelines like FinStat2SQL (Nguyen et al., 29 Jun 2025) and Datrics Text2SQL (Gladkykh et al., 3 Apr 2025) employ modular agents for entity extraction, SQL generation, and self-correction, supporting rapid, cost-effective adaptation to evolving financial standards (e.g., VAS, IFRS) and variable database layouts.

5. Challenges Unique to Financial Text-to-SQL

Financial databases are typified by very wide tables, dense inter-table relations, ambiguous or abbreviated column naming, and complex regulatory/business logic mapping. Persistent challenges include:

• Schema Linking and Ambiguity: Robust schema linking remains non-trivial in the presence of overloaded or semantically similar columns; solutions include semantic clustering (Duan et al., 18 Aug 2025), dynamic attribute weighting, and parallel schema linking (Zhang et al., 19 Jan 2024).
• Compositional and Temporal Reasoning: Effective translation of temporal logic (e.g., “last quarter,” “year-to-date”), nested business rules, and multi-table joins remains a bottleneck; models tend to exhibit sharp performance degradation from simple to hard queries (Kumar et al., 12 Jun 2024Singh et al., 2 Oct 2025).
• Domain Adaptation: Domain-specific augmentation (e.g., question-to-SQL and SQL-to-question syntheses (Li et al., 26 Feb 2024)) and plugin-based fine-tuning (LoRA plugin hub (Zhang et al., 19 Jan 2024)) are crucial for enabling cross-database generalization in fintech.
• Evaluation Alignment: Generic match metrics can penalize minor discrepancies disproportionally. Tolerant, finance-weighted metrics like FINCH Score (Singh et al., 2 Oct 2025) and tree-edit-based approaches (Song et al., 2023) better reflect real-world materiality.
• Explainability and Auditing: Requirement for transparent, human-interpretable reasoning (e.g., Execution Description Language (Duan et al., 18 Aug 2025)) is heightened by the need for regulatory auditing, explainable finance, and human review.

6. Practical Applications and Industry Deployments

Financial text-to-SQL underpin a variety of real-world use cases, such as:

• Zero-Code Analytics for non-technical users (e.g., investment advisors querying via natural language) (Zhang et al., 19 Jan 2024).
• Automated Regulatory Reporting and compliance checks leveraging tailored evaluation and correction agents (Nguyen et al., 29 Jun 2025).
• Chatbot and Virtual Assistants for in-bank query systems, where rapid translation of user intent to SQL is critical (Song et al., 2023).
• Large-Scale Business Platforms such as those operated by Ant Group (SQLfuse (Zhang et al., 19 Jul 2024)) and B2B e-commerce platforms (SQLord (Cheng et al., 14 Jul 2025)), where robustness in handling compositional business logic and reasoning across extensive data is validated in production.

7. Future Directions

Emergent research proposes several advancements:

• Advanced Schema Linking: Integration of domain knowledge bases, hybrid approaches combining semantic and exact-match methods, and adaptive cluster-based retrieval (Duan et al., 18 Aug 2025).
• More Interpretable Reasoning: Expanding intermediate representations (EDL), and integrating user-facing explanations (Duan et al., 18 Aug 2025).
• RL and Distillation Approaches: Scalable, execution-based RL frameworks for further aligning model outputs with financial domain requirements (Gajjar et al., 8 Jul 2025), distilling RL-trained models for deployment in resource-constrained scenarios.
• Benchmark and Metric Extension: Further refinement of domain-aligned benchmarks and development of tolerance-aware, clause-weighted metrics (Singh et al., 2 Oct 2025).
• Bridging Structured/Unstructured Data: Pipelines combining document extraction, KPI structuring, and text-to-SQL over semi-structured/structured artifacts (Choi et al., 25 May 2025).
• Cross-Lingual and Cross-Standard Adaptation: Systems to accommodate local financial standards (VAS, IFRS), diverse languages, and evolving regulatory constraints (Nguyen et al., 29 Jun 2025Zhang et al., 19 Jan 2024).

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Comprehensive progress in financial text-to-SQL now hinges on models and pipelines that rigorously address schema complexity, compositional reasoning, cost efficiency, robust evaluation, and transparent, explainable operations—supported by dedicated domain benchmarks such as FINCH (Singh et al., 2 Oct 2025), BookSQL (Kumar et al., 12 Jun 2024), and BULL (Zhang et al., 19 Jan 2024), as well as new methodologies for schema linking, pipeline modularization, and reward-driven learning. The field continues to evolve, with recent advances promising broader practical impact across financial analytics, compliance, and decision support.