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An Analytical Study of Vehicle Loan Management System in NBFCs

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This study examines the effectiveness of the Vehicle Loan Management System (VLMS) implemented by Shriram Finance Ltd., Pune. The research focuses on analyzing loan processing stages such as application, verification, approval, disbursement, and recovery. The study highlights how efficient loan management systems reduce credit risk and improve operational performance in Non-Banking Financial Companies (NBFCs). Primary data was collected through observation and interaction during internship training, while secondary data was gathered from company records and research publications. The findings indicate that strong documentation, credit appraisal, and digital systems significantly enhance loan performance, although challenges like documentation delays and default risks still persist. The study highlights how modern vehicle loan management systems integrate technologies such as artificial intelligence, machine learning, and digital customer interfaces to streamline credit appraisal, loan disbursement, and repayment tracking. These systems enable NBFCs to assess borrower risk more dynamically and provide customized financial products, enhancing customer accessibility and satisfaction. In large NBFCs like Shriram Finance Limited, extensive branch networks, diversified loan portfolios, and technology-driven platforms support efficient loan lifecycle management and real-time monitoring of transactions The findings suggest that effective vehicle loan management systems in NBFCs are characterized by customer-centric approaches, operational flexibility, and technological innovation. The study concludes that continuous digital transformation and risk optimization are essential for NBFCs to sustain growth, enhance financial inclusion, and remain competitive in the evolving lending landscape. Keywords : Vehicle Loan, NBFC, Credit Risk, Loan Management System, Shriram Finance, Financial Services, Regulatory Compliance, Artificial Intelligence in Finance
Title: An Analytical Study of Vehicle Loan Management System in NBFCs
Description:
This study examines the effectiveness of the Vehicle Loan Management System (VLMS) implemented by Shriram Finance Ltd.
, Pune.
The research focuses on analyzing loan processing stages such as application, verification, approval, disbursement, and recovery.
The study highlights how efficient loan management systems reduce credit risk and improve operational performance in Non-Banking Financial Companies (NBFCs).
Primary data was collected through observation and interaction during internship training, while secondary data was gathered from company records and research publications.
The findings indicate that strong documentation, credit appraisal, and digital systems significantly enhance loan performance, although challenges like documentation delays and default risks still persist.
The study highlights how modern vehicle loan management systems integrate technologies such as artificial intelligence, machine learning, and digital customer interfaces to streamline credit appraisal, loan disbursement, and repayment tracking.
These systems enable NBFCs to assess borrower risk more dynamically and provide customized financial products, enhancing customer accessibility and satisfaction.
In large NBFCs like Shriram Finance Limited, extensive branch networks, diversified loan portfolios, and technology-driven platforms support efficient loan lifecycle management and real-time monitoring of transactions The findings suggest that effective vehicle loan management systems in NBFCs are characterized by customer-centric approaches, operational flexibility, and technological innovation.
The study concludes that continuous digital transformation and risk optimization are essential for NBFCs to sustain growth, enhance financial inclusion, and remain competitive in the evolving lending landscape.
Keywords : Vehicle Loan, NBFC, Credit Risk, Loan Management System, Shriram Finance, Financial Services, Regulatory Compliance, Artificial Intelligence in Finance.

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