The Impacts of Machine Learning in Financial Crisis Prediction

Siddhartha Vadlamudi

doi:10.18034/abr.v10i3.528

Authors

Siddhartha Vadlamudi Xandr

DOI:

https://doi.org/10.18034/abr.v10i3.528

Keywords:

Machine learning, financial crisis prediction, bankruptcy, creditworthiness

Abstract

The most complicated and expected issue to be handled in corporate firms, small-scale businesses, and investors’ even governments are financial crisis prediction. To this effect, it was of interest to us to investigate the current impact of the newly employed technique that is machine learning (ML) to handle this menace in all spheres of business both private and public. The study uses systematic literature assessment to study the impact of ML in financial crisis prediction. From the selected works of literature, we have been able to establish the important role play by this method in the prediction of bankruptcy and creditworthiness that was not handled appropriately by others method. Also, machine learning helps in data handling, data privacy, and confidentiality. This study presents a leading approach to achieving financial growth and plasticity in corporate organizations. We, therefore, recommend a real-time study to investigate the impact of ML in FCP.

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Author Biography

Siddhartha Vadlamudi, Xandr

Software Engineer II, Xandr, AT&T Services Inc., New York, USA

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