Early Warning System Model for Non-performing Loans of Emerging Market Fintech Firms

Daniel Zubair and Daitri Tiwary ()
Additional contact information
Daniel Zubair: Birla Institute of Management Technology
Daitri Tiwary: Birla Institute of Management Technology

A chapter in Financial Markets, Climate Risk and Renewables, 2024, pp 221-240 from Springer

Abstract: Abstract We conceptualize an Early Warning System Model for Fintech lenders in emerging market economies, to categorize their retail loan portfolio and identify potentially non-performing loans to mitigate default risk. Our research identifies Key Performance Indicators (KPIs) for evaluating loan portfolio health and monitoring risky loans for Fintech lenders. While ensuring the privacy of borrowers, we develop a synthetic data-based conceptual model complying with the Digital Personal Data Protection Act 2023. The synthetic data reflect the trends of a leading Fintech lender. Parameters for each KPI are established through literature, historical data, and industry standards, minimizing false alarms while detecting potential defaults. Chosen KPIs encompass delinquency rate, debt-to-income ratio, employment history, loan-to-value ratio, and profitability indicators. Model efficacy is validated through metrics like accuracy, precision, and recall, ensuring robust performance. Our EWS model integrates seamlessly into the lending company’s risk management framework, continuously monitoring KPIs against predetermined thresholds. By regularly evaluating the selected KPIs against the established thresholds, the organization could identify loans showing early signs of deteriorating performance. Since the KPIs are concise, Fintech lenders can save time and resources to monitor only those indicators which are crucial. Further, the categorizing the loans, the focus can be shifted (i) to extend credit to more creditworthy borrowers in the green category of loans and (ii) to weed out or limit the credit to borrowers with red-flagged loans. Our EWS enables timely identification if the yellow category loans degrade to the high-risk red category thus enabling the implementation of proactive measures, including loan restructuring, efficient collections processes, and foreclosure procedures. Taking prompt actions may allow the organization to minimize potential losses associated with non-performing loans and safeguard the overall health of the loan portfolio. The study’s findings have substantial implications for the financial industry, offering valuable insights to Fintech lenders aiming to enhance their retail loan portfolio management and risk assessment capabilities. The adoption of the EWS model is aimed at bringing transformative improvements in the company’s loan portfolio management practices. It can significantly enhance the assessment of loan quality, enabling the organization to identify potential issues before escalations. The refined risk assessment practices provide a comprehensive understanding of the organization’s exposure to risks and facilitate the implementation of effective risk mitigation strategies. These strategies, informed by the insights provided by the EWS model, may strengthen credit portfolio management and lead to overall improvements in the performance of the loan portfolio. The study’s implications resonate across the financial industry, regulatory institutions and Fintech firms in retail loans and risk assessment. Our EWS model fosters early identification, timely action, and effective strategies. The study underscores the transformative power of technology-driven solutions in loan management.

Keywords: Early warning systems; Fintech lending; Loan delinquency (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-97-6687-1_4

Ordering information: This item can be ordered from
http://www.springer.com/9789819766871

DOI: 10.1007/978-981-97-6687-1_4

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().