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Organizations engaged in online commerce must remain vigilant against fraudulent activities, including the creation of counterfeit accounts and transactions executed with stolen credit cards. To combat these threats, many companies employ fraud detection systems, some of which leverage machine learning (ML).

A prevalent issue with ML solutions is the necessity for extensive, labeled datasets to effectively train models designed to identify fraudulent behavior. Additionally, organizations require the requisite skills and infrastructure to construct, train, deploy, and expand their ML models.

In this article, I will delve into the process of detecting fraud across batches of events utilizing Amazon Fraud Detector, a fully managed service that identifies potentially fraudulent online activities, such as fake account creation or payment fraud. Unlike general ML frameworks, Amazon Fraud Detector is tailored specifically for fraud detection. This article will also cover how to analyze fraud transaction predictions using Amazon Athena and Amazon QuickSight.

Batch Fraud Prediction Use Cases

You can utilize batch prediction jobs in Amazon Fraud Detector to obtain fraud predictions for a collection of events that do not necessitate real-time scoring. This approach is useful for generating predictions related to payment fraud, account takeover incidents, and misuse of free tiers during offline proof-of-concept evaluations. Furthermore, batch predictions can assist in assessing risk across events hourly, daily, or weekly, depending on your business requirements.

Insights from Batch Fraud Using Amazon Fraud Detector

Companies, particularly in ecommerce and finance, utilize ML to combat fraud. Common forms of fraud include email account compromise, new account fraud, and instances of non-payment or non-delivery resulting from compromised card information.

Amazon Fraud Detector automates the intricate and costly processes involved in developing, training, and deploying ML models for fraud detection. The service customizes each model to fit your dataset, yielding greater accuracy than conventional, one-size-fits-all ML solutions. Additionally, you incur costs only for what you utilize, allowing you to bypass considerable upfront expenses.

To analyze fraud transactions retrospectively, you can execute batch fraud predictions using Amazon Fraud Detector, storing the results in an Amazon S3 bucket. Amazon Athena can then be employed to analyze these fraud prediction outcomes, while Amazon QuickSight can be used to create visualization dashboards for your findings.

The subsequent diagram illustrates the procedure for executing batch fraud predictions and analyzing the results with Amazon Athena.

Architecture Overview

1. Create and publish a detector: Start by creating and publishing a detector using Amazon Fraud Detector. This detector should encompass your fraud prediction model and associated rules.
2. Set up an input Amazon S3 bucket: Prepare a CSV file containing the events you wish to evaluate and upload it to the input S3 bucket. Ensure the file includes columns for EVENT_ID, ENTITY_ID, EVENT_TIMESTAMP, ENTITY_TYPE, along with other relevant variables.
3. Establish an output Amazon S3 bucket: Create a separate output Amazon S3 bucket to store the prediction results generated by Amazon Fraud Detector.
4. Conduct a batch prediction: Utilize a batch predictions job in Amazon Fraud Detector to generate predictions for your set of events.
5. Review prediction results: Examine the results stored in the output bucket’s generated CSV file.
6. Analyze fraud prediction results: After creating a Data Catalog using AWS Glue, leverage Amazon Athena to execute SQL queries to analyze your fraud prediction results. You can also develop user-friendly dashboards in Amazon QuickSight by creating new datasets sourced from Amazon Athena.

Fraud Detection with Amazon SageMaker

The AWS Solutions Implementation, Fraud Detection Using Machine Learning, empowers you to automate transaction processing on either an example dataset or your own. This solution includes an ML model that detects potentially fraudulent activities and flags them for further review. The accompanying architecture can be automatically deployed using the solution’s implementation guide and AWS CloudFormation template.

SageMaker features several built-in ML algorithms suitable for diverse problem types. The solution employs the Random Cut Forest algorithm for unsupervised learning and the XGBoost algorithm for supervised learning. You can learn about these algorithms in the SageMaker Developer Guide.

Conclusion

In this article, we explored how to analyze fraudulent transactions utilizing Amazon Fraud Detector and Amazon Athena. By leveraging Amazon Fraud Detector and SageMaker’s built-in algorithms, such as Random Cut Forest and XGBoost, you can develop your own fraud detection models on AWS. This knowledge enables faster fraud detection and addresses various types of fraud, including new account fraud, online transaction fraud, and fake reviews. For additional insights, you might find this blog on font selection for resumes informative, as it can help in professional presentations. Also, take a look at the insights from SHRM regarding middle management challenges, as they are an authority on employee relations. Furthermore, if you’re interested in medical roles, check out this excellent resource for job opportunities at Amazon.