{"id":1121,"date":"2021-10-30T08:42:09","date_gmt":"2021-10-30T08:42:09","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/30\/prevent-fake-account-sign-ups-in-real-time-with-ai-using-amazon-fraud-detector\/"},"modified":"2021-10-30T08:42:09","modified_gmt":"2021-10-30T08:42:09","slug":"prevent-fake-account-sign-ups-in-real-time-with-ai-using-amazon-fraud-detector","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/30\/prevent-fake-account-sign-ups-in-real-time-with-ai-using-amazon-fraud-detector\/","title":{"rendered":"Prevent fake account sign-ups in real time with AI using Amazon Fraud Detector"},"content":{"rendered":"
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Implementing an effective fraud prevention system is one of the top priorities for businesses that operate online web or mobile platforms. Businesses report millions of dollars of lost revenue each year due to fraud. Platform abuse and fraud prevention largely remain reactive, and is achieved by studying the profile behavior and transaction history of a user after they sign up. This approach is often manual, time-consuming, and expensive. Early detection and prevention of fraudulent account sign-ups on online platforms using artificial intelligence (AI) is an effective defense mechanism for combating fraud and abuse.<\/p>\n

This post shows how you can use Amazon Fraud Detector<\/a> in real time along with Amazon Cognito<\/a> custom authentication workflows to prevent fake account sign-ups. Amazon Fraud Detector is a fully managed service that can identify potentially fraudulent online activities, such as creation of fake accounts or online payment fraud. Plus, you can use it without the need for any prior machine learning (ML) expertise. Unlike general-purpose ML packages, Amazon Fraud Detector is designed specifically to detect fraud.<\/p>\n

Amazon Cognito lets you add user sign-up, sign-in, and access control to your web and mobile applications quickly and easily. It\u2019s serverless, and can scale up to millions of users. I also discuss how you can use Amazon Pinpoint<\/a> to track user sign-up flow events via user journeys and categorize users into segments. This is useful for user profiles and activity analysis in order to run effective marketing or promotional campaigns while maintaining a quality user experience.<\/p>\n

Solution overview<\/h2>\n

In its general design, the solution uses an Amazon Fraud Detector supervised ML model along with a customized Amazon Cognito sign-up workflow to implement a real-time new user fraud prevention mechanism for online web and mobile applications. It also uses Amazon DynamoDB<\/a> and AWS Lambda<\/a> to customize the Amazon Cognito sign-up workflow. The following diagram illustrates the high-level architecture.<\/p>\n

\"High-level<\/p>\n

Using Amazon Fraud Detector Online Fraud Insights<\/h3>\n

Amazon Fraud Detector Online Fraud Insights<\/a> is a supervised ML model designed to detect a variety of online fraud. You can use Online Fraud Insights to detect fraudulent accounts during the sign-up process. The model generates a model score between 0 and 1,000. The higher the score, the higher the risk of the new account being fraudulent.<\/p>\n

Because it\u2019s a supervised ML model, your model accuracy may vary depending on the quality and maturity of the labeled training data. The model requires at least two features in the training dataset along with two required features: EVENT_TIMESTAMP<\/code> and EVENT_LABEL<\/code>. Using more features may help achieve higher model accuracy and lower false positive rates<\/a>. Amazon Fraud Detector provides information on the importance of the features<\/a> used in training the model, which is useful for addressing model overfitting or underfitting. The training dataset can be prepared with data from an existing fraud prevention system by following the data preparation guidance<\/a>. In this case, the Amazon Fraud Detector model is trained with a labeled dataset with the following features.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
Feature<\/span><\/td>\nDescription<\/span><\/td>\n<\/tr>\n
ip_address<\/code><\/td>\nUser\u2019s public IP address<\/td>\n<\/tr>\n
email_address<\/code><\/td>\nUser\u2019s email address<\/td>\n<\/tr>\n
user_agent<\/code><\/td>\nThe User-Agent<\/a> request header value<\/td>\n<\/tr>\n
billing_state<\/code><\/td>\nUser\u2019s postal address state<\/td>\n<\/tr>\n
billing_postal<\/code><\/td>\nUser\u2019s zip or postal code<\/td>\n<\/tr>\n
billing_address<\/code><\/td>\nUser\u2019s billing address<\/td>\n<\/tr>\n
phone_number<\/code><\/td>\nUser\u2019s phone number<\/td>\n<\/tr>\n
EVENT_TIMESTAMP<\/code><\/td>\nRequired EVENT_TIMESTAMP variable<\/td>\n<\/tr>\n
EVENT_LABEL<\/code><\/td>\nThe label (fraud or legitimate)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Amazon Fraud Detector also provides a way to define rules that tell the detector how to interpret the inference outcome. These rules can be defined using the rule language<\/a>. A set of three specific rules is defined for this solution:<\/p>\n