| --- |
| pretty_name: ForensicBench Ledgers |
| language: |
| - en |
| tags: |
| - fraud-detection |
| - accounting |
| - journal-entries |
| - agents |
| - benchmark |
| - synthetic |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: je_header |
| data_files: |
| - split: energy |
| path: data/energy/je_header.parquet |
| - split: healthcare |
| path: data/healthcare/je_header.parquet |
| - split: luxurygoods |
| path: data/luxurygoods/je_header.parquet |
| - split: manufacturing |
| path: data/manufacturing/je_header.parquet |
| - split: transport |
| path: data/transport/je_header.parquet |
| - config_name: je_line |
| data_files: |
| - split: energy |
| path: data/energy/je_line.parquet |
| - split: healthcare |
| path: data/healthcare/je_line.parquet |
| - split: luxurygoods |
| path: data/luxurygoods/je_line.parquet |
| - split: manufacturing |
| path: data/manufacturing/je_line.parquet |
| - split: transport |
| path: data/transport/je_line.parquet |
| - config_name: chart_of_accounts |
| data_files: |
| - split: energy |
| path: data/energy/chart_of_accounts.parquet |
| - split: healthcare |
| path: data/healthcare/chart_of_accounts.parquet |
| - split: luxurygoods |
| path: data/luxurygoods/chart_of_accounts.parquet |
| - split: manufacturing |
| path: data/manufacturing/chart_of_accounts.parquet |
| - split: transport |
| path: data/transport/chart_of_accounts.parquet |
| - config_name: vendors |
| data_files: |
| - split: energy |
| path: data/energy/vendors.parquet |
| - split: healthcare |
| path: data/healthcare/vendors.parquet |
| - split: luxurygoods |
| path: data/luxurygoods/vendors.parquet |
| - split: manufacturing |
| path: data/manufacturing/vendors.parquet |
| - split: transport |
| path: data/transport/vendors.parquet |
| - config_name: customers |
| data_files: |
| - split: energy |
| path: data/energy/customers.parquet |
| - split: healthcare |
| path: data/healthcare/customers.parquet |
| - split: luxurygoods |
| path: data/luxurygoods/customers.parquet |
| - split: manufacturing |
| path: data/manufacturing/customers.parquet |
| - split: transport |
| path: data/transport/customers.parquet |
| - config_name: employees |
| data_files: |
| - split: energy |
| path: data/energy/employees.parquet |
| - split: healthcare |
| path: data/healthcare/employees.parquet |
| - split: luxurygoods |
| path: data/luxurygoods/employees.parquet |
| - split: manufacturing |
| path: data/manufacturing/employees.parquet |
| - split: transport |
| path: data/transport/employees.parquet |
| --- |
| |
| # ForensicBench Ledgers |
|
|
| Unlabelled synthetic general ledgers for **ForensicBench**, a benchmark that evaluates whether |
| agentic LLMs can discover and type coordinated journal-entry fraud schemes over a live ledger |
| (EMNLP 2026 Industry Track). |
|
|
| Five sector ledgers (Energy, Healthcare, Luxury Goods, Manufacturing, Transport), about 300K |
| journal entries each (1.51M in total) over three years, in EUR under the French Plan Comptable General (PCG). |
| Five multi-entry fraud scheme types are injected into each ledger. **No fraud label is distributed here**: |
| labels live in a private store and are used only by the automatic scorer. |
|
|
| ## Content |
|
|
| `data/<sector>/<table>.parquet`, with the tables below (one split per sector in the dataset viewer): |
|
|
| | Table | Description | |
| |---|---| |
| | `je_header` | one row per journal entry (document id, dates, type, business process, creator) | |
| | `je_line` | entry lines (GL account, debit, credit, auxiliary account, lettrage) | |
| | `chart_of_accounts` | PCG account tree | |
| | `vendors`, `customers`, `employees` | master data | |
|
|
| The HR payroll table used in the paper (`hr_employees`) is documented in the schema of the reference |
| harness but is access-controlled there; it is not distributed. |
|
|
| ## Fraud catalogue and agent prompts |
|
|
| The reference harness gives the agent a **fraud catalogue** describing the five scheme types. It is conceptual: it |
| describes the business process, what normal operations look like and the kinds of breakdown that can indicate |
| manipulation. It does not name expected GL accounts, posting templates or injection parameters, and it contains no labels. |
|
|
| | File | Content | |
| |---|---| |
| | `catalogue/fraud_catalogue.md` | the catalogue exactly as injected into every run | |
| | `catalogue/fraud_catalogue.json` | the same content, one record per scheme type (`scheme_type`, `title`, `normal_process`, `warning_signs`) | |
| | `prompts/` | the prompt modules of the reference harness: system prompt, orientation, planning, hypothesis worker, schema description, investigation discipline | |
|
|
| Use them as they are to reproduce the baseline, or as a starting point for your own harness. Any harness may use |
| a different catalogue and different prompts, as long as it states them in its submission. |
|
|
| ## Use with PostgreSQL |
|
|
| The reference agent queries a read-only PostgreSQL database. To rebuild one sector: |
|
|
| ```bash |
| pip install pandas pyarrow psycopg2-binary |
| python tools/load_postgres.py --sector energy --dsn "dbname=forensicbench_energy user=postgres" |
| ``` |
|
|
| ## Task and submission |
|
|
| An agent investigates each ledger and flags fraudulent journal entries with a scheme type from: |
| `fictitious_ap_disbursements`, `revenue_manipulation`, `vendor_collusion`, `shadow_payroll`, `inventory_manipulation`. |
|
|
| Each model is run on the 5 sectors with 5 replicates (25 runs). A submission is a CSV with one row per flag (`replicate` is only needed for the multi run format): |
|
|
| | Column | Meaning | |
| |---|---| |
| | `sector` | one of the five sectors | |
| | `replicate` | replicate index of the run | |
| | `document_id` | `je_header.document_id` of the flagged entry | |
| | `scheme_type` | one of the five scheme types, or `unknown` (counts for Entry-F1 only) | |
| | `rationale` | optional free text | |
|
|
| Check a file before submitting: `python tools/validate_submission.py my_flags.csv`. |
|
|
| ## Metrics |
|
|
| - **Entry-F1** (primary): F1 over flagged journal entries. |
| - **Type-F1**: an entry counts only if its scheme type is also correct (at most Entry-F1). |
| - **Coverage**: share of each fraud family recovered, averaged over families. |
| - **Consistency**: stability of Entry-F1 across the 5 replicates (0 to 100). |
|
|
| Entries are ranked by Entry-F1, then Type-F1, Recall, Precision, Coverage and Consistency (no composite score). |
| Per-sector scores are averaged over replicates, then macro-averaged over sectors. |
|
|
| ## Labels |
|
|
| The ground-truth labels are held out so that the leaderboard stays meaningful. To obtain them for research evaluation |
| (local re-scoring, reproduction), write to guywaffo@gmail.com. |
|
|
| ## Leaderboard and submissions |
|
|
| Leaderboard, submission form and scoring: https://waguy02.github.io/PHD-Research-Website/forensicbench/leaderboard/ |
|
|
| Two formats are accepted: a **single run** (one run per sector, no standard deviation) or a **multi run** |
| (same replicates in every sector, reported as mean, standard deviation and Consistency). Each submission must come |
| with the code of its harness (repository URL and commit hash, or an archive); entries are unverified until a |
| maintainer has reviewed it. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{waffo2026forensicbench, |
| title={ForensicBench: Evaluating Agentic LLMs on Journal-Entry Fraud Detection}, |
| author={Waffo Dzuyo, Guy Stephane and Guibon, Ga{\"e}l and Cerisara, Christophe and Belmar-Letelier, Luis}, |
| booktitle={EMNLP 2026, Industry Track}, |
| year={2026} |
| } |
| ``` |
|
|
