Predictive Modelling of Retail Banking Transactions for Credit Scoring, Cross-Selling and Payment Pattern Discovery

Evaluating transactional payment behaviour offers a competitive advantage in the modern payment ecosystem, not only for confirming the presence of good credit applicants or unlocking the cross-selling potential between the respective product and service portfolios of financial institutions, but also to rule out bad credit applicants precisely in transactional payments streams. In a diagnostic test for analysing the payment behaviour, I have used a hybrid approach comprising a combination of supervised and unsupervised learning algorithms to discover behavioural patterns. Supervised learning algorithms can compute a range of credit scores and cross-sell candidates, although the applied methods only discover limited behavioural patterns across the payment streams. Moreover, the performance of the applied supervised learning algorithms varies across the different data models and their optimisation is inversely related to the pre-processed dataset. Subsequently, the research experiments conducted suggest that the Two-Class Decision Forest is an effective algorithm to determine both the cross-sell candidates and creditworthiness of their customers. In addition, a deep-learning model using neural network has been considered with a meaningful interpretation of future payment behaviour through categorised payment transactions, in particular by providing additional deep insights through graph-based visualisations. However, the research shows that unsupervised learning algorithms play a central role in evaluating the transactional payment behaviour of customers to discover associations using market basket analysis based on previous payment transactions, finding the frequent transactions categories, and developing interesting rules when each transaction category is performed on the same payment stream. Current research also reveals that the transactional payment behaviour analysis is multifaceted in the financial industry for assessing the diagnostic ability of promotion candidates and classifying bad credit applicants from among the entire customer base. The developed predictive models can also be commonly used to estimate the credit risk of any credit applicant based on his/her transactional payment behaviour profile, combined with deep insights from the categorised payment transactions analysis. The research study provides a full review of the performance characteristic results from different developed data models. Thus, the demonstrated data science approach is a possible proof of how machine learning models can be turned into cost-sensitive data models

Impact of Internal Control, Cybersecurity Risk, and Competitive Advantage on Retail Cybersecurity Budget

Retail organizations are driven to improve security posture for many reasons, including meeting financial regulation requirements, mitigating threats of data breach, and differentiating themselves within markets affected by customer perception. The problem was that little was known about how these drivers of internal control, cybersecurity risk, and competitive advantage impact retail cybersecurity budgets within the retail sector. The purpose of this quantitative nonexperimental correlational study was to describe the relationship between cybersecurity budget and drivers of internal control, cybersecurity risk, and competitive advantage among U.S.-based retail merchant organizations. Real options theory provided a foundation for explaining this decision-making process. Data were collected from a web-based survey of 66 U.S. retail merchants. Results from multiple linear regression analysis indicated a positive predictive relationship between the driver of internal control and cybersecurity budget (F = 10.369, p = .002). Analysis also resulted in a regression formula by which assessment of this predictive organizational trait may be used to forecast or benchmark expected cybersecurity budget. Retail organizations may evaluate these factors to learn how they may be contributing to inefficient cybersecurity investment strategies, and security firms and regulators may develop improved tools and education initiatives by which to address drivers of underinvestment. With this information, leaders may effect social change by optimizing security investments that lead to lower prices, improved consumer privacy, and a more stable retail economy

Cross channel fraud detection framework in financial services using recurrent neural networks

The reliability and performance of real time fraud detection techniques has been a major concern for the financial institutions as traditional fraud detection models couldn’t cope with the emerging new and innovative attacks that deceive banks. The problems are further exacerbated with evolving customer behaviour as existing fraud detection models unable to cope with class imbalance problem and longer feedback loop. This thesis looks at the holistic view of fraud detection and proposes a conceptual fraud detection framework that can detect anomalous transaction quickly and accurately, as well as dynamically evolve to maintain the efficiency with minimum input from subject matter expert. The framework is used to analyse Internet Banking (IB) transactions and contextual information to reduce the false positives and improve fraud detection rates. Based on the proposed framework, Long Short-Term Memory (LSTM) based Recurrent Neural Network model for detecting fraud in remote banking is implemented and performance is evaluated against Support Vector Machine (SVM) and Markov models. The main research element is to model events as state vectors so that sequence-based learning can be applied, followed by a weak classifier to deal with noise. Firstly, the study focuses on Feature Engineering where along raw attributes such as IP Address, Amount and other, two novel features for remote banking fraud are evaluated, i.e., the time spend on a page and the time between page transition. The second focus is on modelling which is performed on an anonymised real-life dataset, provided by a large financial institution in Europe. The results of the modelling demonstrate that given the labelled dataset all models can detect payment fraud with acceptable accuracy. Various tests proved that the LSTM model achieves a F1 score of 97.7% whereas the SVM and Markov model achieve 93.5% and 95.0% respectively. As the time elapsed, the LSTM model performance significantly improves as the sequence of events became larger. As the dataset increases that time it takes to train traditional models becomes a bottleneck. This proves the hypothesis that the events across banking channels can be modelled as time series data and then sequence-based learners such as Recurrent Neural Network (RNN) can be applied to improve or reduce the False Positive Rate (FPR) and False Negative Rate (FNR)

Catalog | 2021-2022

(2021-2022). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1220/thumbnail.jp

Catalog | 2015-2016

[Vol. 104, No. 1] (2015-2016). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1214/thumbnail.jp

Catalog | 2020-2021

(2020-2021). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1219/thumbnail.jp

Catalog | 2022-2023

(2022-2023). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1221/thumbnail.jp

Catalog | 2018-2019 (May)

Vol. 107, No. 1 (May 2018). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1217/thumbnail.jp

Catalog | 2019-2020

(2019-2020). In its early years as the State Normal School, JSU produced a variety of publications (announcements, bulletins, and catalogs) that contain course information combined with the types of information that would later be found in yearbooks. Examples include historical information about the school, lists of enrolled students and club officers, photographs of athletic teams and literary clubs, notes on alumni, faculty and campus facilities, and more.https://digitalcommons.jsu.edu/lib_ac_bul_bulletin/1218/thumbnail.jp