745H1N/edinet-fraud-xgboost
AI model by 745H1N
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XGBoost classifier for accounting-fraud detection on Japanese annual securities reports (有価証券報告書), trained on the SakanaAI/EDINET-Bench fraud-detection train split (865 filings). Test ROC-AUC 0.7833 on the benchmark's 224-row holdout — above the strongest published result in the EDINET-Bench paper (Random Forest, 0.75; best LLM Claude 3.5 Sonnet, 0.73; arXiv:2506.08762). Group-OOF (train-only, 10-fold StratifiedGroupKFold by company): 0.7078. Selection never touched the test split. (summary 18 indicators x 5 years, bs 57, pl 24, cf 30 lines): the 8 Beneish (1999) M-score components (DSRI, GMI, AQI, SGI, DEPI, SGAI, LVGI, TATA), profitability / liquidity / leverage / efficiency ratios, cash-flow quality, YoY growth, 5-year volatility, size, and red-flag items (107 features total). (StratifiedGroupKFold, 10 folds, seed 42) so no company spans train/valid. features (mean…
Read the full model card ↗ · Preview checked 2026-10-31T06:51:11.823Z
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Model overview
745H1N/edinet-fraud-xgboost is a task-unspecified model repository published by 745H1N on Hugging Face. A library was not reported.
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- Created
- 2026-10-31T06:26:55.000Z
- Last modified
- 2026-10-31T06:28:22.000Z
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