Credit Card Fraud Detection: Machine learning is the best solution.
As the digital world continues to develop and criminals find ways to take advantage of it, digital security must also constantly evolve. One of the most serious scams today is credit card theft. In 2018, the loss caused by the credit card was $27.85 billion, and estimated that by 2027 this loss will double.
Two cases may explain this growth.
- First, electronic transactions are increasingly adopted in many sectors, whether for the sale, purchase of goods, or services.
- Second, fraudsters are tricky, so fraudulent methods outstrip traditional fraud detection techniques.
Fortunately, technology is constantly evolving. AI is proving to be an effective tool to analyze and process vast amounts of credit card data able to classify the transaction into normal and abnormal types in real-time. In this way, an AI-driven system can be a relevant solution for companies to prevent and detect fraudulent transactions.
By the end of this article, you'll know how Machine Learning helps with fraud detection and how your company can efficiently implement an AI-driven system with SmartPredict Credit Card Fraud Detection out-of-the-box AI use case.
What is Credit Card Fraud, and how does it occur?
The FBI defines credit card fraud as the unauthorized use of a credit or debit card or similar payment tool to obtain money or goods fraudulently.
According to this description, there are two schemes for this type of crime.
The most common type of credit card fraud is identity theft, in which fraudsters acquire a persons' credit card information or the physical card itself. The criminal then takes advantage of this to perform activities without the cardholders' authorization, such as purchases, withdrawals, new accounts, or new credit card creation.
The second type of credit card fraud is transaction laundering, also known as credit card laundering. It's a type of money laundering in which legitimate merchants are involved. Criminals use the credentials of these people to conduct illegal transactions. Another scenario is when they create normal-looking sites to sell illicit substances.
Thus all actors involved in the card payment process are victims of credit card fraud: cardholders, online merchants, credit card payment systems, and financial services.
For businesses, scams are particularly frightening because they lose money and customers' trust.
It is therefore imperative to detect and prevent fraud. There are two approaches for this:
- the traditional rule-based system,
- and the system powered by machine learning algorithms.
The traditional rule-based vs ML-based Fraud Detection systems
Conventional fraud detection relies on analysis in which experts find fraud patterns and write a set of “rules” or conditions to identify potentially fraudulent transactions. However, potential fraudsters are clever and always find ways to circumvent the principle and run down new techniques to achieve their scams. It is, therefore, an endless game between fraudsters and the security system. Thus, it takes effort, days, or weeks to find new signals and write rules.
The Machine Learning-based system also relies on finding patterns in the data, but the algorithm automatically defines and updates the rules instead of humans. It can process vast amounts of data and discover anomalies that humans overlook.
The system can also detect and spot new fraud schemes as the algorithm changes its rules based on past and emerging threats. Thus, the ML-feed system is accurate and effective in anomaly detection.
Companies’ results on using an AI-based Fraud detection system.
Artificial Intelligence is proving to be a better solution for a scam protection system, and it has paid off for companies. Here are some metrics showing how well the AI system has worked for them.
Quickly implement your AI-driven Fraud Detection with SmartPredict out-of-the-box AI use case.
What are SmartPredict out-of-the-box AI use cases?
SmartPredict ready-to-use AI use cases are real-world AI projects that SmartPredict's Data Scientists team has already fulfilled and can be into production in any software and platform by everyone. It is easy to implement, so an AI expert's intervention is not required. It, therefore, saves time and money and constitutes an effective solution for implementing an AI-based system.
All AI out-of-the-box AI use cases on SmartPredict AI platform:
Easily implement your ML-driven Fraud Detection.
Credit Card Fraud Detection is one of SmartPredict's out-of-the-box AI use cases, that able to classify abnormal and normal transactions to prevent any type of scams by credit card in real-time, based on supervised and unsupervised learning with high-performance ML models.
A 100% customizable ML workflow and flowchart deployment is already generated that performs all AI processes, including data processing, ML training, and deployment. Customization of this AI use case for credit card fraud detection with Python is then quite possible.
The generated (100% customizable) ML workflow of Credit Card Fraud Detection use case:
The generated (100% customizable) deployment flowchart:
Users only have to upload their data or use the existing data in the platform, make simple configurations and launch the system in a few clicks.
Uploading or using the existing dataset:
Simple configuration in SmartPredict Credit Card Fraud Detection out-of-the-box AI use case:
SmartPredict performs all AI processes:
An operational Web API is generated at the end of the process and can be used in any payment system to stop fraudulent transactions. It can drastically reduce the percentage of fraudulent transactions in all entities whether it is for e-commerce, banks, and others.
The generated operational web API:
Test results in the Predict space: