You will get Production-Grade Fraud Detection System (End-to-End)
Project details
Stop fraud before it impacts your bottom line with a research-backed, industry-grade ML pipeline.
Generic ML models often fail in production because they can’t handle "Concept Drift"—where fraudsters change their tactics. This project delivers a production-hardened fraud detection system designed for Fintech, E-commerce, and Banking.
As a published AI researcher (IEEE/Springer), I don't just provide a script; I build a self-monitoring ecosystem. Your system will not only detect suspicious transactions but will also alert you the moment your model’s performance begins to degrade due to shifting data patterns.
What sets this project apart:
Drift-Aware Design: Integration of Evidently AI to monitor Data and Concept drift.
Lightweight MLOps: Using MLflow for experiment tracking and Docker for seamless deployment.
Cost-Aware Remediation: Logic designed to balance model complexity with inference costs.
Generic ML models often fail in production because they can’t handle "Concept Drift"—where fraudsters change their tactics. This project delivers a production-hardened fraud detection system designed for Fintech, E-commerce, and Banking.
As a published AI researcher (IEEE/Springer), I don't just provide a script; I build a self-monitoring ecosystem. Your system will not only detect suspicious transactions but will also alert you the moment your model’s performance begins to degrade due to shifting data patterns.
What sets this project apart:
Drift-Aware Design: Integration of Evidently AI to monitor Data and Concept drift.
Lightweight MLOps: Using MLflow for experiment tracking and Docker for seamless deployment.
Cost-Aware Remediation: Logic designed to balance model complexity with inference costs.
Machine Learning Tools
MLflow, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, XGBoostWhat's included
| Service Tiers |
Starter
$650
|
Standard
$1,800
|
Advanced
$4,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 0 | 1 | 3 |
Number of Model Variations | 2 | 2 | 3 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$300 - $1,000
Additional Revision
+$100
Additional Model Variation
(+ 7 Days)
+$200
Additional Scenario
(+ 5 Days)
+$100
Additional Graph/Chart
(+ 1 Day)
+$30Frequently asked questions
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CM
Cynthia M.
Feb 9, 2024
Google and Microsoft Ads
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About Beenish
MLOps Engineer | XAI Specialist | MSDS & IEEE Researcher
Lahore, Pakistan - 3:38 am local time
Published Research
CPU-Optimized Financial NLP:
Authored and published research on optimizing Transformers for CPU deployment. I specialize in resolving class imbalance in financial datasets—ensuring your models detect rare but critical market signals accurately. If your business needs NLP that runs fast on standard servers without needing expensive GPUs, this is my area of expertise.
Whether you need to monitor fraud detection models for drift or need a culturally sensitive AI system, I provide peer-reviewed rigor with freelance speed.
Steps for completing your project
After purchasing the project, send requirements so Beenish can start the project.
Delivery time starts when Beenish receives requirements from you.
Beenish works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit and Feature Engineering followed by all steps
Model Selection & Training Monitoring Setup Testing & Handover