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All Journal JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen Jurnal Manajemen Perbankan Keuangan Nitro JRABA Jurnal Audit dan Perpajakan
Husnaningtyas, Nadia
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Economics, Econometrics & Finance Education Other

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FINANCIAL FRAUD DETECTION AND MACHINE LEARNING ALGORITHM (UNSUPERVISED LEARNING): SYSTEMATIC LITERATURE REVIEW Husnaningtyas, Nadia; Totok Dewayanto
Jurnal Riset Akuntansi Dan Bisnis Airlangga Vol 8 No 2 (2023): Jurnal Riset Akuntansi dan Bisnis Airlangga
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jraba.v8i2.49927

Abstract

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.
A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration Husnaningtyas, Nadia; Hanin, Ghalizha Failazufah; Dewayanto, Totok; Malik, Muhammad Fahad
JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen Vol. 20 No. 1 (2023): JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31106/jema.v20i1.20602

Abstract

Money laundering is a complex issue with global impact, leading to the increased adoption of artificial intelligence (AI) to bolster anti-money laundering (AML) measures. AI, with machine learning and deep learning as key drivers, has become an essential enhancement for AML strategies. Recognizing this emerging trend, this study embarks on a systematic literature review, aiming to provide novel insights into the implementation, effectiveness, and challenges of these sophisticated computational techniques within AML frameworks. A critical analysis of 26 selected studies published from 2018 to 2023 highlights the essential role of machine learning and deep learning in identifying money laundering schemes. Notably, the decision tree algorithm stands out as the most commonly utilized technique. The combined use of both learning models has proven to significantly increase the effectiveness of AML systems in detecting suspicious financial patterns. However, the optimization of these advanced methods is still constrained by issues related to data complexity, quality, and access.
Kontribusi Kepatuhan Pajak Usaha Mikro, Kecil, dan Menengah (UMKM) terhadap Produk Domestik Bruto (PDB): Tinjauan Literatur Sistematis Husnaningtyas, Nadia
Jurnal Audit dan Perpajakan (JAP) Vol. 5 No. 1 (2025): Artikel Riset Mei 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jap.v5i1.6752

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi kepatuhan pajak UMKM dan kontribusinya terhadap Produk Domestik Bruto (PDB). Melalui tinjauan literatur sistematis (SLR) terhadap 33 artikel yang diterbitkan antara tahun 2011 dan 2025 , penelitian mengklasifikasikan faktor-faktor tersebut ke dalam empat kategori utama: ekonomi, perilaku, kelembagaan, dan teknologi. Faktor-faktor ekonomi seperti tingkat pendapatan dan tarif pajak , faktor perilaku seperti moral pajak dan tekanan sosial , serta faktor kelembagaan seperti kualitas tata kelola dan efektivitas penegakan, semuanya berperan penting. Selain itu, faktor teknologi, termasuk sistem pajak digital, juga memfasilitasi kepatuhan dengan menyederhanakan prosedur. Tinjauan ini menunjukkan bahwa tantangan seperti kompleksitas sistem dan infrastruktur yang terbatas masih menghambat kepatuhan optimal. Penelitian ini memberikan pemahaman yang terintegrasi bagi pembuat kebijakan untuk merumuskan strategi fiskal yang lebih tepat guna meningkatkan penerimaan pajak dan mendukung pertumbuhan ekonomi. Sebagai salah satu SLR pertama yang mengintegrasikan bukti global, studi ini juga memetakan penelitian dan mengidentifikasi kesenjangan kritis.
The DIGITALISASI PERBANKAN TERHADAP KINERJA BANK : TINJAUAN SISTEMATIS DAN ANALISIS BIBLIOMETRIK Husnaningtyas, Nadia
Jurnal Manajemen Perbankan Keuangan Nitro Vol. 1 No. 4 (2025): Special Volume for International Collaboration
Publisher : LP2M IBK Nitro

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Digital transformation has revolutionized global banking, enhancing efficiency, innovation, and customer experience. This study examines digital technology's impact on banking performance through a Systematic Literature Review (SLR) of 108 Scopus articles (2019–2025). Findings show digitally mature banks achieve 1.0–1.5% higher ROA, 10–15% lower cost-to-income ratios, and 15–25% fee-based revenue from digital services. Challenges include infrastructure gaps (40% rural areas lack internet), cyber risks (60–70% attack surge), and skill shortages (1.2 million professionals needed by 2025). Bibliometric analysis reveals three clusters: financial impacts, adoption drivers, and market-specific contexts (e.g., Islamic banking). The study offers an integrated impact model and practical transformation roadmap for banks.
Co-Authors Hanin, Ghalizha Failazufah Malik, Muhammad Fahad Totok Dewayanto