An Artificial Neural Network for Bank Robbery Risk Management: The OS.SI.F Web On-Line Tool of the ABI Anti-crime Department

Guazzoni, Carlo; Ronsivalle, Gaetano Bruno

doi:10.1007/978-3-540-88181-0_1

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Financial market predictions using ANNs have been applied to stock markets (Atiya, Talaat, & Shaheen 1997;Sutheebanjard & Premchaiswadi, 2010;Guresen, Kayakutlu, & Daim, 2011), currency markets (Kondratenko & Kuperin, 2003), futures markets (Kim, 2004), business failure predictions (Tang & Chi, 2005), debt and credit risk assessments (Dissananayake, Hendahewa, & Karunananda, 2007), bank theft detection (Guazzoni & Ronsivalle, 2009), bank failure analysis (Al-Shayea & El-Refae, 2012) and credit card approvals (Ilgun, Mekiš, & Mekiš, 2014).…”

Section: Artificial Intelligence (Ai) Modelsmentioning

confidence: 99%

Models Applied in Stock Market Prediction: A Literature Survey

Dassanayake¹,

Jayawardena²,

Ardekani³

et al. 2019

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.

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Section: Artificial Intelligence (Ai) Modelsmentioning

confidence: 99%

Models Applied in Stock Market Prediction: A Literature Survey

Dassanayake¹,

Jayawardena²,

Ardekani³

et al. 2019

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show abstract

“…To the best of the authors’ knowledge, there is only one quantitative model to manage the risk of robbery in bank branches: Guazzoni and Ronsivalle (2009). It uses an Artificial Neural Network (ANN), which takes into account both endogenous (concerning the bank office and its security systems) and exogenous (associated with the geographic location, population density, and the crime rate) factors.…”

Section: Introduction: Literature Review and Main Objectivesmentioning

confidence: 99%

Assessing the risk of robbery in bank branches to reduce impact on personnel

et al. 2021

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According to existing literature, bank robberies can have a considerable impact on the people involved (employees, customers, and police officers), even if the direct economic losses are negligible. Consequently, this article presents a model to assess the risk of bank robbery, with the aim of reducing the impact on the people and prioritizing the investments in security measures. It is based on the MIVES (Spanish acronym for the Integrated Value Model for Sustainability Assessment) method and it was combined with Monte Carlo simulation as a way of taking into account the uncertainty. Correlations were also modeled, for simulation purposes. Indicators for addressing issues related to security features, employees, operational procedures, and physical and social environment were defined. The model was applied to two fictitious but realistic sets of cases. The first simulation provides a quick overview of the risk level of a fictitious bank, before collecting the full set of data from hundreds or thousands of branches. The second simulation analyzes the risk variation of a specific bank branch over time. The model was also used to assess the risk index of 636 real branches belonging to a Spanish bank. All the results are presented and discussed in depth. The model allows the user to identify the weak points of a branch, so that corrective measures can be taken.

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An Artificial Neural Network for Bank Robbery Risk Management: The OS.SI.F Web On-Line Tool of the ABI Anti-crime Department

Cited by 2 publications

References 2 publications

Models Applied in Stock Market Prediction: A Literature Survey

Models Applied in Stock Market Prediction: A Literature Survey

Assessing the risk of robbery in bank branches to reduce impact on personnel

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