You work for a major banking and financial services firm as a machine learning specialist. Your firm has decided to improve its fraud detection for specialized cases where fraudulent actors attempt to open accounts through your firm’s banking and trading services. These services have websites where potential customers can open accounts by completing online forms. These services make use of your firm’s highly secure customer and account data stores.
You have been assigned the task of determining when a known fraudulent actor attempts to open a new account. You have decided to build a machine learning solution to solve this problem. Since your firm has a very large customer base, several million customer accounts, you need to consider the performance and the precision of your fraud detection process.
You have decided to use the AWS Glue FindMatches ML Transform to process your online form data to find matching known fraudulent accounts in your firm’s data stores. Knowing that detecting a fraudulent actor is of primary importance, how should you configure the AWS Glue FindMatches ML Transform parameters to achieve the most performant and accurate fraud detection process?

Answer options:

A.Set the FindMatches precision-recall parameter to ‘precision’ and the accuracy-cost parameter to ‘accuracy’.
B.Set the FindMatches precision-recall parameter to ‘precision’ and the accuracy-cost parameter to ‘lower cost’.
C.Set the FindMatches precision-recall parameter to ‘recall’ and the accuracy-cost parameter to ‘accuracy’.
D.Set the FindMatches precision-recall parameter to ‘recall’ and the accuracy-cost parameter to ‘lower cost’.

Answer: C
Option A is incorrect. Setting the FindMatches precision-recall parameter to ‘precision’ minimizes false positives (when you don’t have a match of a fraudulent account but mark it as a match mistakenly). But you are more concerned about minimizing false negatives (when you have a match of a fraudulent account but fail to detect it).
Option B is incorrect. Setting the FindMatches precision-recall parameter to ‘precision’ minimizes false positives (when you don’t have a match of a fraudulent account but mark it as a match mistakenly). But you are more concerned about minimizing false negatives (when you have a match of a fraudulent account but fail to detect it).
Option C is correct. Setting the FindMatches precision-recall parameter to ‘recall’ minimizes false negatives (when you have a match of a fraudulent account but fail to detect it). This is what you want. Also, setting the FindMatches accuracy-cost parameter to ‘accuracy’ maximizes the transform accuracy of finding matching records as fraudulent.
Option D is incorrect. Setting the FindMatches precision-recall parameter to ‘recall’ minimizes false negatives (when you have a match of a fraudulent account but fail to detect it). This is what you want. But, setting the accuracy-cost parameter to ‘lower cost’ favors cost or the speed of running the transform at the expense of the transform’s accuracy. This may make your transform more performant, but your primary concern is detecting a fraudulent actor. So you should set the accuracy-cost parameter to ‘accuracy’.
Reference:
Please see the AWS Glue developer guide titled Machine Learning Transforms in AWS Glue, and the AWS Glue developer guide titled Tuning Machine Learning Transforms in AWS Glue.