Modeling OTP Delivery Notification Status through a Causality Bayesian Network

Abstract

Digital money is the fundamental driving factor behind today's modern economy. Credit/debit cards, e-wallets, and other contactless payment options are widely available nowadays. This also raises the security risk associated with passwords in online transactions. One-time passwords (OTPs) are another option for mitigating this. A one-time password (OTP) serves as an additional password authentication or validation technique for each user authentication session. Failures in transmitting OTP passwords through SMS can arise owing to operator network faults or technological concerns.To minimize the risk value that arises in online transactions, it is necessary to evaluate the causality of the OTP SMS sending transaction status category by determining the main factors for successful OTP SMS sending and identifying the causes of failure when sending OTP SMS using the Bayesian Network method. According to data analysis, online transactions occur more frequently in the morning, with status summaries such as no delay, unknown status, and others. Furthermore, there is causality with at least three variables in the principal status summary, including no delay, uncertain summary, long delay, normal, likely operator issues, abnormal, and more. With a high accuracy rate of around 90% in forecasting the likelihood of recurrence.