Detecting fraud in financial control processes poses significant challenges due to the complex nature of financial transactions and the evolving tactics employed by fraudsters. This paper investigates the integration of data mining techniques, specifically the combination of Benford's Law and machine learning algorithms, to create an enhanced framework for fraud detection. The paper highlights the importance of combating fraudulent activities and the potential of data mining techniques to bolster detection efforts. The literature review explores existing methodologies and their limitations, emphasizing the suitability of Benford's Law for fraud detection. However, shortcomings in practical implementation necessitate improvements for its effective utilization in financial control. Consequently, the article proposes a methodology that combines informative statistical features revealed by Benford’s law tests and subsequent clustering to overcome its limitations. The results present findings from a financial audit conducted on a road-construction company, showcasing representations of primary, advanced, and associated Benford’s law tests. Additionally, by applying clustering techniques, a distinct class of suspicious transactions is successfully identified, highlighting the efficacy of the integrated approach. This class represents only a small proportion of the entire sample, thereby significantly reducing the labor costs of specialists for manual audit of transactions. In conclusion, this paper underscores the comprehensive understanding that can be achieved through the integration of Benford's Law and other data mining techniques in fraud detection, emphasizing their potential to automate and scale fraud detection efforts in financial control processes. 

pdf/integrating-data-mining-techniques-for-fraud-detection-in-4vrgwzwl51.pdf

Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes

In the age of a large number of data that scale up on a daily basis, what becomes highly relevant is the credibility analysis and accuracy, i.e., detecting potential manipulations of the respective data. Benford's law is widely used to detect anomalies in sets of data, ranging from official population numbers, stock prices and information in scientific papers to financial reports in companies' financial statements taking the form of forensic accounting. Benford's law as a tool to analyze data in this paper focuses on the planned values expressed in the budget of the defense system in the Republic of Serbia. Research in this sphere of social life is important from the viewpoint of accurately presenting the activities and of transparency of future actions, of minimizing misuses and, henceforth, increasing trust in the operation of institutions. This paper identifies a minimal discrepancy in publicly available data in the laws on budget for the observed period, which suggests a proper distribution of assets approved for funding the defense system. 

Benford's law: Planning and analysis of the planned values in the defense budget

Bu çalışmanın ana amacı Benford Yasası’ndan ve Beneish Modelinden yararlanılarak olası finansal hilenin/hatanın ve/veya muhasebe manipülasyonunun gerçekleşip gerçekleşmediğini tespit etmek ve bu tespitin bu modeller yardımıyla doğru zamanda tespit edilip edilemeyeceğini belirlemektir. Bu amaç doğrultusunda iflası açıklanmış Borsa İstanbul’da faaliyet göstermiş bir şirketin (ABC) açıklanan son beş yıllık verisinden yararlanılmıştır. Çalışmada değerlendirilen iki model, ABC şirketinin son beş yıllık veri setinde olası manipülasyon tespit edilmesi sebebiyle, uygun zamanı belirleme kabiliyetini doğru bir şekilde ele almakta sınırlı kalmıştır. İki model arasında benzer sonuçlar elde edilmiştir.

Benford yasasi ve beneish model i̇le muhasebe mani̇pülasyonunun beli̇rlenmesi̇: ampi̇ri̇k bi̇r uygulama

this paper aims to discover the capability of software of device studying (ML) for virtual forensics evidence extraction. Specifically, it is miles focused on examining methods that ML algorithms could be leveraged to come across and analyze virtual evidence for you to maximize the performance of digital forensics investigations. The first step taken in this research is to take advantage of the know-how of the existing techniques utilized in virtual forensics proof extraction. After discussing the inherent obstacles of such strategies, the point of interest shifts to describing the diverse ML procedures and algorithms that have evolved in recent years and outlining how they might give a boost to digital forensics research. Mainly, several ML-based approaches are discussed, inclusive of anomaly detection algorithms, supervised getting-to-know fashions, unsupervised learning algorithms, and deep getting-to-know algorithms. After imparting an overview of those ML-based approaches, the primary utility areas in which they can be used to extract digital evidence are mentioned, together with photo evaluation, network visitor's evaluation, and consumer behavior analysis. An example of ways ML will be used to detect and analyze picks from an online social media platform to find out evidence of a criminal offense is discussed in element.

Application of Machine Learning to Extraction of Digital Forensics Evidence

Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. 

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis

/pdf/benford-s-law-applied-to-digital-forensic-analysis-1r03qc8j.pdf

Benford's law applied to digital forensic analysis

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hongsheng Gui

Author Tools

Chat about Author

Papers

Benford's law applied to digital forensic analysis