Machine Learning Approaches in Financial Fraud Detection: Performance Evaluation and Statistical Insights | Chapter 5 | Digital Crossroads: Integrating Humanities, Science and Technology Edition 1

The number of financial crimes has increased in modern times. Financial fraud must be identified to ensure that transactions remain secure and to maintain public trust. Machine Learning algorithms like Logistic Regression, Decision Trees, and Random Forests help us achieve this. The straight-forward method of Logistic Regression is used to identify linear relationships. Decision Trees, on the other hand, are more suitable to handle complex fraud patterns as they can capture non-linear relationships. Random Forests use numerous decision trees making them best suited for datasets which are at risk of overfitting.

Algorithms get evaluated by performance criteria such as F1 score, accuracy and precision. Out of all the transactions labelled as fraud, precision is the proportion of correctly identified fraud transactions. The proportion of accurately classified transactions is known as accuracy. The harmonic mean of precision and recall gives us the F1 score and a normalized score of the model's performance is obtained by balancing.

Statistical hypothesis tests are applied for an accurate comparison is used to compare. When three or more algorithms are to be compared to check for a statistically significant difference between their means, we use Analysis of Variance (ANOVA). This study aims to understand the effectiveness of the ML algorithms in fraud detection by performing ANOVA tests on the selected performance metrics. Here synthetic data set is generated and applied statistical techniques for evaluation.

 

Author (s) Details

 

W. Grace Shanthi
Kakatiya Institute of Technology and Science, Warangal, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-10-5/CH5

 

Accountability Simulacra: Highlights about Fraud Cases from South and North America | Chapter 8 | Current Topics on Business, Economics and Finance Vol. 2

 Cases of allied fraud involving fiscal instruments rely on copy of accountability. This study, based on educated research, looked at cases that went ignored for years, causing billion-greenback losses to investors. The aim was to demonstrate in what way or manner they bypassed internal controls, allied governance, and all supervisory apparatus by utilizing nominal and symbolic resources at the administrative level. An extensive analysis authorized the identification of a common manner of operating, which is described attending organized by objectives. By various resources, the modus operandi involves getting people engrossment, turning fraud and culpabilities wordy and routinizing it and, creating positive sense making about trade. This paper also registers challenges for accountability bettering as sensemaking is out of check lists and models.

Author(s) Details:

Ana Paula Paulino da Costa,
FIPE – Economic Research Foundation Institute, Brazil.

Please see the link here: https://stm.bookpi.org/CTBEF-V2/article/view/9823