Credit Risk Identification and Prevention Strategies in Small and Medium Banks Using Big Data Techniques

Abstract

One of the most important problems small and medium-sized banks directly confronts affecting financial stability and profitability is credit risk.  But, outmoded analytical models and insufficient data availability cause conventional credit risk assessment methods to fall short in some areas.  Conversely, the large data approaches provide fresh possibilities to support credit risk identification and preventative actions.  This study investigates the possible uses of big data analytics to enhance risk assessment in small and medium banks.  Combining structured and unstructured data from many sources, banks could discover that it helps them build more dynamic risk models properly reflecting evolving circumstances.  Among the key methods discussed are: decision trees, neural networks, NLP, anomaly detection algorithms to find high-risk borrowers.  Combining structured and unstructured data from many sources might help banks build more dynamic risk models that properly represent evolving circumstances.. Some of the main approaches addressed are: decision trees, neural networks, NLP, anomaly detection algorithms to identify high-risk borrowers.

Hence, the effectiveness of this hybrid model is precisely tested using objective measures namely Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). This indicated that the proposed hybrid model yields outstanding performance as compared to other image enhancement techniques with PSNR=38.76, SSIM=98.6, MSE=0001.Interesting, the proposed hybrid image enhancement model can outperform other techniques. This further emphasizes the benefit of the model to retain key elements of the image while eliminating the noise in the image and enhancing the general quality of the image. This research presents a novel concept of feature extraction and parameter tuning that can be a base for establishing hybrid networks in medical image improvement. In this manner, the proposed methodology is beneficial in closing the gap between intricate recognition methods and real medical imaging implementations that serve to enhance diagnostic accuracy and speed in the medical field.