cs.AISep 28, 2026
SaveBehavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoning
Organizations: School of Computer Science and Technology, Tongji University, Shanghai, China
Abstract
In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However, public real-world financial datasets often lack rich semantics due to privacy constraints. Consequently, synthetic datasets incorporate generated semantics, but at the cost of behavioral realism; textual descriptions for contextual reasoning remain scarce. We address this gap through a semantic enrichment framework grounded in original transaction behavior to simulate multimodal financial data. We (1) propose a multi-agent semantic enrichment framework that generates interpretable financial semantics grounded in transaction behavior through role-specialized agents and consistency refinement, and (2) newly contribute a valuable multimodal financial fraud dataset, MS-FFSD, enriched with structured semantics and textual semantics while preserving real-data-grounded transaction behavior. Furthermore, we systematically analyze the quality and utility of semantic enrichment. Results demonstrate statistical fidelity and framework generalizability, while showing that richer semantics benefit fraud modeling and context-aware LLM reasoning. Overall, this work advances multimodal financial fraud research and bridges emerging LLM and multi-agent capabilities with operational anti-fraud practice. The framework and dataset are released at https://github.com/AI4Risk/MS-FFSD.
Figures & tables
| Data Foundation | Feature Information | Semantic Information | ||||
| Dataset | Real-Data Grounded | Feature Interpretability | Original Representation | Transaction | Entity | Textual |
| Elliptic++ ( Elmougy and Liu, 2023 ) | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ |
| FraudEcom ( Vu, 2018 ) | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
| IEEE-CIS ( Vesta Corporation, 2019 ) | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Credit Card Fraud ( Pozzolo et al., 2015 ) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Sparkov ( Shenoy, 2020 ) | ✗ | ✓ | ✓ | ✓ | ✓ | ✗ |
Table 1: Comparison of public financial transaction datasets. Real-Data Grounded denotes real transaction origin or generation from real transaction data; Feature Interpretability and Original Representation indicate retained feature meanings and untransformed values; Transaction, Entity, and Textual indicate available semantic information. MS-FFSD covers all dimensions.
Figure 1: Overview of the proposed multi-agent semantic enrichment framework. The agents sequentially construct synthetic timestamp, user attributes, merchant semantics, cross-entity consistency, and entity-level textual descriptions, while shared knowledge, constraint, and fraud-preservation mechanisms, resulting in multimodal financial transaction data with tabular and textual modalities.
| Statistic | Value |
|---|---|
| Transactions | 77,881 |
| Users | 30,346 |
| Merchants | 886 |
| Normal transactions | 31.31% |
| Fraud transactions | 6.75% |
| Unlabeled transactions | 61.94% |
Table 2: Summary statistics of MS-FFSD.
Figure 2: Data organization and modalities of MS-FFSD. Tabular modality: transaction preserves original fields and adds temporal and geographic attributes. user provides demographic and geographic attributes. merchant provides hierarchical MCC categories and identities. Textual modality: description summarizes user behavior and merchant business characteristics in natural language.
Figure 3: Semantic enrichment quality of MS-FFSD. (A) Generated timestamps follow reference rhythms. (B) Generated user demographics align with reference age-group and provincial distributions. (C) Merchant radar plots show category relative deviations from reference amount and count priors. (D) Textual descriptions show clear within-group coherence and cross-group discriminability.
| Original Features | + Structured Semantics | + Textual Semantics | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Model | AUC | AP | F1 | AUC | AP | F1 | AUC | AP | F1 |
| GCN | 85.30 | 96.29 | 70.04 | 86.47 | 96.31 | 74.29 | 89.04 | 97.14 | 76.05 |
| GAT | 86.92 | 96.58 | 73.47 | 87.83 | 97.09 | 74.07 | 88.24 | 97.19 | 73.76 |
| GraphSAGE | 89.43 | 97.43 | 74.86 | 89.82 | 97.27 | 76.42 | 90.27 | 97.59 | 75.13 |
| CARE-GNN | 86.03 | 96.07 | 71.02 | 86.48 | 96.27 | 71.61 | 88.28 | 96.81 | 72.81 |
| PC-GNN | 87.86 | 96.82 | 76.73 | 87.86 | 96.80 | 76.15 | 88.94 | 96.97 | 77.89 |
Table 3: Downstream fraud detection performance on MS-FFSD under progressive semantic enrichment. Structured augments Original with structured semantic attributes, while Textual further adds entity-level textual descriptions. Positive gains over Original are shaded, with darker colors indicating larger improvements within each metric. All results are reported in percentage (%).
| Private-1 | Private-2 | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Original | + Structured | + Textual | Original | + Structured | + Textual | |||||||||||||
| Model | AUC | AP | F1 | AUC | AP | F1 | AUC | AP | F1 | AUC | AP | F1 | AUC | AP | F1 | AUC | AP | F1 |
| GCN | 90.36 | 98.84 | 70.09 | 93.73 | 99.28 | 76.82 | 94.17 | 99.47 | 78.39 | 96.43 | 98.98 | 87.21 | 96.41 | 98.97 | 88.69 | 97.90 | 99.41 | 89.88 |
| GAT | 93.43 | 99.26 | 75.34 | 94.22 | 99.40 | 77.32 | 94.68 | 99.41 | 77.99 | 96.85 | 99.04 | 89.58 | 97.00 | 99.08 | 90.21 | 97.36 | 99.20 | 91.24 |
| GraphSAGE | 94.66 | 99.42 | 76.08 | 94.67 | 99.40 | 77.61 | 95.04 | 99.45 | 78.16 | 98.67 | 99.63 | 92.95 | 98.51 | 99.59 | 93.16 | 98.76 | 99.64 | 93.18 |
| CARE-GNN | 90.10 | 98.31 | 64.96 | 92.05 | 99.09 | 71.71 | 93.99 | 99.34 | 77.83 | 97.75 | 99.38 | 91.50 | 98.02 | 99.43 | 90.75 | 98.06 | 99.44 | 92.54 |
Table 4: Downstream fraud detection performance on private real-world financial datasets under progressive semantic enrichment. Structured augments Original with structured semantics, while Textual further adds entity textual descriptions. Positive gains over Original are shaded, with darker colors indicating larger improvements within each metric. Results are reported in percentage (%).
Figure 4: Illustrative examples of LLM-based semantic reasoning. Example A shows how rich-text semantics support context-aware interpretation of numerical variation, while Example B shows how semantic reasoning distinguishes contextually plausible variation from a residual anomaly.
Appendix figures & tables14 assets
Supplementary material from the paper’s appendix.
Appendix
| Component | External Knowledge | Granularity | Usage |
| Temporal Priors | |||
| Temporal Rhythm | IEEE-CIS Fraud Detection | Coarse Temporal Activity Pattern | Temporal rhythm reference |
| User-related Priors | |||
| Geographic Assignment | 2020 Population Census | Region | User-count distribution |
| Geographic Assignment | Statistical Yearbook 2021 | Region | Transaction-amount distribution |
| Gender | 2020 Population Census | Region Gender | Region-specific sampling |
Table 5: Primary external knowledge sources and their roles in semantic enrichment.
Figure 5: Prompt used for multi-transaction user description generation.
Figure 6: Deterministic template used for single-transaction user description generation.
Figure 7: Prompt used for merchant description generation.
Figure 8: Prompt used for duplicate merchant name resolution.
| Transaction | |
| Datetime | Synthesized physical date and time constructed using IEEE-CIS-based temporal priors. |
| Source | User identifier and foreign key to the user table. |
| Target | Merchant identifier and foreign key to the merchant table. |
| Amount | Original transaction amount preserved during semantic enrichment. |
| Location | Original transaction-location identifier. |
| Type | Original transaction-type identifier. |
Table 6: Field definitions of the released MS-FFSD dataset.
| Attribute | Categories |
|---|---|
| Gender | Male; Female. |
| Age Group | Young Adults (15–44); Middle-aged Adults (45–59); Older Adults ( 60). |
| Education | Junior College and Above; Senior Secondary School; Junior Secondary School; Primary School. |
| Occupation Industry | Agriculture, Forestry, Animal Husbandry and Fishery; Mining; Manufacturing; Production and Supply of Electricity, Heat, Gas and Water; Construction; Wholesale and Retail Trade; Transport, Storage and Postal Services; Accommodation and Food Services; Information Transmission, Software and Information Technology Services; Financial Services; Real Estate; Leasing and Business Services; Scientific Research and Technical Services; Water Conservancy, Environment and Public Facilities Management; Resident Services, Repair and Other Services; Education; Health and Social Work; Culture, Sports and Entertainment; Public Administration, Social Security and Social Organizations. |
Table 7: Categorical definitions of user-level semantic attributes in MS-FFSD.
| MCC Level 1 | MCC Level 2 |
|---|---|
| Food, Tobacco and Liquor | |
| Food, Tobacco and Liquor Retail | Convenience Store; Supermarket and Hypermarket; Fresh Produce Retail; Specialty Food Retail; Tobacco, Liquor and Tea Retail. |
| Food and Beverage Services | Fast Food and Snacks; Beverages, Bakery and Coffee; Cafeteria and Group Dining; Full-Service Restaurant; Nightlife Bar and Dining; Banquet and Large-Scale Catering. |
| Clothing and Footwear | |
| Apparel and General Shopping | Apparel, Footwear and Bags; Department Store; Shopping Mall; Outlet Shopping; Commercial Street Retail. |
| Housing | |
Table 8: Hierarchical definitions of merchant-level semantic categories in MS-FFSD.
| Description Group | Number | Proportion | Avg. Length |
| User Descriptions | |||
| All Users | 30,346 | – | 45.81 words |
| Single-transaction Users | 21,110 | 69.56% | 46.38 words |
| Multi-transaction Users | 9,236 | 30.44% | 44.51 words |
| Merchant Descriptions | |||
| All Merchants | 886 | – | 46.94 words |
Table 9: Statistics of textual descriptions in MS-FFSD.
| Field | Description |
|---|---|
| Time | Global transaction order indicating the relative sequence of transactions |
| Source | Anonymized identifier of the user |
| Target | Anonymized identifier of the merchant |
| Amount | Transaction amount |
| Location | Anonymized location associated with the transaction |
| Type | Anonymized transaction type |
Table 10: Descriptions of transaction fields in the Private-1 dataset.
| Field | Description |
|---|---|
| customer_number | Anonymized identifier of the transaction customer |
| merchant_code | Anonymized identifier of the merchant |
| receiving_customer_code | Anonymized identifier of the merchant receiving the customer’s transaction |
| card_area | Geographical region associated with the card |
| pre_trade_result | Outcome of the previous transaction attempt |
| phone_equal | Whether the transaction phone matches the registered phone |
Table 11: Descriptions of transaction fields in the Private-2 dataset.
Figure 9: Semantic templates for user.
Figure 10: Semantic templates for merchant.
Figure 11: Prompt used for LLM-based semantic reasoning across all enrichment settings.