IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

OPERATIONAL CHALLENGES, CUSTOMER SEGMENTATION, AND AUTOMATION READINESS IN VEHICLE FINANCE OPERATIONS

Diya Mehta

Dr. Sanjay Christian

JG University Ahmedabad Gujarat-India
DOI:https://doi.org/10.55041/ijcope.v2i5.344

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Download PDF Review Report

Abstract

This study investigates operational challenges in vehicle finance at a multi-brand automobile dealership, focusing on documentation difficulties, refinance processes, customer preferences, lead conversions, tracking tools, and automation readiness. Primary data from 269 employees via structured questionnaires were analyzed using descriptive statistics, Welch ANOVA and Games-Howell post hoc tests in SPSS to examine differences across customer categories. Findings reveal Non-Income Proof (NIP) customers as the most challenging for documentation (43% frequency, mean=1.34), followed by Agri-based (29%, mean=1.54). Own-vehicle refinance is easiest (mean=1.45), while external balance transfers are hardest (mean=3.88). Customers prefer in-house finance (mean=4.49); walk-ins and referrals yield top conversions. Excel dominates tracking (95%), with high readiness for automation (96%), prioritizing scheme comparison dashboards (26%). Welch ANOVA confirms significant documentation differences across categories , with Games-Howell tests showing NIP significantly harder . The study highlights bottlenecks and digital tool needs for dealership efficiency.

Keywords: Auto finance, operational bottlenecks, NIP customers, lead conversion, scheme comparison

How to Cite this Paper

Mehta, D. (2026). Operational Challenges, Customer Segmentation, and Automation Readiness in Vehicle Finance Operations. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.344

Mehta, Diya. "Operational Challenges, Customer Segmentation, and Automation Readiness in Vehicle Finance Operations." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.344.

Mehta, Diya. "Operational Challenges, Customer Segmentation, and Automation Readiness in Vehicle Finance Operations." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.344.

Search & Index

Google Scholar Crossref DOI ResearchGate

References


  1. Kamble, S. S. (2011). A study of consumer perception towards car finance schemes in Dakshina Kannada district. International Journal of Management Studies, 18(2), 45–58.

  2. Mann, B. J. S. (2012). Factors influencing Internet banking adoption in India. International Journal of Bank Marketing, 30(4), 283–301. https://doi.org/10.1108/02652321211236968

  3. Niranjan, S. (2017). Barriers to financial inclusion in India: A study of documentation, literacy, and access constraints. Journal of Rural and Development Studies, 9(1), 61–75.

  4. Yaseen, G. (2018). Borrower and loan characteristics in subprime auto loan default risk: Evidence from the UK. Journal of Financial Services Research, 54(3), 235–260. https://doi.org/10.1007/s10693-018-0294-7

  5. Óskarsdóttir, M., Bravo, C., Van Vlasselaer, V., Snoeck, M., & Baesens, B. (2018). Social network analytics for credit scoring: The value of network-based variables. Expert Systems with Applications, 112, 52–65. https://doi.org/10.1016/j.eswa.2018.06.030

  6. Putri, N. E. (2020). Customer relationship management strategies and retention in auto-loan financing. Journal of Relationship Marketing, 19(4), 315–332. https://doi.org/10.1080/15332667.2020.1767192

  7. Chen, C.-C., Tseng, Y.-H., & Ho, J.-C. (2020). Text mining and analytics applications in financial services: A review. Journal of Financial Innovation, 6(1), 1–22. https://doi.org/10.1186/s40854-020-00185-6

  8. Hadji Misheva, B., Osterrieder, J., Kulkarni, O., & Lin, S. (2021). Explainable AI in credit risk management. Journal of Risk Management in Financial Institutions, 14(3), 215–228.

  9. Makreo Research and Consulting. (2023). India used car finance market: Growth drivers, digital lending, and risk trends. Makreo Research & Consulting.

  10. IMARC Group. (2024). India car loan market: Industry trends, size, share, growth forecast 2024–2033. IMARC Group.

  11. Makreo Research and Consulting. (2025). Electric vehicle financing in India: Emerging loan models and technology adoption. Makreo Research & Consulting.

  12. Mordor Intelligence. (2025). India auto finance market: Competitive landscape and digital transformation. Mordor Intelligence.


Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: May 11 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Back to Volume 02, Issue 05View All Issues Next Article

← Previous Article

Open AGI — Multi-Agent Automation Framework for AI-Orchestrated Software Development

Next Article →

Operational Efficiency of Customs House Agents (CHA) in Import and Export
Scroll to Top