Forensic Analysis of Mobile Banking Apps

Osho, Oluwafemi; Mohammed, Uthman L.; Nimzing, Nanfa N.; Uduimoh, Andrew A.; Misra, Sanjay

doi:10.1007/978-3-030-24308-1_49

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…The dataset was reduced using chi-square, a classifier was trained and tested. Measure the performance of the SVM classification algorithm, and evaluation metrics use Accuracy, Precision, and Recall for evaluating the classification [36][37][38][39]. This study proposes a Chi-Square feature selection algorithm in selecting relevant features in high-dimensionality customer retention data.…”

Section: Discussionmentioning

confidence: 99%

Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

Alabi

Abdulsalam

Ogundokun

et al. 2021

Communications in Computer and Information Science

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Financial credit risk analysis management has been a foremost influence with a lot of challenges, especially for banks reducing their principal loss. In this study, the machine learning technique is a promising area used for credit scoring for analyzing risks in banks. It has become critical to extract beneficial knowledge for a great number of complex datasets. In this study, a machine learning approach using Chi-Square with SVM-RBF classifier was analyzed for Taiwan bank credit data. The model provides important information with enhanced accuracy that will help in predicting loan status. The experiment achieves 93% accuracy compared to the state-of-the-art.

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Section: Discussionmentioning

confidence: 99%

Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

Alabi

Abdulsalam

Ogundokun

et al. 2021

Communications in Computer and Information Science

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“…Deve-se também tomar cuidado sobre dados confidenciais em backups que são realizados pelos sistemas operacionais. Essa é uma prática não recomendada (Osho, 2019). O Quadro 5 resume algumas ações 3 Existem, hoje, reflexões sobre a conexão entre a servidão consentida, conceito proposto por La Boétie (1987) no século 16, e as novas tecnologias da informação e comunicação (Karnal, 2016).…”

Section: Problemas E Soluçõesunclassified

Utilização e compartilhamento de dados capturados por aplicativos de smartphones: uma revisão sistemática de literatura

Silva¹,

Rosa²,

Ferneda³

et al. 2021

RGCTI

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As pesquisas sobre segurança de dados têm avançado nos últimos anos influenciadas pela expansão de aparelhos móveis, cada vez mais presentes na vida das pessoas. No mesmo sentido, também cresce a importância dada por nossa socie-dade à privacidade e segurança dos dados gerados ou armazenados nesses dispositivos. Assim, pesquisadores se veem estimu-lados a compreender de que forma esses dados podem ser acessados por aplicativos e pelo próprio sistema operacional neles instalados. Esse é um tema de pesquisa relativamente novo, mas já com significativa quantidade de trabalhos publicados na última década. Dessa forma, é pertinente uma sistematização de conhecimento para um melhor direcionamento de pesquisas sobre o assunto. O objetivo deste trabalho é realizar uma revisão sistemática de literatura sobre a utilização de informações produzidas em smartphones.

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“…However, despite these benefits and many more as pointed out in prior studies e.g., see [9,10], its growth in most of the evolving nations is far less than what is seen in advanced nations [11]. Furthermore, little investigations were carried out in Nigeria, in connection to Internet and mobile banking [12,13]. However, Baptista [10] argued that the pace at which m-banking is progressing in Nigeria and other developing countries is disturbing compared to some Asian-Pacific nations and European states.…”

Section: Introductionmentioning

confidence: 98%

An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps

2021

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Nowadays, mobile banking apps are becoming an integral part of people lives due to its suppleness and convenience. Despite these benefits, yet its growth in evolving states is beyond expectations. However, using mobiles devices to conduct financial transactions involved a lot of risk. This paper aims to investigate customers’ reasons for non-usage of the new conduits in developing countries with distinct interest in Nigeria. The study adopts two methods of analysis, artificial intelligence-based methods (AI), and structural equations modeling (SEM). A feed-forward neural network (FFNN) sensitivity examination technique was used to choose the most dominant parameters of mobile banking data collected from 823 respondents. Four algebraic directories were used to corroborate the study AI-based model. The study AI results found risk, trust, facilitating conditions, and inadequate digital laws to be the most dominant parameters that affect mobile banking growth in Nigeria, and discovered social influence and service quality to have no influence on Nigerians’ resolve to use moveable banking apps. Moreover, the results proved the superiority of AI-based models above the classical models. Government and pecuniary institutes can use the study outcomes to ensure secured services offering, and improve growth. Finally, the study suggests some areas for future studies.

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Forensic Analysis of Mobile Banking Apps

Cited by 16 publications

References 18 publications

Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

Utilização e compartilhamento de dados capturados por aplicativos de smartphones: uma revisão sistemática de literatura

An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps

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