| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: microsoft/Phi-3.5-mini-instruct |
| pipeline_tag: text-generation |
| tags: |
| - finance |
| - accounts-payable |
| - invoice-audit |
| - fraud-detection |
| - qlora |
| - phi3 |
| --- |
| |
| # AP Auditor — Accounts Payable Fraud Detector |
|
|
| Fine-tuned **Phi-3.5-mini-instruct** for Accounts Payable invoice auditing. |
| Detects fraud, duplicates, pricing errors, and compliance violations instantly. |
|
|
|  |
|
|
| ## Evaluation Results |
|
|
| | Metric | Score | |
| |---|---| |
| | JSON Parse Success | 8/8 (100%) | |
| | Action Accuracy | 7/8 (87.5%) | |
| | Risk Level Accuracy | 7/8 (87.5%) | |
| | Flag Detection | 5/6 (83.3%) | |
| | Overall | 87.5% | |
|
|
| ## Model Details |
|
|
| | Property | Value | |
| |---|---| |
| | Base Model | Phi-3.5-mini-instruct | |
| | Parameters | 3.8B | |
| | Method | QLoRA (4-bit NF4 + double quantization) | |
| | LoRA Rank | r=64, alpha=128 | |
| | Training Samples | 1,219 | |
| | Real Data | CORD-v2 (400 receipts) | |
| | Synthetic Data | 600 AP audit scenarios | |
| | Epochs | 3 | |
| | Final Train Loss | 0.853 | |
| | Final Val Loss | 0.137 | |
|
|
| ## Detects |
|
|
| - `duplicate_invoice` — same invoice submitted twice |
| - `unapproved_vendor` — vendor not on approved list |
| - `missing_po_reference` — no PO number attached |
| - `tax_discrepancy` — wrong GST rate applied |
| - `round_number_fraud` — suspiciously round amounts |
| - `split_invoice` — invoices split to avoid approval threshold |
| - `price_mismatch` — amount exceeds contracted rate |
| - `weekend_submission` — invoice submitted on weekend |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
| import torch, json, re |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "ratulsur/ap-auditor", |
| torch_dtype=torch.float16, |
| device_map="auto", |
| ) |
| tok = AutoTokenizer.from_pretrained("ratulsur/ap-auditor") |
| |
| SYSTEM_PROMPT = """You are a senior Accounts Payable Auditor AI. |
| Output ONLY a valid JSON audit result.""" |
| |
| def audit(invoice: dict) -> dict: |
| prompt = ( |
| f"<|system|>\n{SYSTEM_PROMPT}<|end|>\n" |
| f"<|user|>\nAudit this invoice:\n\n{json.dumps(invoice, indent=2)}<|end|>\n" |
| f"<|assistant|>\n" |
| ) |
| pipe = pipeline("text-generation", model=model, tokenizer=tok, |
| return_full_text=False) |
| out = pipe(prompt, max_new_tokens=512, do_sample=False) |
| raw = out[0]["generated_text"].strip() |
| match = re.search(r"\{.*\}", raw, re.DOTALL) |
| return json.loads(match.group()) if match else {"error": raw} |
| ``` |
|
|
| ## Live Demo |
|
|
| Try it: [huggingface.co/spaces/ratulsur/ap-auditor-demo](https://huggingface.co/spaces/ratulsur/ap-auditor-demo) |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
