Real-Time Data Processing in Credit Risk Assessment: Enhancing Predictive Models and Decision-Making
Year: 2024
Type: article
Abstract: Real-time data processing has emerged as a transformative approach in credit risk assessment, enabling financial institutions to make timely, accurate and data-driven decisions.This paper explores the integration of real-time data streams with advanced predictive models to enhance credit risk evaluation processes.By leveraging cutting-edge technologies, such as stream processing frameworks and machine learning algorithms, the proposed approach provides instantaneous insights into borrower behavior, macroeconomic indicators and market conditions.The study highlights the architecture of real-time data pipelines and their role in updating credit scores, monitoring portfolio health and identifying early warning signals of credit deterioration.Experimental results demonstrate significant improvements in model accuracy and responsiveness when incorporating real-time data, compared to traditional batch processing.Additionally, the research discusses challenges such as data latency, computational scalability and model drift, along with strategies to address them.The findings underscore the potential of real-time data processing to transform credit risk management, allowing institutions to proactively manage risk and seize opportunities in dynamic financial environments.This research contributes to the field by proposing a scalable framework for integrating real-time data into predictive modeling, fostering resilience and competitiveness in the financial industry.
Author: Sandeep Yadav
Institution: Citizens Financial Group (United States)
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4406033807