Analyzing Credit Risk Models in The Digital Lending Era
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Date
2025
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Publisher
NHCE
Abstract
In the evolving landscape of financial services, the adoption of digital technologies has significantly reshaped the way credit is delivered and managed. One of the most notable transformations has been the emergence and rapid growth of digital lending platforms. These platforms leverage advancements in data analytics, mobile connectivity, and artificial intelligence to provide borrowers with quick and seamless access to credit—often without relying on traditional banking channels. While this development has improved financial inclusion and customer reach, it also introduces new complexities, especially in the area of credit risk assessment.
The primary aim of this study is to assess how effective various credit risk assessment models are—ranging from traditional statistical methods to modern machine learning algorithms—within the unique context of digital lending. A key focus is on how alternative data sources, such as behavioral and mobile usage data, can enhance credit risk evaluation, particularly for borrowers lacking conventional credit histories. Insights gained during the research serve as a foundation for evaluating the adaptability and reliability of different modeling techniques in today’s dynamic financial environment.
Project Purpose
The main objective of this research is to evaluate the accuracy, feasibility, and real-world application of different credit risk models in digital lending . digital lenders increasingly catering to underserved and first-time borrowers, there is a growing need for innovative risk assessment approaches.