A model towards global demographics: an application—a universal bank branch geolocator based on branch size

2021

Int J Intell Syst

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

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

2021

Int J Intell Syst

Self Cite

“…Our proposal can also be considered to be one of the probabilistic forecasting methodologies inasmuch as it employs joint probability distributions. It has a high degree of novelty as it relies on MRFs for predicting purposes, see [11], [12], while MRFs are tools known in AI as instruments that have traditionally been used in image processing or pattern recognition and, more recently in activity detection (see [8] for an application of MFRs for the detection of spammers).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Time-Dynamic Markov Random Fields for price outcome prediction in the presence of lobbying

2021

Appl Intell

Self Cite

This paper presents a mathematical/Artificial Intelligence (AI) model for the prediction of price outcomes in markets with the presence of lobbying, whose outputs are pricing trends that aggregate the opinions of lobbies on future prices. Our proposal succeeds in unraveling this complex real-world problem by reducing the solution to straightforward probability computations. We tested our method on real olive oil prices (Andalusia, Spain) with encouraging results in a challenging sector, where opacity in the entry of oil shipments which are stored while waiting for the price to rise, makes it very difficult to forecast the prices. Specifically, understanding by minimum price that the price level is at least reached, specific formulas for computing the likelihood of both the aggregate and the minimum market price are provided. These formulas are based on the price levels that lobbies expect which in turn, can be calculated using the probability that each lobby gives to market prices. An innovative quantitative study of the lobbies is also carried out by explicitly computing the weight of each lobby in the process thus solving a problem for which there were only qualitative references up until now. The structural model is based on Time Dynamic Markov random fields (TD-MRFs). This model requires significantly less information to produce an output and enjoys transparency during the process when compared with other approaches, such as neural networks (known as black boxes). Transparency also ensures that the internal structures can be fine tuned to fit to each context as well as possible.

Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger

2020

Heliyon

Intimate Partner Violence (IPV) has become a problem of a great magnitude as it is a leading cause of death for women according to the World Health Organization. This paper presents a novel Early Warning System (EWS) for locating and protecting potential victims of IPV in which the victims' environment plays a central role. Specifically, based on Markov random fields, our EWS measures the danger through the perception that the victims' surroundings have. This new standpoint is of particular relevance since most of the IPV victims do not have a clear perception of the approaching danger. The EWS has been designed without geographical constrains to be appropriate for use in every country of the world (universal EWS). Importantly, it may be rewritten into computational terms thereby providing a real tool for police investigations. Connections with text mining and sentiment analysis may provide additional devices based on this model.