CREDIT LIMITS IN MASS UNSECUERED LENDING

This article focuses on addressing the fundamental challenges of mass unsecured lending, specifically the decision-making process. The study examines two primary questions: whether a loan should be granted to a client and, if so, what the optimal loan amount (credit limit) should be. The research establishes that implementing a cutoff point for unprofitable loans serves as both a necessary and sufficient condition for maximizing financial returns. It is demonstrated that this cutoff point exists due to the sign-changing nature of marginal revenue taking into account credit risk. Moreover, the article explores the issue of setting credit limits while incorporating multiple essential factors, including credit risk management, liquidity constraints of financial institutions, and the smooth adaptation of credit limits according to the probability of default of the client. To derive a universal decision-making curve that represents the relationship between credit limits and probability of default, the study proposes shifting from absolute credit limits to dimensionless ones. This transformation ensures greater adaptability and consistency in credit policy formulation. The research employs mathematical modeling techniques to systematically describe the credit decision-making process and to determine optimal credit limits. Additionally, methods of control theory are applied to substantiate strategies for managing the lending process effectively. A graphical method is also utilized to visually represent the obtained results, facilitating a deeper understanding of dependencies and optimizing decision-making strategies in mass unsecured lending. The findings of this study contribute to the development of more efficient lending strategies that enhance financial stability and profitability while controlling the associated risks. The proposed methodology can be implemented by financial institutions (banks and microfinance organizations) to refine their credit decision-making frameworks, improve the efficiency of mass lending operations, and ensure a more balanced approach to risk assessment. These insights hold practical significance in the optimization of lending policies, ultimately leading to sustainable credit portfolio growth and long-term financial resilience.