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Exploring the Impact of Real-Time Data Streams on Predictive Analytics for Credit Risk Mitigation in Financial Institutions
Subject area: Science,Engineering and Technology · Area of research: Predictive Analytics
Abstract
A delicate financial landscape combined with escalating digital cross-border transactions has made financial institutions vulnerable to cash flow shortages, a phenomenon epitomized by the alarming ratio of non-performing loans forecasted to reach a staggering 3-5% globally. Such a prediction poses immense threats to the recovery of the post-pandemic economy. This research investigates the impact of real-time data streams on transaction, system and user behavior, and external data on the clean predictive models used in estimating credit risk. This allows the models to perform pre-emptive risk assessments using predictive analytics, which are active rather than static and based on Basel III conditions forecasting. This research draws on the Machine Learning in Finance reports, McKinsey?s industry reports, analyses pertaining to Banking analytics in real time and real time predictive system integration models, and Basel III frameworks, as well as IMF financial stability reports. It also evaluates the integration of real time data into predictive systems predictive systems. It provides a detailed organizational readiness assessment, synthesis tables addressing organizational readiness in the context of 120 financial experts, adoption strategy providing actionable phased recommendations, and organizational readiness barriers and distractions. The study posits that, with rigorous governance, the fusion of real-time data streams into predictive analytics can enhance default prediction accuracy by 15-30%, reduce NPL exposure through timely interventions, and optimize capital allocation, while addressing hurdles in data privacy, stream processing latency, model interpretability, regulatory compliance, and infrastructure scalability in machine learning applications.
Keywords
Real-Time Data Streams, Predictive Analytics, Credit Risk Mitigation, Financial Institutions, Machine Learning
How to cite this paper
@article{1711221,
author = {Ehimare Ucheoma Austie},
title = {Exploring the Impact of Real-Time Data Streams on Predictive Analytics for Credit Risk Mitigation in Financial Institutions},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {11},
pages = {856-861},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1711221.pdf},
abstract = {A delicate financial landscape combined with escalating digital cross-border transactions has made financial institutions vulnerable to cash flow shortages, a phenomenon epitomized by the alarming ratio of non-performing loans forecasted to reach a staggering 3-5% globally. Such a prediction poses immense threats to the recovery of the post-pandemic economy. This research investigates the impact of real-time data streams on transaction, system and user behavior, and external data on the clean predictive models used in estimating credit risk. This allows the models to perform pre-emptive risk assessments using predictive analytics, which are active rather than static and based on Basel III conditions forecasting. This research draws on the Machine Learning in Finance reports, McKinsey?s industry reports, analyses pertaining to Banking analytics in real time and real time predictive system integration models, and Basel III frameworks, as well as IMF financial stability reports. It also evaluates the integration of real time data into predictive systems predictive systems. It provides a detailed organizational readiness assessment, synthesis tables addressing organizational readiness in the context of 120 financial experts, adoption strategy providing actionable phased recommendations, and organizational readiness barriers and distractions. The study posits that, with rigorous governance, the fusion of real-time data streams into predictive analytics can enhance default prediction accuracy by 15-30%, reduce NPL exposure through timely interventions, and optimize capital allocation, while addressing hurdles in data privacy, stream processing latency, model interpretability, regulatory compliance, and infrastructure scalability in machine learning applications.},
keywords = {Real-Time Data Streams, Predictive Analytics, Credit Risk Mitigation, Financial Institutions, Machine Learning},
month = {May},
}