Here’s the problem: most machine learning algorithms are optimists. They aim for high accuracy by favoring the majority class, completely missing the subtle patterns in the underrepresented group. Take fraud detection—if only 0.1% of transactions are fraudulent, a model might lazily label everythi...
This paper analyzes the banking dataset in the weka environment for the detection of interesting patterns based on its applications ofcustomer acquisition, customer retention management, and marketing and management of risk, fraudulence detections.Amritpal Singh...
The goal of the research branch, Visually Rich Document Understanding (VRDU), is to find ways to understand such materials automatically. Structured information like names, addresses, dates, and sums can be extracted from documents using VRDU ...
The results from the entry phase are read-only and used as basis for the subset calculations which are updated for each cycle run. 3.2.1. Step 4: Identify subset There are various approaches for the identification of subsets. Common approaches are outlier detection methods such as isolation ...
FinanceData AnalyticsBankingText Mining An error occurred: Unexpected end of JSON input lightbulb See what others are saying about this dataset What have you used this dataset for? How would you describe this dataset? Other text_snippet Metadata Oh no! Loading items failed. If the issue persist...
Using Transactional Data for Financial Analysis and Fraud Detection Data CardCode (34)Discussion (1)Suggestions (0) About Dataset The Credit Card Transactions Dataset provides detailed records of credit card transactions, including information about transaction times, amounts, and associated personal and ...
be used to reproduce and compare results. The absence of open datasets for ID document fraud prevention research inspired us to create a new dataset, called DLC-2021 [4,5,6]. It can be used to establish an evaluation methodology and set up baselines for document image recapture detection, ...