Big data, little security: Addressing security issues in your platform

Macklin, Thomas; Mathews, Joseph

doi:10.1117/12.2268002

Cited by 1 publication

(1 citation statement)

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“…Other banks plan to use machine learning tools to assess credit risk learning. Such tools will help credit risk managers improve and reduce credit and liquidity risks, as both are the most common and highest risks in financial institutions [12], [13]. Moreover, banks use data analytics to analyze their customers and the financial industry's activity patterns to forecast a potential default, whether on a large scale or a personal account level.…”

Section: Risk Managementmentioning

confidence: 99%

Improving the Performance of Loan Risk Prediction based on Machine Learning via Applying Deep Neural Networks

Qu¹,

Said²

2023

EJECE

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Deep learning algorithms can be applied to data acquired regularly from consumers' financial activity in order to create predictive tools used to foresee credit card defaults. Methods such as Convolutional Neural Networks and Recurrent Neural networks were utilized to improve the performance of loan risk prediction, hence lowering the risk to financial institutions. To compare the two models, the same data set was used. With the goal of better modeling and moving their precise measures for gauging model performance, the same initial dataset was used. Predictions from deep learning algorithms are helpful in many different areas of study, including representation and abstraction that make sense of images, sound, and text. The study laid the groundwork for filling the void left by outdated banking tools and inadequate computer technology by applying deep learning algorithms to the problem of credit card default prediction.

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Section: Risk Managementmentioning

confidence: 99%