Rasmussen College Module 5 Developing Data Mart Credit Card Fraud Project

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Programming

Rasmussen College

Description

You are working with a bank as a analytics developer. Your job is to build the Data Mart to store the fraudulent credit card transaction. File with necessary data can be downloaded at : https://gofile.io/?c=l9zkuO

Submit a Word document with Data Mart Design that includes all the attributes with Data Mart with Format and Label.

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Explanation & Answer

Attached.

Running head: DEVELOPING DATA MART

1

Developing Data Mart

Name

Institution

Developing Data Mart

2

Data Mart

Credit card fraud is increasing as the use of plastic cards increases. The fraud includes the
increased use of stealing a physical card or using a card such as a card number or pin number.
Therefore, there is a need to recognize customer spending habits and apply these validations for
incoming transactions. [4] If there is a suspicious transaction, it may undergo scrutiny checks. This
paper, therefore, gives the data mart or database implementation for the system that also includes
the concept of stored procedure in Java. The risk of shopping online has increased since customer’s
shop online, and it helps them explore items with few clicks instead of asking journeys to the mall.
The customers also have an option of comparing the amounts charged by different vendors for the
same item.

During this journey of online shopping, personal details are lost in the network that results
in heavy losses of monetary value. The world report shows that the ratio of money transactions to
the fraud volume is 0.06% and has been increasing at 19% every year. The important aspect of
credit card fraud prevention is analyzing customer spending and applying the various validation
rules that are detailed out of the proposed work. The fraud detection database, therefore, includes
Neural networks, Artificial intelligence, and Support Vector Machines and Decision trees. The
attributes are therefore provided from the credit card, and there is a need to validate these
relationships through the calculation of the time-independent suspicion score for every suspicious
transaction. [4] The feature was developed through the supervised learning and exploration of
spending patterns. The approach is used in mi...


Anonymous
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