Safeguarding FinTech innovations with machine learning: Comparative assessment of various approaches

Mirza, Nawazish; Elhoseny, Mohamed; Umar, Muhammad; Metawa, Noura

doi:10.1016/j.ribaf.2023.102009

Cited by 22 publications

(24 citation statements)

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“…Scholars have proposed the utilization of machine learning techniques [ 14 , 18 , 19 ] to predict credit risks by collecting and mining internet data. This approach has yielded superior predictive outcomes compared to conventional methods.…”

Section: Literature Reviewmentioning

confidence: 99%

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Machine learning in internet financial risk management: A systematic literature review

Tian,

Khatib

et al. 2024

PLoS ONE

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Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.

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Section: Literature Reviewmentioning

confidence: 99%

“…This approach has yielded superior predictive outcomes compared to conventional methods. Even within the same data sources, machine learning models exhibit greater accuracy [ 2 , 8 ], stability [ 8 ], predictive precision [ 19 , 20 ], and efficiency [ 20 ] in contrast to traditional credit scoring models.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning in internet financial risk management: A systematic literature review

Tian,

Khatib

et al. 2024

PLoS ONE

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“…Education, gender, age, nationality, marital status, urbanization, employment are the most major socioeconomic and demographic drivers of fraud identified by researchers (Ata and Arvaz, 2011;Bosco, 2016;Hartmann-Wendels et al, 2009;Luo et al, 2020;Saha and Gounder, 2013;Xu et al, 2018). Technology advancements have become significantly more noteworthy in influencing financial fraud occurrences in the present COVID-19 pandemic scenario (Karpoff, 2021), particularly in the last years as the global digital economy has grown quickly (Qin et al, 2023) and FinTech [2] systems have the potential to faster outperform and perhaps replace conventional bank services (Mirza et al, 2023a).…”

Section: Political Stability and Corruption Nexusmentioning

confidence: 99%

“…Technology advancements have become significantly more noteworthy in influencing financial fraud occurrences in the present COVID-19 pandemic scenario (Karpoff, 2021), particularly in the last years as the global digital economy has grown quickly (Qin et al. , 2023) and FinTech [2] systems have the potential to faster outperform and perhaps replace conventional bank services (Mirza et al. , 2023a).…”

Section: Literature Reviewmentioning

confidence: 99%

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Political stability and corruption nexus: an international perspective on European and Asian countries

Beju,

Ciupac-Ulici,

Bresfelean

2024

JRF

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PurposeThis paper aims to investigate the impact of political stability on corruption by drawing upon a sample encompassing both developed and developing European and Asian countries.Design/methodology/approachThe dataset, sourced from the Refinitiv database, spans from July 2014 to May 2022. Panel data techniques, specifically pooled estimation and dynamic panel data [generalized method of moments (GMM)] are employed. The analysis encompasses both fixed and random effects models to capture country-specific cross-sectional effects. To validate our findings, we perform a robustness test by including in the investigation four control variables, namely poverty, type of governance, economic freedom and inflation. To test heterogeneity, the dataset is further divided into two distinct subsamples based on the countries’ locations.FindingsEmpirical findings substantiate that political stability (viewed as the risk of government destabilization) has a positive and significant impact on corruption in all analyzed samples of European and Asian countries, though some differences are observed in various subsamples. When we take into account the control variables, these analysis results are robust.Research limitations/implicationsThis research provided a panel data analysis with GMM, while other empirical methodologies could also be used, like the difference-in-difference approach. However, our results should be validated by extending the time and the sample to a worldwide sample and using alternative measures of corruption and political stability. Moreover, our focus was on a linear and unidirectional relationship between the considered variables, but it would be interesting to test in our further research a non-linear and bidirectional correlation between them. Furthermore, we have introduced in the robustness test only four economic variables, but to consolidate our findings, we plan to include socioeconomic and demographic variables in future studies.Practical implicationsThese outcomes imply that authorities should be aware of the necessity of implementing anti-corruption policies designed to establish effective agencies and enforcement structures for combating systemic corruption, to improve the political environment and the quality of institutions and to apply coherent economic strategies to accelerate economic growth because higher political stability and sustainable development determine a decrease in levels of corruption.Social implicationsAt the microeconomic level, the survival of organizations may be in danger from new types of corruption and money laundering. Therefore, in order to prevent financial harm, the top businesses worldwide should respond to instances of corruption through strengthened supervisory procedures. This calls for the creation of a mechanism inside the code of conduct where correct reporting of suspected situations of corruption would have a prompt procedure to be notified of. To avoid corruption in operational procedures, national plans and policies should be developed by government officials, executives and legislators on a national level, as well as by senior management and the board of directors on an organizational level. This might lower organizations' extra corruption-related expenses, assure economic growth and improve global welfare.Originality/valueA novel feature of our research resides in its broad examination of a sizable sample of European and Asian countries regarding the nexus between corruption and political stability. The paper also investigates a less explored topic in economic literature, namely the impact of political stability on corruption. Furthermore, the study depicts policy recommendations, outlining effective and reasonable measures aimed at improving the political landscape and combating corruption.

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Machine learning-aided modeling for predicting freshwater production of a membrane desalination system: A long-short-term memory coupled with election-based optimizer

Abd Elaziz,

Zayed,

Abdelfattah

et al. 2024

Alexandria Engineering Journal

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Safeguarding FinTech innovations with machine learning: Comparative assessment of various approaches

Cited by 22 publications

References 50 publications

Machine learning in internet financial risk management: A systematic literature review

Machine learning in internet financial risk management: A systematic literature review

Political stability and corruption nexus: an international perspective on European and Asian countries

Machine learning-aided modeling for predicting freshwater production of a membrane desalination system: A long-short-term memory coupled with election-based optimizer

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