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All Journal Instal : Jurnal Komputer International Journal of Information System and Innovative Technology
Indra Marto Silaban
Universitas Pembangunan Panca Budi
Computer Science & IT Decision Sciences, Operations Research & Management

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The Potential of Blockchain Technology in the Financial Industry Rahmadani Rahmadani; Indra Marto Silaban
International Journal of Information System and Innovative Technology Vol. 4 No. 1 (2025): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/86jwrg39

Abstract

Blockchain technology has emerged as a revolutionary solution across various industries, with its potential being particularly recognised in the financial sector. This research aims to explore blockchain technology in the financial industry, focusing on its role in improving transparency, security, and efficiency in financial transactions. A decentralised data management technique, blockchain will eliminate intermediaries, reduce transaction costs, and provide a secure and immutable ledger to increase trust among financial stakeholders. This research will also highlight key areas where blockchain is transforming the financial system, such as cryptocurrencies, smart contracts, and decentralised finance (DeFi). The research also discusses the opportunities and challenges faced by the financial industry in adopting blockchain, including regulatory issues, scalability concerns, and integration with existing infrastructure. Through a comprehensive review of current trends, case studies, and theoretical frameworks, this research aims to offer insights into the future potential of blockchain technology on the financial industry, so that the long-term benefits of blockchain adoption can result in a more efficient, transparent, and secure global financial ecosystem.  
Transaction Fraud Detection in Savings and Loan Cooperatives Using Xgboost with Shap Explanations (Shapley Additive Explanations) Indra Marto Silaban; Muhammad Syahputra Novelan; Muhammad Irfan Sarif
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.535

Abstract

Savings and loan cooperatives play a crucial role in supporting the community's economy. With the motto "From Members, By Members, and For Members," cooperatives focus on developing member funds and providing returns in the form of dividends. However, cooperative operations are not free from the risk of fraud, especially by internal parties (employees or administrators). This study aims to develop a machine learning-based transaction fraud detection model using the Extreme Gradient Boosting (XGBoost) algorithm and to increase model transparency through an Explainable Artificial Intelligence (XAI) approach with the SHAP (SHapley Additive exPlanations) method. This study uses user activity log data and financial transactions that can be described as operator/employee behavior in the savings and loan cooperative system. The model will be trained to classify whether transactions are fraudulent or non-fraudulent. The results will then be evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the XGBoost model has good performance with an accuracy value of 0.81 and an AUC of 0.912. SHAP analysis shows that features such as transaction amount, transaction frequency, transaction time, and changes in user and member data are key factors in fraud detection. This study demonstrates that the integration of XGBoost and SHAP can improve fraud detection accuracy and provide transparency in model decision-making. Therefore, the results of this study can support a more effective supervisory system for savings and loan cooperative financial institutions.
Co-Authors Muhammad Irfan Sarif Muhammad Syahputra Novelan Rahmadani Rahmadani