A META-ANALYSIS OF ERP AND CRM INTEGRATION TOOLS IN BUSINESS PROCESS OPTIMIZATION

This systematic review investigates the psychological and socioeconomic risk indicators that influence loan default behavior, aiming to bridge the gap between traditional credit assessment models and emerging behavioral insights. As financial institutions increasingly face challenges in accurately predicting borrower defaults, it becomes crucial to explore non-traditional variables such as cognitive biases, personality traits, financial literacy, income volatility, and employment stability. Drawing on a comprehensive synthesis of 67 peer-reviewed studies published between 2010 and 2024, this review analyzes a wide range of empirical evidence from diverse geographical, cultural, and lending contexts. The findings indicate that behavioral factors particularly impulsivity, time-inconsistency, and overconfidence play a critical role in undermining repayment discipline, especially when compounded by limited financial literacy and socioeconomic instability. Moreover, the review highlights the growing use of behavioral interventions, such as personalized nudges, commitment devices, and financial education tools, which have shown measurable effectiveness in reducing default rates. The integration of behavioral analytics into credit risk assessment, as seen in emerging hybrid models, represents a shift toward more holistic, personalized, and accurate prediction frameworks. Additionally, the review underscores the importance of tailoring financial products and risk models to cultural and contextual realities, particularly in underserved markets. By synthesizing interdisciplinary research across economics, psychology, and finance, this study provides a comprehensive framework for understanding the multidimensional drivers of loan default and offers strategic insights for lenders, policymakers, and fintech innovators aiming to enhance creditworthiness assessment and borrower support systems.