Regression Analysis for ATM Cash Flow Prediction

Rajwani, Akber; Syed, Tahir Q.; Khan, Behraj; Behlim, Sadaf I.

doi:10.1109/fit.2017.00045

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…It's important to note, however, that fraud detection was not within the scope of the study. Rajwani et al, (2017) concentrated on predicting ATM cash flow using regression techniques, showcasing the effectiveness of the Long Short-Term Memory (LSTM) model. The study, while offering advancements in transaction forecasting, primarily centred on successful withdrawals and did not explore unsupervised learning for fraud detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Fraud in Automated Teller Machine Transactions in the Nigerian Bank System Using Unsupervised Deep Learning

Ojulari,

Oke,

Arulogun

2024

AIMS Research Journal

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Automated Teller Machines (ATMs) are globally adopted for financial transactions. However, the exponential rise in ATM fraud poses a significant risk to Nigeria's digital payment and banking systems, with many banks failing to provide intelligent services to ATM customers. Moreover, existing research predominantly employed supervised learning on labelled data for ATM fraud detection, overlooking unlabelled customer-clustered transaction data. In this study, customer-clustered transaction attributes were employed with unsupervised autoencoder deep learning models to develop a fraud detection system. Three years of historical transaction data were obtained from a selected Nigerian bank's ATM transaction repository, consisting of 1.2 million transaction records from 10 bank customers. The transaction features considered in this work included CardNo, Amount, TransType, BranchCode, and TransDateTime. H2O autoencoder deep learning models, and anomaly detection algorithms were adopted to uncover underlying patterns in ATM transactions. The results indicated a remarkable overall system performance, with an average Mean Squared Error (MSE) of 0.0057, accuracy of 97.6%, precision of 94.8%, recall of 93.5%, and an F1-score of 94.0%. Individual model assessments revealed slightly lower performance for specific customer clusters. The study's findings emphasized the efficacy of unsupervised deep learning in combatting ATM fraud within the Nigerian banking system. Keywords: ATM, H2O.ai, Unsupervised Learning, Deep Learning, Autoencoder, Fraud detection CISDI Journal Reference Format Ojulari, H.O., Oke, A.O. & Arulogun, O.T. (2024): Detecting Fraud in Automated Teller Machine Transactions in the Nigerian Bank System Using Unsupervised Deep Learning. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 1-... dx.doi.org/10.22624/AIMS/CISDI/V15N2P1. Available online at www.isteams.net/cisdijournal

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Section: Literature Reviewmentioning

confidence: 99%

Detecting Fraud in Automated Teller Machine Transactions in the Nigerian Bank System Using Unsupervised Deep Learning

Ojulari,

Oke,

Arulogun

2024

AIMS Research Journal

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“…Also, classical and deep learning methods were applied to this problem, the performance comparison of which are given in Vangala and Vadlamani (2020) and Hasheminejad and Reisjafari (2017). However, an analysis of the literature shows that classical machine learning methods such as regression analysis (Rajwani et al 2017), support vector machines (Jadwal et al 2018), dynamic programming (Ozer et al 2019), ARIMA (Khanarsa and Sinapiromsaran 2017), and gradient boosting (Shcherbitsky et al 2019) are used much more often.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector

2021

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The problem of forecasting time series is very widely debated. In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving optimization problems in the banking sector. A literature analysis on applying such methods in this particular area is presented. As a direct result of the described research, a universal scenario for forecasting various non-stationary time series in automatic mode was developed. The developed scenario for solving specific banking tasks to improve business efficiency, including optimizing demand for ATMs, forecasting the load on the call center and cash center, is considered. A machine learning methodology in economics that can yield robust and reproducible results and can be reused in solving other similar tasks is described. The methodology described in the article was tested on three cases and showed the ability to generate models that are superior in accuracy to similar predictive models described in the literature by at least three percentage points. This article will be helpful to specialists dealing with the problem of forecasting economic time series and students and researchers due to a large number of links to systematic literature reviews on this topic.

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“…They showed that group average forecast performance outperforms the individual forecasts for Istanbul ATMs. Rajwani et al (2017) employed two years' transaction record data set of an ATM network to forecast the cash demand, presenting their regression results and showed the ability of the model both for increasing the customer satisfaction and cost efficiency. Jadwal et al (2017) applied ANN to forecast the reduced data set of NN5 and employed same model to the clustered ATMs using discrete time wrapping as distance measure.…”

Section: Problem Background 21 Literature Reviewmentioning

confidence: 99%

“…Machine learning techniques like deep neural networks (Garcia-Pedrero and Gomez-Gil, 2010; Arabani and Komleh, 2019) or support vector machine (Simutis et al , 2008; Ramirez and Acuna, 2011) are widely used forecasting techniques. However, relatively simple statistical techniques like integrated ARIMA (Zapranis and Alexandridis, 2009; Kamini et al , 2014; Catal et al , 2015; Rafi et al , 2020), regression (Van Anholt and Vis, 2010; Rajwani et al , 2017) and SARIMA (Wagner, 2010; Gurgul and Suder, 2013) are also used techniques in the literature.…”

Section: Problem Backgroundmentioning

confidence: 99%

An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

Cedolin

Genevois

2021

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PurposeThe research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines’ cluster where the forecasting results of the aggregated series are appropriate to use.Design/methodology/approachA comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine.FindingsThe proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series.Research limitations/implicationsThe research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited.Practical implicationsThe proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process.Originality/valueThis study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

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Regression Analysis for ATM Cash Flow Prediction

Cited by 12 publications

References 4 publications

Detecting Fraud in Automated Teller Machine Transactions in the Nigerian Bank System Using Unsupervised Deep Learning

Detecting Fraud in Automated Teller Machine Transactions in the Nigerian Bank System Using Unsupervised Deep Learning

A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector

An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

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