FINANCIAL FRAUDS IN VEHICLE FINANCE COMPANIES: A SECONDARY DATA ANALYTICS STUDY WITH REFERENCE TO TELANGANA STATE, INDIA

Abstract

Abstract

Purpose – This study examines the scale, structure and drivers of financial fraud that affects vehicle finance companies (VFCs) in Telangana, one of India's fastest-growing vehicle markets and, according to official crime statistics, the state with the highest incidence of cyber-enabled fraud. It asks how national lending-fraud patterns, state-level economic-offence trends and borrower grievances against non-banking finance companies (NBFCs) combine to shape the fraud-risk environment in which VFCs operate.

Design/methodology/approach – The paper builds four secondary datasets from Reserve Bank of India (RBI) and National Crime Records Bureau (NCRB) publications covering 2004-05 to 2024-25. It applies log-linear trend estimation, compound growth analysis, a Chow structural-break test, Spearman and Pearson correlation (including first differences), the Mann–Whitney U test, log–log elasticity estimation, Lorenz–Gini concentration analysis, and chi-square tests with Cramér's V. These results are integrated with a document-based typology of vehicle-finance fraud grounded in the fraud triangle, fraud diamond and routine activity theories.

Findings – Reported bank frauds grew at a trend rate of 10.3% a year, with a statistically significant break after 2017-18 (F = 12.99, p < 0.001), when annual growth in incidence rose from about 5.8% to 29.8%. Loan-related (advances) frauds accounted for 90.8% of the cumulative ₹5.77 lakh crore lost over two decades, despite being fewer than half of all cases. In Telangana, economic offences rose 3.4-fold and cyber crimes 10.1-fold between 2019 and 2024; 

the two series move almost in lockstep (r = 0.97 on first differences, p < 0.01), and the state's share of India's cyber crimes climbed from 6.0% to 26.7%. Fraud value in the state is highly concentrated (Gini ≈ 0.84): about 3.5% of cases account for an estimated 69% of the value. Complaints about loans and advances made up 68.7% of complaints against NBFCs received by the RBI Ombudsman in 2024-25, up from 56.4% two years earlier (z = 34.9, p < 0.001).

Research limitations/implications – Public statistics do not separate frauds against VFCs from other lending frauds at the state level; the findings therefore describe the fraud-risk environment rather than firm-level losses. The paper sets out a primary-data agenda to close this gap.

Practical implications – VFCs in Telangana need a two-tier control architecture: automated, high-volume screening for low-value identity, document and digital-channel frauds, and intensive, investigation-led oversight for the small number of high-value insider, dealer and collusive frauds that account for most losses.

Originality/value – To the authors' knowledge, this is the first study to triangulate RBI fraud statistics, NCRB state crime data and RBI Ombudsman grievance data to characterise fraud risk for vehicle financiers in a single Indian state, and to map that evidence to a theory-based fraud typology specific to vehicle lending.