Fraud Detection Automation Through Data Analytics and Artificial Intelligence

Ikhsan, Wishmy Meinawa; Ednoer, Elzami Haqie; Kridantika, Winanda Setyaning; Firmansyah, Amrie

doi:10.37641/riset.v4i2.166

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…This relationship helps auditors make better decisions by allowing them to anticipate future trends and improve audit practice. Ikhsan et al, (2022) finds that increasing audit quality can be achieved with a high accuracy value using a fraud detection model based on data analytics and artificial intelligence in the Asian context.…”

Section: Discussionmentioning

confidence: 99%

The Moderating Effect of Auditor Size in the Relationship between Using Artificial Intelligence Techniques and Fraud Risk Assessment

Humeedat

2024

Preprint

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In recent years, the implementation of artificial intelligence (AI) in accounting has grown in many domains, including auditing. AI permits auditors to evaluate large data sets and rapidly identify discrepancies and sequences in audit. This means auditors can review all of a client's transactions via a low time and effort. This entails auditing firms investing heavily in technological infrastructure as well as training their human resources to use cutting-edge AI technologies. Thus, this research sought to investigate the impact of the use of AI techniques by Jordanian external auditors in fraud risk assessment, the moderating effect of auditor size. The descriptive analytical method was used by distributing 280 questionnaires electronically to auditors employed in auditing firms operating in Jordan, The result reveals that AI techniques are extremely beneficial in fraud risk assessment at all levels, and the relationship between the use of AI techniques and the assessment of fraud risk has improved as auditor size has increased.

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Section: Discussionmentioning

confidence: 99%

The Moderating Effect of Auditor Size in the Relationship between Using Artificial Intelligence Techniques and Fraud Risk Assessment

Humeedat

2024

Preprint

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“…Putra et al (2023) juga menekankan bahwa dalam literatur akuntans. Hal yang sama dengan penelitian Sulistya (2021), Ikhsan (2022) terkait definisi Big Data. Big Data selalu diartikan berdasarkan jenis analisis yang dapat dilakukan dengan data, seperti analisis data atau analisis prediksi.…”

Section: Tinjauan Pustaka Big Dataunclassified

“…Oleh karena itu, kecerdasan buatan tidak hanya menjalankan proses data seperti yang dilakukan alat analitika data, tetapi juga melakukan analisis atas hasil pemrosesan tersebut. (Ikhsan et al, 2022) Data analyst memiliki tanggung jawab mengelola sejumlah besar data dengan tujuan membersihkan, menganalisis, dan menggambarkannya secara visual. Peran data analyst ini fokus pada penggalian informasi terkait perkembangan bisnis di masa mendatang dari berbagai perspektif, dan hasilnya kemudian disampaikan kepada data engineer.…”

Section: Analis Dataunclassified

Literature Review: Big Data dan Data Analys pada Perusahaan

Eka Mayasari,

Agussalim Agussalim

2023

JUISIK

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Companies worldwide have witnessed an extraordinary data revolution. Big data and data analytics have become highly valuable assets for companies, playing a crucial role in decision-making, business strategies, and competitiveness in an increasingly competitive market. This phenomenon creates a new paradigm that can transform how companies operate and interact with their customers. The objective of this research is to understand the importance of using big data and data analytics in a company. This study includes a comprehensive literature analysis and combines empirical findings from various previous studies. The method used in this research is a literature review approach. This journal is compiled based on the analysis of research findings sourced from various journals accessed through the Google Scholar database. The research results indicate that big data and data analytics have become essential elements in modern companies, helping them make more accurate decisions, improve operational efficiency, and achieve competitive advantages. However, companies must also pay attention to data privacy and security issues to ensure that their use of Big Data complies with applicable regulations.

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“…Financial fraud detection is a crucial process that involves identifying fraudulent activities within an organization (Ikhsan et al, 2022). Its significance lies in ensuring public trust in financial reports and the organization's performance, as well as preventing significant financial losses (Pourhabibi et al, 2020).…”

Section: Literature Review Financial Fraud Detectionmentioning

confidence: 99%

Financial Fraud Detection and Machine Learning Algorithm (Unsupervised Learning): Systematic Literature Review

Husnaningtyas,

Totok Dewayanto

2023

JRABA

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This research aims to assess the usage of unsupervised learning in detecting financial fraud across various financial industries by identifying cognitive constructs, benefits, economic optimization, and challenges associated with fraud detection necessitating innovative approaches for effective detection. This study conducts Systematic Literature Review following PRISMA protocol for article selection of 27 journal articles published between 2010 and 2023, sourced from Scopus database. The analysis discloses that unsupervised learning has been implemented across diverse financial sectors, including online payments, insurance, and prominently in banking, especially for identifying anomalies in credit card transactions. K-Means is the most popular method used in unsupervised learning. Nevertheless, there are ongoing challenges that require solutions to ensure the efficacy of machine learning implementation, encompassing issues like class imbalance and the complexity of fraudulent activities. In theoretical terms, this research provides an understanding of cognitive concepts, benefits and applications, challenges, and practical recommendations in the use of unsupervised learning for financial fraud detection. This is useful for practical implementation, benefiting industry practitioners in selecting appropriate models with datasets that have the potential to enhance detection system accuracy and reduce financial losses due to fraud.

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Fraud Detection Automation Through Data Analytics and Artificial Intelligence

Cited by 8 publications

References 26 publications

The Moderating Effect of Auditor Size in the Relationship between Using Artificial Intelligence Techniques and Fraud Risk Assessment

The Moderating Effect of Auditor Size in the Relationship between Using Artificial Intelligence Techniques and Fraud Risk Assessment

Literature Review: Big Data dan Data Analys pada Perusahaan

Financial Fraud Detection and Machine Learning Algorithm (Unsupervised Learning): Systematic Literature Review

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