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All Journal TEPIAN Buletin Poltanesa
Agustiana, Puspa Riri
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education

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Data Quality Risk Management in the Data Quality Issue Management System at Private Banking Using the OCTAVE Allegro Approach Agustiana, Puspa Riri; Wilson; Suroso , Jarot S.
Poltanesa Vol 26 No 1 (2025): June 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i1.3312

Abstract

The success of a private bank is significantly dependent on managing the data quality efficiently so that the operations can run effectively, ensure compliance with regulations, and make its customers happy. Having poor data quality can also result in some pretty major monetary losses, operational inefficiencies, or damage to your reputation. This paper explores the application of the OCTAVE Allegro approach within a Supply Chain Data Quality Issue Management System, to deal with these challenges. The use of an information security risk assessment tool such as OCTAVE Allegro enforces a structured method to gather, analyze, and prioritize data quality risks. It details the benefits of its approach — greater risk comprehension, more effective mitigation strategies, and adherence to industry norms. Using this framework, banks can improve decision-making, enforce data governance policies as well as prevent more serious and costlier data-related errors. Implementation challenges such as how to make OCTAVE Allegro applicable to external requirements, and organizational resistance are explored, and this leads to an evaluation of the proposed strategies. In the end, this paper shows that the implementation of OCTAVE Allegro effectively helps private banks construct a safe and trustworthy data ecosystem. The approach enhances how the process supports improved data quality risk management and ultimately success in a growingly data-centered sector.
Framework for Generating Synthetic Customer Data to Enhance Model Training in Banking Agustiana, Puspa Riri; Santoso, Handri
TEPIAN Vol. 6 No. 4 (2025): December 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i4.3385

Abstract

Data-driven decision-making has taken on a more central role in the banking sector. However, privacy regulations and data security concerns limit the accessibility of real customer data for model training. To address this challenge, synthetic data generation offers a promising solution. This paper presents a framework tool for generating synthetic customer data that closely mimics the statistical properties of real-world data using advanced machine learning techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to enhance model training in banking applications. By leveraging advanced machine learning techniques, our framework can replicate the real Production Data to Synthetic Data customer. This synthetic data can be used to augment existing datasets, enhance model training, and improve the accuracy and robustness of predictive models. We demonstrate the effectiveness of our framework through a case study in a banking context, showcasing its potential to address challenges related to data privacy, data scarcity, and model performance.
Co-Authors Santoso, Handri Suroso , Jarot S. Wilson