Credit-card-fraud detection website

Project Title

Credit-card-fraud detection website

Project Area of Specialization Cyber SecurityProject Summary

The key objective of any credit card fraud detection system is to identify suspicious events and report them to an analyst while letting normal transactions be automatically processed.

Credit card fraud costs consumers and the financial company billions of dollars annually, and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for banks and financial institution, to minimize their losses.

Project Objectives

The purpose is to propose a financial cybercrime detection model that can detect online fraud and to describe various techniques to detect Financial Fraud Detection based on machine learning such as neural network, clustering algorithm. The KMEAN Clustering algorithm is implemented to identify fraudulent transactions based on the spending behavior of a customer. The geographical location of a customer is identified by identifying its IP address and compares the current geographical location with the previous location and identify whether the user can cover entire distance in that time period. If transaction is found to be fraudulent then security system will activate.

The objectives of credit card fraud detection are to reduce losses due to payment fraud for both merchants and issuing banks and increase revenue opportunities for merchants.

Project Implementation Method

At Indellient, we have used these fundamental steps below to help clients get started from the ground up, from ideation, prototype to development and deployment. 

Step 1: Define project goals, measurement metrics and assign resources  

The first step for any data science project will be defining the project goals: 

• What are the fraud cases that we wanted to identify?  
• What kind of analytics techniques have already been implemented to combat fraud?  
• What are the key measurement metrics that we wanted to focus on when assessing the effectiveness of our fraud detection system?  
• What and how many developers do we need for developing the fraud detection system?  

Step 2: Identify proper data sources 

Once the business objectives have been confirmed and communicated, we start to identify and collect proper data sources for the fraud detection system.  

The common data sources for detecting fraud includes: 

• client profile 
• risk profile 
• product usage 
• billing data 

Additional data could also be available from third-party data vendors. For example, for the financial services industry, we will incorporate government compliance data (sanction list, and regulation rules) when building the fraud model. 

Step 3: Design the fraud detection system architecture

There are multiple key factors that needed to be considered when designing the fraud detection system architecture.

Detection frequency determines how often we run the new data through our fraud scoring model.  

Fraud-prevention operation flow impacts how and when we flag different events as suspicious, and how to handle and confirm those suspicious cases afterwards.  

Scoring accuracy baseline helps us to assess the qualification of our fraud scoring model.  

Step 4: Develop the data engineering, transformation, and modeling pipelines 

After we have envisioned the architecture of the fraud detection solution, we will start the development of the data engineering, transformation, and modeling pipelines. I have listed key activities for each of those pipelines in the graph below.  

• For the data engineering pipeline, we need to ingest and merge the data from different sources, aggregate the data based on business metrics, and set up batch processes.  
• For the data transformation pipeline, the main goal is to improve the data quality, deal with data issues such as missing & incorrect data and convert the data so that it could be fed into machine learning models. 
• For the machine learning model pipeline, we focus on building and comparing diversified ML models based on key business metrics. A module for automated model accuracy testing and re-training is a necessity in the production environment to avoid model drifting issue. 

Benefits of the Project

The aim of this project is to predict whether a credit card transaction is fraudulent or not, based on the transaction amount, location and other transaction related data. It aims to track down credit card transaction data, which is done by detecting anomalies in the transaction data. Credit card fraud detection is typically implemented using an algorithm that detects any anomalies in the transaction data and notifies the cardholder (as a precautionary measure) and the bank about any suspicious transaction.

Technical Details of Final Deliverable

The most commonly techniques used fraud detection methods are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor algorithms (KNN). These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers.

Final Deliverable of the Project Software SystemCore Industry SecurityOther IndustriesCore Technology Big DataOther TechnologiesSustainable Development Goals No PovertyRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	5000
domane	Miscellaneous	1	5000	5000