Credit Card Fraud Detection
Analyze Credit Card Transactions in real-time and detect
potential fraudulent cases.
Credit Card Fraud Detection
Analyze Credit Card Transactions in real-time and
detect potential fraudulent cases.
Credit card fraud is a major issue. Last year, the Nilson Group reported that credit card fraud losses climbed to as much as $28.65 billion across the globe. EMV Chip Cards were supposed to fix this problem, and while we have seen a decrease in CP (Card Present) fraud, there has been a rapid increase in CNP (Card Not Present) fraud. By 2025, total payment card volume worldwide is projected to be $56.182 trillion, with gross card fraud globally expected to be $35.31 billion.
Some of the typical challenges which are faced while solving the credit card fraud problem are:
- Large number/High volume of transactions on a daily basis
- The requirement of high processing power to handle real-time transactions
- Accuracy of the decision
- Creating a robust machine learning/AI model
How can we help organizations like you looking for machine learning solutions to counter credit card fraud?
We build custom machine learning pipelines for you to help you make more effective decisions when countering fraud. As part of these machine learning pipelines, the following stages are incorporated to make the entire process seamless and effective:
- Ingesting Data From various sources
- Data Cleaning and Data Preparation
- Custom Machine Learning
- Feedback and Logging
We use various tools to help architect the solution either on-premise or on the cloud. We work with cloud tools such as Databricks, Amazon Web Services, and others depending on your requirements.
How will incorporating machine learning enhance the current process?
Working with Machine Learning models provides a much higher accuracy over time than manually trying to identify fraud based on a set of rules. Models have the capacity to learn and provide assistance to make decisions.
Avoid Manual Work
Creating Machine Learning Model pipelines will greatly reduce the amount of manual work to be done by the team. Having an automated system in place will greatly help in reducing the amount of time and effort.
Fewer False Positives and Negatives
One of the main benefits is to avoid re-iterating between correctly identified and incorrectly identified cases. We want to minimize identifying frauds erroneously. Machine learning models can focus on reducing these and hence greatly improve the accuracy of being able to detect frauds over time.
Want to know more?
If you think this solution would be useful for your organization or you have a relevant use case or pain point you would like to tackle, get in touch with us today and we can help you and work together towards a solution!