Automated Forensic Diagnostics of Mobile Banking Reliability: A Comparative Study of Engineering Failure Modes in Indonesia and Thailand

Mobile banking’s rapid expansion in Southeast Asia has increased user dependency, making service disruptions a critical source of technostress. However, comparative evidence on the modes of engineering failure across different national ecosystems remains limited. This study aimed to investigate and contrast software reliability issues by analyzing user feedback for two market-leading applications: Livin’ by Mandiri (Indonesia) and K PLUS (Thailand). A dataset of 200,000 reviews of Google Play Store was processed using an automated diagnostic framework. This methodology employed Latent Dirichlet Allocation (LDA) to extract latent technical dimensions. In addition, weak supervision via VADER polarity scoring was used to generate pseudo-labels, training the Support Vector Machine (SVM) classifier on the TF-IDF feature to effectively separate failure-related signals from satisfaction reports. The SVM model achieved a strong weighted F1-score of 92.59% and 94.08% for the Indonesian and Thai datasets, respectively. The aspect-based sentiment distribution reveals distinct failure profiles: post-update access barriers and authentication difficulties mostly drive Indonesian user complaints, while client-side runtime instability and application crashes more frequently affect Thai users. Furthermore, the study identifies a security paradox in both ecosystems, where users prioritize seamless service availability over visible data privacy measures. These findings indicate that reliability mitigation strategies should be localized. Indonesian operators should prioritize deployment quality assurance, and Thai operators should optimize client-side resiliency. Both ecosystems require frictionless, backend-driven security measures to minimize user technostress.

Latent dirichlet allocation; Mobile banking; Reliability engineering; Support vector machine; Technostress

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