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Credit Model SME

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An article for rating SME's in India
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  Credit Scoring Model for SMEs in India Abstract The document contains the details of how a credit scoring model is formed and the approaches followed. It also contains the details of how the SMEs will be rated in order to provide funds to them. The SMEs taken is from industry like Agro, Automobiles, Manufacturing, Electrical, Plastics and Apparels. The model is built upon only the financial details of all the SMEs considered under the different industries. Introduction The credit scoring model is considered taking in mind the Indian Market picture. Those SMEs who want to grow their business needs funds. These funds starved SMEs can be provided funds with the help of this model. Basically the aim of this model is to provide funds to high and low risk SMEs on different interest rates. SMEs will be rated on a scale of 10 and then within a certain range they will be considered to grant loans. Future scope of this model, once developed the model will be linked online so that all SMEs can apply online for funds. Based on their online ratings they will be considered for loans without any collaterals. Methodology Below are the steps listed that have been followed to form this model. 1.   Calculation of Ratios As stated above, the whole model is based on financial data so financial ratios for all the SMEs in different industries have been calculated. Based on all the companies data 15 ratios have been taken to check the stableness of an SME. These include ROA, Net Profit, Interest Coverage, Debt Ratio, Total Asset Turnover, Working Capital/Total Assets, Debt Coverage, Retained Earnings/Total Asset, D/E, Operating Cash, Rate of Sales, Profit Growth Rate, Capital Increment, Operating Margin and Asset Growth.  Now these ratios can have different weightage for different industries. For eg., for Plastics industry debt coverage, debt ratio is below 1 and also is not varying on larger range. Some SMEs have reduced their debt over the period of 5 years. Also, operating profit margin (%) have also increased to a small extend for the SMEs in the 5 years. For the auto spare parts companies, debt plays an important role, so we should see how the ratios related to debts and assets behaves, these ratios include, Return on Assets, Debt- Equity Ratio and interest coverage ratio, interest coverage ratio also helps in determining the solvency of the firm. Debt coverage ratio has also shown steady growth rate excluding IP rings. Debt equity ratio is high because the sector is a manufacturing sector  Credit Scoring Model All Ratios- M 2.   Factor Analysis After calculating the ratios for different SME in different industries, Factor Analysis was done. The primary aim of factor analysis is to take out those ratios which will have major impact in building the model. It also reduces the number of variables that you have taken. The analysis and screen shots are below The KMO and Bartlett’s test tells us whether our Factor Analysis is valid or not. The value should be greater than 0.5 in order for a satisfactory Factor analysis. In our case it is 0.651, which shows that our Factor analysis is satisfactory. KMO measures strength of relationship among variables. Communalities tells us how much percentage of a variable we have extracted or how much of the variance in the variables has been accounted for to form the new factors. Here ROA is extracted 92.4, Debt_Coverage 99.9% and so on.   The Total variance Explained chart tells us how many factors are there and what is total percentage the will form to explain the model. Here, we can see Initial Eigenvalues Cumulative % says that total 82.7% have been explained by these five factors which have come after the analysis. It also tells us how much a single factor is affecting. So, 1 st  factor accounts for 30.829% of the variance, 2 nd  factor for 21.226% and so on. The cumulative is 82.72% for all the 5 factors.  Pattern Matrix is the main matrix from which we can tell which ratio is majorly forming which factor. This matrix is formed by rotating the variables as many as 15 times with each other and then finding the numbers for the factors. Now from the above table we can see that ROA and Debt_Equity is majorly affecting Factor 3 (here we consider only those ratios which have value greater than 0.5). So, the largest value we take from the two ratios which Debt_Equity ratio majorly forming factor 3. Below is the table summarized for all the factors based on the above observation. Factor 1 Interest Coverage Factor 2 Debt Coverage Factor 3 Debt_Equity Factor 4 Operating Margin Factor 5 Rate of Sales Based on the above analysis, we formed 5 factors which will have a major on the model. As shown by the analysis as well 82% of total model is explained by these five factors. Also, we have debt ratios coming which can be seen as a prime explaining ratios in some industries. This model shows considerable results in explaining the factors but more work needs to be done to refine some components to make it convenient to provide funds to SMEs STP_Credit Scoring Model.sav   Output2_CREDIT_MODEL.spv  
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