Machine Learning Functionality for a Shopify Fraud Detection App

As the e-commerce space continues to grow and diversify, so do the fraudulent schemes. A US-based startup contacted the RNDpoint team with a project on Shopify fraud detection using the predictive modeling capacity of advanced ML algorithms. Here are the project’s details. 

Client’s Profile 

The Client is a US-based startup offering fraud detection tools for merchants operating Shopify stores. The company’s identity platform is aimed at helping commercial entities address abuse and retail shrinkage on Shopify to restore seller profits and reduce the likelihood of skewed data and duplicate accounts. 

Client’s Requirements/Goals 

The сlient wanted the RNDpoint team to create a technically superior ML-based solution that would address two major problems: fraudulent transactions and resellers. An advanced AI-powered fraudulent transaction identification system was needed to prevent fake transactions in real time and minimize the seller’s financial losses resulting from chargebacks. The reseller detection system was required to monitor and prevent the merchant’s policy violations and dilution of customer experience.  

Business Challenge 

Working on the ML functionality, we came across the following challenges: 

• Rising fraudulent activity. The client reported a recent increase in fraudulent activities on its platform, which led to financial losses and reduced customers’ trust in the project. 
• Increasing numbers of unauthorized resellers. Predatory reselling violated the companies’ terms and conditions and worsened customer experience, thus harming the entire business. 
• Growing data complexity and volume. Efficient processing of big data requires robust data engineering and scalable technology infrastructure, which is a resource-intensive part of the development. 
• Real-time data processing. The need to detect and prevent malicious activities on the platform required the implementation of advanced AI/ML algorithms and the usage of low-latency data processing pipelines. 

Implementation 

The project lasted from October 2021 to June 2022 and involved 10 team members. The technology stack applied for the creation of the ML functionality included: Boto3, Scikit-learn, NumPy, Pandas, and Matplotlib (pyplot) as programming technologies and libraries, and RDS, Lambda, Neptune, SageMaker, AuroraDB/PostgreSQL, Gremlin, S3, and X-Ray as AWS services.  

Solution Delivered 

As a result of working on the project, the 4IRE team has delivered technically robust solutions in the following aspects. 

Integrated Real-Time Fraud Detection System 

A real-time anomaly detection system employing advanced ML algorithms was developed to enable immediate action on the part of the client. The reseller identification system was integrated into the platform IT infrastructure for the early detection and monitoring of unauthorized resellers. 

End-to-End Data Processing Pipeline 

A comprehensive data pipeline was integrated to enable fast and efficient fraud detection, from the data ingestion point to model deployment. The pipeline was effectively integrated with the client’s operations for timely alerts and action. The system was created with flexibility and scalability in mind so that the tech team would be able to introduce adjustments depending on the changing data patterns and volumes. 

UI and Reporting Improvements 

We added a visually clear and user-friendly dashboard for alert monitoring and review of the flagged transactions to enable fraud analysts’ 360-degree data control and timely action. The implemented solution can generate regular summative reports for performance and KPI analytics. 

Project Outcomes 

Within eight months of close work on the fraud and reseller detection system, the RNDpoint team has managed to achieve the following outcomes: 

• A 98% detection rate for fraudulent transactions and a considerable reduction of financial losses from chargebacks. 
• Increasing accuracy of unauthorized reseller identification, which contributed to company policy enforcement and legitimate seller protection. 
• Improvement of customer trust and satisfaction attained by means of reducing their exposure to fraud and unauthorized resellers. 
• Reduction of manual fraud detection operations achieved via effective automation, freeing the client’s human resources for core business tasks. 
• The technical solution’s scalability that allows for handling greater data volumes and adjusting the ML algorithms to new fraudulent schemes.  

The system went live in the middle of 2023, giving the client better control over fraudulent activities and real-time anomaly detection tools for taking timely action and protecting its business infrastructure.