Choosing the Right Model Type for Fraud Detection
Question
You are a machine learning specialist at a large bank.
Your machine learning team has recently been assigned the task of detecting fraud in the bank's web and mobile applications.
Your management team is excited about using machine learning for fraud detection.
But they have limited money in the yearly budget for this work. You have decided to use the Amazon Fraud Detector service to deliver your fraud detection layer in your web and mobile architecture.
When building your Fraud Detector model, which model type should you choose?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect.
The model type available for the Fraud Detector service is the ONLINE_FRAUD_INSIGHTS model.
Option B is CORRECT.
The model available in the Fraud Detector service is the ONLINE_FRAUD_INSIGHTS model.
Option C is incorrect.The model type available for the Fraud Detector service is the ONLINE_FRAUD_INSIGHTS model.
Option D is incorrect.
The model type available for the Fraud Detector service is the ONLINE_FRAUD_INSIGHTS model.
Reference:
Please see the Amazon Fraud Detector user guide titled How Amazon Fraud Detector works.
Please see the Amazon Fraud Detector welcome page titled CreateModel.
Please see the Amazon Fraud Detector FAQs.
In this scenario, the bank's machine learning team has been tasked with detecting fraud in the bank's web and mobile applications with a limited budget. To accomplish this, they have decided to use the Amazon Fraud Detector service, which provides pre-built machine learning models to detect fraud.
When building a Fraud Detector model, the type of model to choose depends on the use case and the requirements of the business. The available options are:
A. ONLINE_FRAUD_DETECTOR: This model type is used for real-time fraud detection in online transactions. It allows for low latency and high throughput, making it ideal for use cases such as e-commerce, financial services, and online gaming.
B. ONLINE_FRAUD_INSIGHTS: This model type is used for real-time fraud detection and risk scoring in online transactions. It provides a risk score for each transaction, allowing businesses to make informed decisions about whether to approve or reject a transaction.
C. FRAUD_INSIGHTS: This model type is used for batch fraud detection and risk scoring. It provides insights into fraudulent activity over a period of time, allowing businesses to identify patterns and trends in fraud activity.
D. FRAUD_DETECTOR: This model type is a general-purpose fraud detection model that can be used for both real-time and batch fraud detection. It provides a variety of pre-built fraud detection models that can be customized to suit specific business needs.
In the given scenario, the bank's machine learning team is tasked with detecting fraud in web and mobile applications. Based on this use case, the ONLINE_FRAUD_DETECTOR model type is the best choice, as it provides real-time fraud detection with low latency and high throughput, which is ideal for online transactions. Additionally, this model type is likely to be more cost-effective than the other options, as it is optimized for online transactions and does not require the same level of processing power as the batch models.
