A novel intelligent system for securing cash levels using Markov random fields

Abstract

The maintenance of cash levels under certain security thresholds is key for the health of the banking sector. In this paper, the monitoring process of branch network cash levels is performed using a single intelligent system which should provide an alert when there are cash shortages at any point of the network. Such an integral solution would provide a unified insight that guarantees that branches with similar cash features are secured as a whole. That is to say, a triggered alarm at a specific branch would indicate that attention must also be paid to similar (in‐cash‐feature) branches. The system also incorporates a (complementary) specific treatment for individual branches. The Early Warning System for securing cash levels presented in this paper (cash level EWS) is deliberately free of local demographic specifications, thereby overcoming the current lack of worldwide definitions for local demographics. This aspect would be particularly valuable for banking institutions with branch networks all over the world. A further benefit is the cost reductions that are a result of replacing several approaches with a single global one. Instead of local demographic parameters, a solid theoretical model based on Markov random fields (MRFs) has been developed. The use of MRFs means a reduction in the amount of information required. This would mean a higher processing speed as well as a significant reduction in the amount of storage capacity required. To the best of the author's knowledge, this is the first time that MRFs have been applied to cash monitoring.

Consensus algorithm based on verifiable quantum random numbers

“…The randomness and unpredictability of random numbers largely affect the security level of cryptosystems. 19,20 In most cases, random numbers are the key factors that accompany great benefits, fairness, and security, and therefore have been an important research direction in the field of cryptography and information security. Nowadays, commonly used random numbers are generated using mathematical algorithms that can produce a large number of random numbers with good statistical properties.…”

“…The generation of random numbers is a long‐standing problem. The randomness and unpredictability of random numbers largely affect the security level of cryptosystems 19,20 . In most cases, random numbers are the key factors that accompany great benefits, fairness, and security, and therefore have been an important research direction in the field of cryptography and information security.…”

A New Decision Making Method for Selection of Optimal Data Using the Von Neumann-Morgenstern Theorem

“…In its origins, they exclusively performed functions related to image processing such as restoration or reconstructing. Later works such as García Cabello (2021) or Wang et al (2022) have acknowledged their high predictive capability due to the equivalence between MRFs and Gibbs distributions, which provides an explicit expression of the prior likelihood after appropriate choice of the energy functions. MRF solutions are widely regarded as generative models as opposed to discriminative approaches, more related to tasks which involve classification.…”