Commercial model card: binary classifier, live demo CTA, production guide

2b00ed1 verified 2 months ago

7.24 kB

license: mit
language:
  - en
tags:
  - fraud-detection
  - binary-classification
  - tabular-classification
  - explainable-ai
  - xai
  - trustworthy-ai
  - responsible-ai
  - finance
  - scikit-learn
  - lead-ai-labs
  - synthetic-data
pipeline_tag: tabular-classification
library_name: sklearn

🔍 Lead.AI Fraud Detection Model

Binary Fraud / Safe Classifier with Risk Score and Plain-Language Explanation

Built by Arun Kumar Gharami · Lead.AI Labs

The Business Problem This Solves

Your payment stack processes transactions in milliseconds. Fraudulent ones look identical to legitimate ones — until the chargeback arrives weeks later. By then you've shipped the goods, paid the processing fee, and absorbed the dispute cost.

This model scores every transaction as Fraud or Safe in real time, with a risk probability and a human-readable reason — so your team knows not just what to block, but why.

What It Returns

{
  "prediction": "Fraud",
  "risk_score": 0.91,
  "explanation": "High transaction velocity in the past hour, new account, and international flag are the primary risk drivers."
}

Output	Values	Business meaning
`prediction`	`Fraud` / `Safe`	Binary routing decision
`risk_score`	0.0–1.0	Tune your own threshold — e.g. >0.7 = manual review
`explanation`	Plain English	Audit trail for disputes, compliance, chargebacks

Need three risk tiers instead of binary? See the extended Lead.AI Fraud Shield (Low / Medium / High with confidence score).

Who Should Use This

Business Type	Use Case
Banks & credit unions	Real-time transaction monitoring
Lending platforms	Application and disbursement fraud
Fintech startups	Fraud layer before you can afford a dedicated risk team
Internal risk / ops	First-pass automated triage before analyst review
Researchers	Baseline binary fraud classifier for benchmarking

Input Features

Feature	Type	Description
`transaction_amount`	float	Transaction value
`transaction_hour`	int	Hour of day (0–23)
`account_age_days`	int	Days since account creation
`previous_chargebacks`	int	Historical chargeback count
`transaction_velocity_1h`	int	Transactions in last 1 hour
`transaction_velocity_24h`	int	Transactions in last 24 hours
`is_international`	int	1 = international transaction
`is_high_risk_merchant`	int	1 = high-risk merchant category
`customer_risk_score`	float	Aggregated customer risk (0.0–1.0)

Integration Example

import joblib
import pandas as pd

model = joblib.load("model/model.joblib")

txn = pd.DataFrame([{
    "transaction_amount": 850.0,
    "transaction_hour": 2,
    "account_age_days": 12,
    "previous_chargebacks": 2,
    "transaction_velocity_1h": 5,
    "transaction_velocity_24h": 18,
    "is_international": 1,
    "is_high_risk_merchant": 1,
    "customer_risk_score": 0.87
}])

label = model.predict(txn)           # "Fraud"
score = model.predict_proba(txn)     # [[0.09, 0.91]]

Integration time: ~2 hours for a developer familiar with Python REST APIs.

Live Demo

Don't take our word for it — try it yourself:

🖥️ Fraud Detection XAI Demo →

Adjust sliders, submit a transaction, and see the fraud/safe decision with a live explanation. Share the demo link directly with stakeholders who need to approve the AI budget.

Explainability — the Feature That Closes Disputes

Most fraud models are black boxes. This one tells you why it flagged a transaction.

That matters for three reasons:

Disputes: "The transaction was flagged because of high velocity, new account, and international origin" is a defensible audit trail
Analyst efficiency: Reviewers who see a reason take 60–80% less time per decision
Compliance: Financial regulators increasingly expect decision transparency for automated systems

Deployment Options

Option	Description	Time
Python direct	`joblib.load()` + your API wrapper	~2 hours
Gradio demo	Run `app.py` for a visual interface	~30 min
FastAPI wrapper	See `sample_api_usage.py`	~1 day
Production custom build	Retrained on your data, your infra	Contact us

Want This in Production?

✅ Retrained on your actual transaction history
✅ Threshold tuning matched to your chargeback tolerance
✅ REST API with authentication, rate limiting, and logging
✅ Integration with Stripe, Braintree, Adyen, or custom payment stack
✅ Live monitoring dashboard with fraud rate trends
✅ Monthly model refresh as fraud patterns evolve

→ Commission a custom build at lead-ai.us
→ Connect on LinkedIn

Responsible AI & Limitations

Trained on synthetic data — real-world validation required before production use
No regulatory certification (FFIEC, PCI-DSS, GDPR, etc.)
Must not be used as the sole basis for fraud enforcement without human review
False positive rate must be tuned per business — defaults are not production-ready

Related Assets

Asset	Description
Lead.AI Fraud Shield	Extended 3-tier scorer with confidence %
Fraud Dataset v2 (100K)	Training data — 21 features, 100K rows
XAI Demo Space	Live interactive demo

Citation

@misc{gharami2024frauddetection,
  author       = {Arun Kumar Gharami},
  title        = {Lead.AI Fraud Detection Model: Binary Transaction Fraud Classifier with XAI},
  year         = {2024},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/arun-gharami/lead-ai-fraud-detection-model}}
}

License

MIT — Free to use. For a production deployment with support SLA, contact lead-ai.us.

Lead.AI Labs — Trustworthy AI Systems for Practical Business Intelligence
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