The influence of external factors on the credit risk in leasing industry

Abstract

Credit risk consists of probability of non-return, which may be in the form of bankruptcy or a decrease in financial and credit situation of the lessee. The variables are extracted from the Central Bank. In this study the independent variables are measured with six factors that are called external factors. The external factors are size of leasing, ownership interest rate, foreign exchange, inflation, and Gross Domestic Product (GDP). The present study uses related observations from 31 leasing companies from 2008 to 2013 to find out the determinants of the credit risk. The combined evidences suggest that internal factors such as upfront prepayment, credit insurance contract, security deposits, time and period contract, collateral and guarantees, contract amount, as well as external factors such as interest rate, inflation, foreign exchange, Gross Domestic Product infrastructure, and credit risk are determinants in the policy-making process involving the industrial leasing. Furthermore, the empirical results indicate the size of leasing and ownership are not the significant determinants of credit risk. The results of this dissertation provide several implications for policy-makers in the leasing industry. Policymakers will be better off employing different procedures for leasing activities in the leasing industry.

Risk management of leasing companies

“…The second involves possible changes in interest rates on loan financing, which may later exceed the rates under the lease agreement. In the future, a number of researchers proposed the following classification of risks for leasing companies: credit, interest rate, price, currency, legal, tax, and liquidity risks [2][3][4]. Below is the risk classification of leasing companies.…”

Big Data Credit Report in Credit Risk Management of Consumer Finance

“…The emergence of big data has largely overturned this traditional credit reference model and concept. Big data credit can be regarded as "big data technology + traditional credit," which uses computer and Internet technology to support the model analysis and processing of data analysis and processing involved in credit information activities, which can more fully reflect the economic activities and the credit situation involved [11][12][13].…”

Integrating Algorithmic Decision Making into Small Business Credit Initiatives: a path to Enhanced Efficiency and Inclusive Economic Growth

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