Open Journal Systems

Improving Leasing Viability: A comprehensive analysis of customer credibility checks and trustworthiness

Khushi Manjunath, Sundar Srinivasan, Praveen George

Abstract

The contemporary leasing landscape is marked by increasing complexity and risk, necessitating rigorous customer credibility checks as a critical safeguard for lessors. This research paper delves into the multifaceted realm of customer credibility checks, shedding light on their paramount importance in the leasing industry. Central to this study are the diverse methods employed to assess customer credibility. Comprehensive coverage is provided on widely adopted approaches, including credit rating, GST analysis, balance sheet analysis, cash flow analysis, and profit and loss analysis. Through detailed examination, the paper unveils the strengths and limitations of each method, facilitating a nuanced understanding of their applicability in different leasing scenarios.

Keywords

credit rating; GST analysis; balance sheet analysis; cash flow analysis; profit and loss analysis

Full Text:

PDF

References

1. Durand D. Risk Elements in Consumer Instalment Financing. National Bureau of Economic Research; 1941.

2. Altman EI. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 1968; 23(4): 589–609. doi: 10.1111/j.1540-6261.1968.tb00843.x

3. Krugman P. A model of balance-of-payments crises. Journal of Money, Credit and Banking 1979; 11(3): 311. doi: 10.2307/1991793

4. Richards VD, Laughlin EJ. A cash conversion cycle approach to liquidity analysis. Financial Management 1980; 9(1): 32. doi: 10.2307/3665310

5. Capon N. Credit scoring systems: A critical analysis. Journal of Marketing 1982; 46(2): 82–91. doi: 10.1177/002224298204600209

6. Emery GW. Measuring short-term liquidity. Journal of Cash Management 1984; 4(4): 25–32.

7. Viscione JA. Assessing financial distress. The Journal of Commercial Bank Lending Posted 1985; 39–55.

8. Gaharan CI. A Comparison of the Operating Funds Flow Measures of Cash, Net Quick Assets, and Working Capital in Predicting Future Cash Flow [PhD thesis]. Louisiana State University: 1988.

9. Arnold AJ, Clubb CDB, Manson S, et al. The relationship between earnings, funds flows and cash flows: Evidence for the UK. Accounting and Business Research 1991; 22(85): 13–19. doi: 10.1080/00014788.1991.9729413

10. Soenen LA. Cash conversion cycle and corporate profitability. Journal of cash Management 1993; 13: 53–53.

11. Crook J. Credit scoring an overview. Journal of Financial Services Marketing 1997; 2: 152–174.

12. Gallinger G. The current and quick ratios: Do they stand up to scrutiny? Business Credit 1997; 99(5): 24–25.

13. Schneider M, Tornell A. Balance sheet effects, bailout guarantees and financial crises. NBER Working Paper 8060. National Bureau of Economic Research; 2000.

14. Anderson R. The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation. Oxford University Press; 2007. doi: 10.1093/oso/9780199226405.001.0001

15. Abdou H, Pointon J, El-Masry A. Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications 2008; 35(3): 1275–1292. doi: 10.1016/j.eswa.2007.08.030

16. GST reforms and intergovernmental consideration in India. Asia Research Centre Working Paper 26. Asia Research Centre Working Paper; 2009.

17. Rao RK, Chakraborty P. Goods and services tax in India: An assessment of the base. Economic and Political Weekly 2010; 45(1): 49–54.

18. Abdou HA, Pointon J. Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent Systems in Accounting, Finance and Management 2011; 18(2–3): 59–88. doi: 10.1002/isaf.325

19. Abuzayed B. Working capital management and firms’ performance in emerging markets: The case of Jordan. International Journal of Managerial Finance 2012; 8(2): 155–179. doi: 10.1108/17439131211216620

20. Blanco A, Pino-Mejías R, Lara J, et al. Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Systems with Applications 2013; 40(1): 356–364. doi: 10.1016/j.eswa.2012.07.051

21. Guo G, Zhu F, Chen E, et al. From footprint to evidence. ACM Transactions on the Web 2016; 10(4): 1–38. doi: 10.1145/2996465

22. Bumacov V, Ashta A, Singh P. Credit scoring: A historic recurrence in microfinance. Strategic Change 2017; 26(6): 543–554. doi: 10.1002/jsc.2165

23. Bjorkegren D, Grissen D. Behavior revealed in mobile phone usage predicts loan repayment. SSRN Electronic Journal 2018. doi: 10.2139/ssrn.2611775

24. Hill J. Fintech and the Remaking of Financial Institutions. Academic Press; 2018. doi: 10.1016/c2016-0-03863-9

25. Shash AA, Qarra AA. Cash flow management of construction projects in Saudi Arabia. Project Management Journal 2018; 49(5): 48–63. doi: 10.1177/8756972818787976


DOI: https://doi.org/10.59400/fefs.v1i1.349
(76 Abstract Views, 21 PDF Downloads)

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Khushi Manjunath, Sundar Srinivasan, Praveen George


This site is licensed under a Creative Commons Attribution 4.0 International License.