Authors:
The Indian economy and the lending ecosystem appear to be making a gradual recovery from the pandemic’s impact. The RBI’s Financial Stability Report (December 2022) depicts that overall, the banking sector is stabilising with a seven-year low in the gross non-performing assets (GNPA) ratio for banks. However, there continue to exist pockets of distress. For instance, the aforementioned report mentions that as of September 2022, one-sixth of the micro, small and medium enterprise (MSME) accounts that availed funds under the Emergency Credit Line Guarantee Scheme (ECLGS) have turned into NPAs. Moreover, it is foreseen that the proportion of bad loans in the retail and small business sectors is likely to rise further considering the accelerated growth being witnessed in these sectors. Such over-saturation of credit markets has adverse consequences for borrowers, financial service providers, as well as the larger financial system.The most immediate and significant consequence is experienced by the borrowers who may fall into a state of over-indebtedness i.e., wherein an individual/household continuously struggles with loan repayments. If the loss of repayment ability extends to a large proportion of the customer base, the providers may also face extensive economic losses. A second-order problem emerges when over-indebtedness is widespread in a region as it may cause significant political and social unrest. Such a situation was observed during the microfinance crises in Andhra Pradesh and more recently, in Assam. To prevent such escalating over-indebtedness, and the resultant debt distress, there is a need to continuously monitor the state of over-indebtedness. Providers, being most proximate to the borrowers are well-suited, but unfortunately not well-equipped, for the task. At Dvara Research, we have embarked on an action project titled “Debt Distress Protocols” to refine the design of a tool that predicts the impact of over-indebtedness on customers, i.e., debt distress, using an ML-based model. The model is currently being finalised in collaboration with IIT Madras. Upon finalisation of the model, thus detecting distressed households, we shall pilot a series of solutions that providers can deploy, within the bounds of credit contracts, to address and alleviate such distress. The insights from the field studies shall be used to develop a comprehensive set of guidelines and protocols that detail how providers can introduce these solutions to mitigate debt distress.
Further information regarding the project has been provided in this brief.
Cite this Item:
APA
Neelam, A., Nambiar, A., & Bhattacharya, D. (2023). Identification & Alleviation of Over-indebtedness: Introducing the Debt Distress Protocols Project. Retrieved from Dvara Research.
MLA
Neelam, Amulya, Anjali Nambiar and Dwijaraj Bhattacharya. “Identification & Alleviation of Over-indebtedness: Introducing the Debt Distress Protocols Project.” 2023. Dvara Research.
Chicago
Neelam, Amulya, Anjali Nambiar, and Dwijaraj Bhattacharya. 2023. “Identification & Alleviation of Over-indebtedness: Introducing the Debt Distress Protocols Project.” Dvara Research.
