Artificial intelligence in Peer-to-peer lending in India: a cross-case analysis. Issue 4 (1st February 2022)

• Record Type: Journal Article
• Authors: Anil, Kanwal; Misra, Anil
• Is Part Of: International journal of emerging markets. Volume 17:Issue 4(2022)
• Page Start: 1085
• Page End: 1106
• Publication Date: 2022-02-01
• Subjects: Artificial intelligence -- Fintech -- P2P lending -- Disruptive innovation -- India -- Credit decisioning -- Cross-case analysis -- NBFC-P2Ps; Business enterprises -- Finance -- Periodicals; Investments -- Developing countries -- Periodicals; Commerce -- Periodicals; Developing countries -- Economic conditions -- Periodicals; 330.91724
• DOI: 10.1108/IJOEM-05-2021-0822
• Languages: English
• ISSNs: 1746-8809

Abstract

Abstract : Purpose: This is an original piece of research holding the promise to position itself as a pioneering research to showcase the evolving role of Artificial intelligence (AI) in the Indian peer-to-peer lending (P2P) markets. The research effectively uses the holistic multiple case study design to highlight the phenomenon of how AI as the holy grail of investments is proving to be a game changer for the Indian P2P markets. Design/methodology/approach: The study uses a unique research design and curates six Indian licensed Non-Banking Financial Company (NBFC)-P2P as exemplary cases to cull out unique contextual findings on how AI has penetrated the Indian P2P market and road ahead. The research is based on a total of 18 semi-structured interviews of six NBFC-P2P founders and 12 Fintech and P2P industry experts. These interviews were used as alternate sources of evidence for data triangulation along with within case analysis, cross-case analysis to achieve well-rounded results. Findings: The findings have been propounded in the form of unique, context specific results achieved with a bouquet of six NBFC-P2P cases and supplemented through triangulation of data done through multiple industry experts. Findings indicate that AI has reached that tipping point in India. Research limitations/implications: There is a scope of further refinement of our results with a larger sample size. Therefore future researches could consider conducting a comprehensive study including all existing NBFC-P2Ps in the space. Practical implications: The research builds perspective for improving the practice in many ways. It shows the way to the other P2Ps still stuck to manual underwriting and see merit in AI-driven processes. It would guide them to embrace new technology driven business models to enhance customer experience and champion service transformation by making financial processes faster and secure. It also highlights how some of the P2Ps are scaling up and improving their visibility and outreach through strategic partnerships. Social implications: The research would assist in creating awareness about the unique P2P sector and AI solutions for individual investors, particularly the "new to credit customers" and "thin file borrowers". AI led initiatives in the P2P space validate a certain amount of sophistication thereby giving sanctity to the sector and would therefore enforce confidence in the minds of new age investors and borrowers. Originality/value: This original research unravels avenues for novel and untraversed area in the Indian settings where paucity of extant literature and structured data highlighted a research gap and hence necessitated this study. AI as a form of disruptive innovation offering predictive intelligence to the Indian P2P space and empowering it with process efficiency, cost optimization and client engagement is definitely paving the way for an exponential growth in the Indian Fintech Industry.