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I will build a fraud detection system to protect your business using machine learning
Full Stack AI Engineer LangChain RAG GPT4 NextJS FastAPI
I am a Full Stack AI Engineer with M.Sc. from Norway and 4+ yrs shipping production systems. AWS GenAI Hackathon 2025 1st Runner-Up.
I help businesses cut costs and automate workflows using GPT-4, La...
About this Gig
Is your business losing money to fraud you can't see?
I build ML-powered fraud detection systems that catch what manual rules miss - with real, measurable accuracy.
PROVEN PERFORMANCE:
- XGBoost: AUC 0.9939 | Precision 1.0 | Recall 0.96
- Ensemble system: AUC 0.9980
- SHAP explainability - every flag comes with a reason
WHAT I DELIVER:
- Data preprocessing & feature engineering
- Multi-model training (XGBoost, Random Forest, LightGBM)
- Anomaly detection & risk scoring
- SHAP explainability reports
- ROC curve, confusion matrix, precision-recall analysis
- Clean, documented Jupyter Notebook + source code
- REST API & Streamlit dashboard (Premium)
BEST FIT FOR:
- Fintech startups & banks
- E-commerce chargeback problems
- Insurance claim fraud
- AML compliance teams
TECH: Python | XGBoost | LightGBM | Scikit-learn | SHAP | FastAPI | Streamlit
DELIVERABLES: Source code, evaluation report, visualizations, Jupyter Notebook
Works with your dataset OR standard datasets (credit card, PaySim, insurance).
Message me before ordering to discuss your use case.
My Portfolio
FAQ
What kind of data do you need to build the fraud detection model?
I can work with your own dataset (CSV, Excel, or database export) or use industry-standard datasets like the credit card fraud dataset, PaySim, or insurance claims data. Just message me before ordering and we will align on the data source.
What machine learning algorithms do you use?
I use XGBoost, LightGBM, Random Forest, Isolation Forest, and deep learning models including Autoencoder and LSTM for temporal pattern detection. The choice depends on your data size and needs. I will recommend the best fit.
Will I understand why a transaction was flagged?
Yes. All models include SHAP explainability reports so you can see exactly which features drove each fraud flag. This is critical for compliance, auditing, and building trust with stakeholders.
Is my data kept confidential?
Absolutely. Your data is used solely for this project and never shared or reused. I can sign an NDA upon request before work begins.
What deliverables will I receive?
You will receive clean, well-documented Python source code, a Jupyter Notebook with step-by-step explanation, model evaluation report with visualizations (ROC curve, confusion matrix, SHAP plots), and a requirements file for easy setup. Premium package also includes a REST API and Streamlit dashboar
Can this work for fraud types other than payment fraud?
Yes. The system works for insurance claim fraud, e-commerce chargeback fraud, account takeover detection, AML transaction monitoring, and more. Message me with your specific use case before ordering.
Do I need to provide labeled data (fraud vs non-fraud)?
Not necessarily. If you have labeled data, I will build a supervised model for higher accuracy. If you only have raw transaction data with no labels, I will use unsupervised anomaly detection (Isolation Forest, Autoencoder) to identify suspicious patterns.
Can this be deployed to production after delivery?
The Basic and Standard packages deliver production-ready code you can run locally or on any server. The Premium package includes a REST API and deployment-ready setup. Cloud deployment (AWS, GCP, Azure) is available as a paid extra.
