Agriculture Farm Vs Non-Farm Rural Credit

DEPARTMENT OF MANAGEMENT SCIENCES

Report Title: "Measuring Default (Non-Performing Loan (NPL) Risk in Agriculture Farm Vs Non-Farm Rural Credit".

Course : MBA (M2) Morning

Stage of course : 3rd semester

Subject : Business Research Methods

Submitted to : Mr. Mannan Khan

Submitted by :

Names Roll No

Irfan Ali 62.

Date Submitted : 1st November, 2011

Introduction:

Nature of our project is "Agriculture Farm Vs Non-Farm rural credit".

In this report we will discuss default risk found in farming and non-farming sectors and their corresponding methods/techniques to assess and measure that risk. The purpose of this report is to assess important variables in order to correctly classify, differentiate, predict loan applicants to avoid the bankruptcy risk.

We will discuss that there are some factors that are directly affecting financial performance of farmers and how by concentrating on these factors farmers can increase their income or get more return. We will see that how MDA model as a credit score technique is helpful in risk evaluation and assessment and making correct customer classification. How GDP and inflation can affect the farmers incomes. The main purpose of this report and techniques discussed here is to help the management in measuring, controlling and assessing default risk and making the correct decisions about their credits.

Back ground/History:

At past, lenders were not willing to give loans because they were not sure about the payback of their credits. The reason was that Farmers were not well-educated so, they use old technology and methods to cultivate their crops and sometimes face losses. But in the modern age farmers can use latest technology and fertilizers to increase their production and sometimes they have to lend due to lack of resources and often they are not able to perform their debt obligation. Hence finance provider will not be ready to lend due to the probability of default. The important credit risk evaluation needs to predict the financial failures which have been proved from last financial crises. In past, to predict business failures or successes many statistical methods and approaches have been used (Gepp and Kumar, 2008; Karbhari and