A Project Report on

“CREDIT SCORING TOOL FOR NEW INDIVIDUAL LOAN PRODUCTS”

Submitted to

KIIT SCHOOL OF RURAL MANAGEMENT

Bhubaneswar, India

Submitted by

Abhisek Tak (9201002)Rojalin Pattnaik (9201032)

Host Organization: MIMO FINANCE

Faculty Guide – Dr. Prasun Das

MTS-1 Coordinator – Prof. Nandini Sen

A report submitted in partial fulfillment of the requirement for Masters in Rural Management, 2009-11

Date of submission: 18th September 2010

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SCHOOL OF RURAL MANAGEMENT, KIIT UNIVERSITY

BHUBANESWAR.

The MTS-1 Report of:

Abhisek Tak (9201002)

Rojalin Pattnaik (9201032)

Candidate for the degree of MBA Rural Management

Is hereby APPROVED

Dr. Prasun Das

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Products of Mimo Finance

Joint Liability Group (JLG) MESO Loan

Individual Home Loan

Individual Diary Loan

Individual Business

Loan

Individual Micro

Enterprise loan

Executive Summary

Mimo Finance is a Micro Finance Institution (MFI) in north India working mainly for empowering women and changing lives of poor. They have two departments, MESO and Joint Liability Group (JLG).They give loans for four products in MESO department to individuals, that are –

MIMO create sustainable sources of income for women, which enables them to fulfill their greater corporate mission to help families obtain health care, access to education, and the empowerment necessary to make choices that best serve their needs. They work in 6 states of India (Uttarakhand, Uttar Pradesh, Hariyana, Rajasthan, Himachal Pradesh and Madhya Pradesh).

The project for the Management Training Segment(MTS) was “Credit Scoring Tool for New Individual Loans” for MESO department for Individual Home and Business Loan. The Head Office of MIMO is in Delhi and the reporting officer was Mr. Thirunavukkarasu. The study area assigned was Saharanpur, Uttar Pradesh (West). This also happens to be the regional office (RO) for six districts. The areas covered for the study were many parts of Saharanpur city and Chuttmalpur. The duration for the study was two months, which was from 5 th of July to 3th of September 2010.

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Credit Scoring

“A statistically derived numeric expression of a person's creditworthiness that is used by lenders to access the likelihood that a person will repay his or her debts. It is a number between 30 and 85 - the higher the number, the more creditworthy the person is deemed to be”

Objectives of the study

To understand working guidelines of credit process in the organization. To have a complete knowledge on existing credit scoring procedure. To develop credit scoring tool for individual lending for business and home loans. To prepare one user-friendly excel sheet with credit scoring components. To implement the credit scoring tool with the existing customer.

Advantages of credit scoring

Ranking the customers based on their credit worthiness. Decide whether to extend credit & how much credit to extend Predict loan-default chances/ poor repayment Identify/isolate not so good customers Predict the future repayments Reduce individual biasness

The methodology adopted for the research was both qualitative and quantitative data collection through questionnaire and personal interview with the existing clients in Saharanpur and nearby areas. Then the data was analyzed and credit scoring tool was designed in MS Excel with the mathematical formulae’s.

After completing the credit scoring tool for the MESO department for the Individual Business Loan and Individual Home Loan it would be more convenient and easy for the credit executive in particular to access the repayment capacity of the applicant and also as a part of the tool it has been suggested the amount of loan to be given as per the applicants marks received. This would save time on the part of the officer to estimate his/her (applicant) credit worthiness and also increase the rate the loan could be disbursed. After the tool this would reduce the personal biasness and every client would be treated on the same platform and also the client could get the loan as per his/her credit worth.

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Acknowledgement:

It gives us immense pleasure when work comes to an end successfully. Our acknowledgements are many times more than what we are expressing. We shall ever remain thankfully indebted to all those known and unknown personalities, who have directly and indirectly encouraged us to achieve our goal and enlightened us with the touch of their knowledge and constant encouragement.

We would like to take this opportunity to duly acknowledge the contribution of a number of people without whom the MTS-1 would not have been a successful exercise. We are thankful to School of Rural Management, KIIT University for providing MTS project where we could apply our theoretical knowledge during the study.

With immense pleasure, we express our profound sense of reverence and gratitude to Dr. L.K Vaswani director KIIT School of rural management, BBSR for providing us with an opportunity to work with MIMO Finance, a renowned name in Micro Finance based in Delhi.

This project reports marks the end of our Management Traineeship Segment (MTS) of MBA in Rural Management in KSRM. We are grateful to our Reporting Officer as well as the Operations Director of MIMO Finance Mr. Thirunavukkarasu at Delhi for offering us the opportunity to work on “Credit Scoring Tool For New Individual Loan Products” project. Field research for this paper was conducted at Saharanpur, U.P in July and August 2010. We would like to thank MIMO for its willingness to participate in the study and to share the results. The openness and responsiveness with which the staffs of MIMO worked with us contributed greatly to the depth of the analysis. We wish to extend gratitude for the warm welcome we received from MIMO staffs including our Reporting Officer as well as the Operations manager (Mr. Thirunavukkarasu), area regional manager (Mr. Naresh Kumar), credit executive (Mr. Narinder Kumar), branch managers and different clients at Saharanpur and for devoting their precious time to providing valuable suggestions, guidance as well as their abiding inspiration throughout the project period to accomplish the project work successfully. We take this opportunity to express our deep sense of gratitude to all the staff members of Cluster offices specially Mr. Satish and Mr. K.P Singh for their kind cooperation during our field visit.

We also thank our MTS coordinator Prof. Nandini Sen, our faculty guide Prof. Prasun Das and all the faculty members of KSRM for their guidance and support in carrying out this project.

We have no words to express our feeling to those who blessed us from the depth of their heart, provided tremendous encouragement, moral support and sacrificed all their comforts for the sake of our academic achievements, they are our parents and land lord. . Last but not the least we thank the ALMIGHTY for blessing us with enough patience, endurance and strength in accomplishment of the endeavor.

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Table of ContentsChapter – 1...................................................................................................................................................1

Introduction....................................................................................................................................................1

1.2 Rationale behind choosing the project:....................................................................................................2

1.3 Problem Statement:..................................................................................................................................2

1.4 Research Objectives:................................................................................................................................2

1.5 Limitations of the study:..........................................................................................................................2

1.6 Highlights of credit scoring:....................................................................................................................3

1.7 Organizational Profile..............................................................................................................................3

1.7.1 Organization History:............................................................................................................................3

1.7.2 Vision:...................................................................................................................................................3

1.7.3 Mission:................................................................................................................................................4

1.7.4 Areas of Operations of MIMO:.............................................................................................................4

1.7.5 Organization Structure:.........................................................................................................................5

1.7.6 Competitors of MIMO in Saharanpur:..................................................................................................5

1.8 Benefits of credit scoring:........................................................................................................................6

1.9 Advantages of credit scoring:..................................................................................................................6

Chapter - 2....................................................................................................................................................7

Methodology..................................................................................................................................................7

2.1 Study Area...............................................................................................................................................7

2.2 Type of Data............................................................................................................................................7

2.3 Techniques of data collection..................................................................................................................7

2.4 Study period.............................................................................................................................................7

2.5 Sample size..............................................................................................................................................7

2.6 Steps for doing project:............................................................................................................................8

2.7 Process for credit scoring project:............................................................................................................9

Chapter – 3.................................................................................................................................................10

Literature Review.........................................................................................................................................10

Types of Credit Scoring Model....................................................................................................................11

Chapter - 4..................................................................................................................................................14

Findings and Discussion..............................................................................................................................14

4.1 List of documents required before the loan is provided........................................................................14

4.2 Proposed Loan Process..........................................................................................................................14

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4.3 Financial analysis:..................................................................................................................................15

4.4 Technical Analysis:................................................................................................................................16

Chapter- 5...................................................................................................................................................17

Case Study...................................................................................................................................................17

5.1 Personal Details.....................................................................................................................................17

5.2 Personal Details.....................................................................................................................................19

Chapter – 6.................................................................................................................................................21

Conclusion & Recommendations................................................................................................................21

6.2 Recommendations for loan amount.......................................................................................................21

6.2.1 Recommendation for the policy..........................................................................................................21

6.3 Important guidelines..............................................................................................................................21

Annexure - 1: Questionnaire........................................................................................................................22

For business loan..........................................................................................................................................22

For Home loan.............................................................................................................................................24

Annexure – 2: Document Checklist cum Sequence of Documents.............................................................26

Annexure – 3(Parameters and Sub parameters for Home Improvement Loan)...........................................27

Annexure – 4(Parameters and Sub parameters for Home Rental Loan)......................................................28

Annexure – 5(Parameters and Sub parameters for Business Loan).............................................................29

Annexure – 6 : Score card of Home Improvement Loan.............................................................................30

Annexure – 7: Score card of Home Rental Loan.........................................................................................31

Annexure – 8: Score card of Business Loan................................................................................................32

Annexure 9: Tool test Results......................................................................................................................33

Reference:....................................................................................................................................................34

List of Figures

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Figure 1: Map of Areas of operations..........................................................................................................4Figure 2: Structure of MESO.......................................................................................................................5Figure 3: Tool Testing Results..................................................................................................................33

List of Flow Charts

Flow Chart 1: Steps for doing project..........................................................................................................8Flow Chart 2: Process for Credit Scoring....................................................................................................9Flow Chart 3: Proposed Loan Process.......................................................................................................14Flow Chart 4: Mathematical model used for the tool.................................................................................16

List of Flow Charts

Table 1: Organizational Structure................................................................................................................5Table 2 :Checklist cum Sequence of Documents.......................................................................................26Table 3: Parameters and Sub parameters for Home Improvement Loan....................................................27Table 4: Parameters and Sub parameters for Home Rental Loan...............................................................28Table 5: Parameters and Sub parameters for Business Loan......................................................................29Table 6: Score card of Home Improvement Loan......................................................................................30Table 7: Score card of Home Rental Loan.................................................................................................31Table 8: Score card of Business Loan........................................................................................................32

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Glossary:JLG – Joint Liability Group

MEL – Micro Enterprise Loan

NBFC - Non-Banking Financial Company

CEO – Chief Executive Officer

COO – Chief Operational Officer

BL – Business Loan

HL – Home Loan

DL – Dairy Loan

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Chapter – 1Introduction

1.1 Background of the project:Credit scoring is used throughout the world to process many types of small-value loan transactions. It has been applied most widely and successfully for personal credit cards and consumer and mortgage loans. Repayment risk for these products is closely linked to verifiable factors such as income, credit bureau information, and demographic factors such as age, education, and homeowner status. More recently, credit scoring has been used to evaluate loans to small and micro businesses, but even in the most developed financial markets, credit scoring for small business loans generally works in conjunction with a judgmental process rather than as an independent decision-making tool.

Credit scoring systems help to:

Streamline the lending process Improve loan officer efficiency Increase the consistency of the evaluation process Reduce human bias in the lending decision Enable the bank to vary the credit policy according to risk classification, such as

underwriting or monitoring some lower risk loans without on-site business inspections Better quantify expected losses for different risk classes of borrowers Reduce time spent on collections, which in some markets claim up to 50 percent of loan

officers’ time.

One conceptual difficulty with embracing credit scoring for microfinance is that a data-driven business approach does not intuitively seem like a good fit for reaching data-poor clients who have been typically excluded by banks. Some examples of data limitations in the microfinance field are:

The self-employed poor frequently cannot document income and credit history Small businesses purposefully misstate tax accounting statements, particularly profit, to

reduce their tax burden Microfinance borrowers are rarely included in credit bureaus, or credit bureaus

themselves are underdeveloped in many markets.

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1.2 Rationale behind choosing the project: The existing clients are only offered JLG loans by MIMO and the other companies. To take one step ahead MIMO is now providing individual loans (business loan, home

loan, microenterprise loan and diary loan). Hence, the credit tool we have designed would help them (MIMO) to assess the

repayment capacity of the individual & also reduce the risk involved in the repayment on the loan distributed.

1.3 Problem Statement:

Till date the organization was using a tool which was designed specifically for microenterprise loans (MEL), which was designed for JLG model. So, there were few parameters and sub parameters which were not applicable for an individual and here is where they lost their marks in the grading tool and hence they were not eligible for the loan.

Another problem that prevailed was that due to lack of credit tool personal biasness was generally indulged in the process of loan distribution.

1.4 Research Objectives:

To understand how an MFI/NBFC functions To understand the working environment in the organization. To have a complete knowledge of the credit scoring tool and its implementation. To identify credit-worthy customers To develop credit scoring tool for business lending and home loan. To put my knowledge on excel to actual implementation

1.5 Limitations of the study:

The study has only been conducted in Saharanpur and its neighboring areas but it is made for the entire operation of Memo Finance(the entire Area where it operates)

The number of clients available for the “Business Loan” were less in number as this is a newly launched product.

The parameters and sub parameters on the basis of individual observations were developed out of individual limited knowledge

1.6 Highlights of credit scoring: Better assess the repayment capacity of the client

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Reduce the number of defaults Reduce individual biasness Process data quickly and inexpensively Reduce risk and increase efficiency of recovery process

1.7 Organizational Profile

1.7.1 Organization History:

2007: Shah Sandhu Finance (NBFC) was acquired by Manab Chakraborty (Promoter) & Bellwether MF Fund

2008: Change of company name to Mimoza Enterprises Finance (Mimo Finance) 2009: Silver Certificate for reporting on Social Indicators to MIX Market awarded

(CGAP, Michael & Susan Dell Foundation, Ford Foundation & MIX Market) CRISIL awarded a rating of MF4 (1-8 scale); top rating received by early stage MFIs in

India

Mimoza Enterprises Finance Co. Pvt. Ltd. (NBFC) offers its microfinance products under the MIMO Finance brand. Through microfinance, they create sustainable sources of income for women, which enables them to fulfill their greater corporate mission to help families obtain health care, access to education, and the empowerment necessary to make choices that best serve their needs.

Having established MIMO’s operations in Uttarakhand, they now operate across the various states of North India. Specifically, they have established regional hubs and branch offices in urban and peri-urban areas along the major highways and towns in Uttarakhand, Uttar Pradesh, Haryana, Rajasthan, Himachal Pradesh and Madhya Pradesh. The present time is an exciting one for MIMO, as MIMO continuously look to take advantage of the myriad expansion opportunities that present themselves in the North Indian "Hindi Belt."

As of 31st March 2010 they have 52,345 active loan clients. They have disbursed 102,138 loans in total.

1.7.2 Vision:

To reach One million households in India by 2015.

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1.7.3 Mission:To strengthen the economic capacity of urban and rural micro and small business operators to ensure their economic self-reliance.

1.7.4 Areas of Operations of MIMO: Uttarakhand Uttar Pradesh Madhya Pradesh Himachal Pradesh Haryana Rajasthan

Figure 1: Map of Areas of operations

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BUSINESS HEAD

Sales and Recovery Dept. Credit Appraisal Dept.

GM Sales

Sales Manager

Unit Manager (UM)

Recovery OfficerMeso Loan Officer Verification Officer

Credit Exec. /Asst.

Credit Manager

GM Credit

1.7.5 Organization Structure:Table 1: Organizational Structure

Name of the person DesignationManav Chakravorty C.E.OSwaminathan A. Aiyar Journalist and Angel Investor of MIMO FinanceR. Venkatram Reddypally Nominee Director of the Bellwether Microfinance Fund

and Angel Investor of Mimo FinanceMurli Shrinivas C.O.OR.Thirunavukkarasu Operations DirectorDr. Rahul Kumar C.F.OMohd. Jeena HR and Training ManagerSandeep Mahajan Internal AuditorAnkit Bukharia MIS Head

Figure 2: Structure of MESO

1.7.6 Competitors of MIMO in Saharanpur: Fusion Bank SKS Basix Ujeevan Bandhan Sonata Disha (SHG) Suvidha(Individual) Share

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1.8 Benefits of credit scoring:Scoring can help credit grantors keep pace with various changes in the market place, while providing the following benefits to impact their business -

Speed - cut response time from days to minutes so that credit grantors can respond quickly to their customers

Efficiency - manage growth, manage staffing levels and automate their decision policy Minimize risk - maintain high approval levels, reduce bad debt and better manage credit

portfolios Consistency - reduce training time, achieve consistent policy and provide consistent

explanations Maximize sales - identify those customers who have financial strength to handle

increased credit limits

1.9 Advantages of credit scoring:

Advantages

To predict the likelihood of losing valuable customers To decide whether to extend credit & how much credit to extend To predict loan-default chances or poor repayment behaviors. To identify/isolate bad customers To predict the future payment behavior Reduce individual biasness

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Chapter - 2Methodology

2.1 Study AreaSaharanpur is a city and a Municipal Corporation in the state of Uttar Pradesh in northern India. It is the administrative headquarters of Saharanpur District as well as Saharanpur Division. Saharanpur is internationally famous for its wood carving work cottage industry. It is a thriving market of local agricultural produce, including basmati rice and mangoes. A variety of agro-based industrial enterprises such as textiles, sugar, paper and cigarette factories are located in it. Our field covered Shakh pura, Chipyan, Chak Devli, Noor Basti, Rakesh Takis, Sadakdudhli, Kailashpur, Mohajampura, Mahaswari and,Chak Harati.

2.2 Type of Data

The data we had to collect was qualitative as well as quantitative.

2.3 Techniques of data collection

The various ways and means of collection of data was via a questionnaire, personal interview with the potential clients and existing clients, discussion with the staff of MIMO, and close observation along the entire stay. The project allotted required more of desk work as this was for the organization and had to full fill their expectation, so there was lot of redoing of the tool.

2.4 Study periodThe entire study duration was of two month dated 6th July to 3rd of September.

2.5 Sample sizeA sample of 15 from both business loan and home loan was taken.

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2.6 Steps for doing project:Flow Chart 1: Steps for doing project

Field visits (two days)

Prepared scope and timeline for the project

Preparation of questionnaire

Visiting to the clients and analysis

Collection of data by interviewing the clients

Analysis of data

Prepared the credit scoring tool for both home loan and business loan

Testing of the tool

First we visited to the clients in Saharanpur as it was easy for us to collect data. Then scope and

time line were prepared for the project. Then we prepared questionnaires for both home loan and

business loan. Then we went to the clients and data were collected through the questionnaire and

personal interview with the clients. Then data analysis was done. After the analysis of data we

prepared credit scoring tool for home loan and business loan. Then report was prepared.

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Preparation of report

2.7 Process for credit scoring project:Flow Chart 2: Process for Credit Scoring

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Questionnaire

Collection of quantitative and qualitative data through personal interview with existing clients

Analysis of data

Decide parameters and sub-parameters for designing credit scoring tool

Credit scoring tool designing through MS Excel

Testing of the tool

Chapter – 3Literature Review

“A statistical technique used to determine whether to extend credit (and if so, how much) to a borrower. Credit scoring is often considered more accurate than a qualitative assessment of a person's credit worthiness, since it is based on actual data. When performing credit scoring, a creditor will analyze a relevant sample of people (either selected from current debtors, or a similar set of people) to see what factors have the most effect on credit worthiness. Once these factors and their relative importances are established, a model is developed to calculate a credit score (a number indicating how credit-worthy the applicant is) for new applicants. The officer inputs applicant-specific information for each variable in the model, and can thus find out how credit-worthy he/she is. Developing a credit scoring model is usually a time-consuming, complicated process given that creditors often have to look at a large sample and consider many different variables. Thus, these models are usually developed at the firm level as opposed to the individual credit office level. Some of the factors considered when developing a credit scoring model are outstanding debt, the number of credit accounts maintained, age, income, credit history, etc. As required by the Equal Credit Opportunity Act, a credit scoring model cannot consider race, sex, marital status, national origin, or religion. If age is considered, the analysis should be such that older people are given equal consideration in a credit application”

(Source: InvestorWords.com)

“A statistically derived numeric expression of a person's creditworthiness that is used by lenders to access the likelihood that a person will repay his or her debts. A credit score is based on, among other things, a person's past credit history. It is a number between 300 and 850 - the higher the number, the more creditworthy the person is deemed to be”

(Source: www.investopedia.com)

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Types of Credit Scoring ModelCredit score assures the credit worthiness of the consumer. There are basically two types of credit scoring models. One is Statistical Scoring Model and the other is Judgmental Scoring Model.

Judgmental Scoring Model: A judgmental scoring model is primarily based on traditional standards of credit analysis. Judgmental Scoring Model considers factors like bank and trade references, payment history, credit bureau ratings and financial statement ratios to produce the total credit score of the individual. Judgmental scoring model is comparatively easier to interpret and is almost free from ambiguity.

Statistical Scoring Model: Statistical model works in almost the same way as judgmental model. Statistical model considers many factors at the same time. This model analyzes multivariate (any procedure which involves two or more variables) correlation (A reciprocal relation between two or more things) to assign statistically derived weights used in the model. The factors are normally obtained from individual's credit files and also from the credit bureau reports. Statistical Scoring Model can also be described in terms of a scorecard, a pooled scorecard, and a custom scorecard. A scorecard applies data from one firm, whereas a pooled scorecard applies data from more than one firms and a custom scorecard mixes the data acquired from both the statistical model and judgmental model.

A Laundry list of Microfinance Client Characteristics

Demographics: gender, year of birth, marital status, highest education Household information: number of people in household Household Assets: homeowner status, years at a residence, number of rooms, vehicles

owned Business Demographics: sector, type of business, years in business, number of employees Financial Flows: business revenue, household income, rent payment Balance Sheet: total assets, total liability(formal debt, informal debt),total equity Repayment History: number of loans, longest spell in arrears, days in arrears per

installment Credit Bureau Information: bureau score, presence on “Blacklist” Quantified Subjective Judgments: managerial skills, cash-flow variability, business

prospects Loan Characteristics: amount requested, type of loan, borrower’s contribution to

financing

(Source: Schreiner, 2003)

The approximate makeup of the FICO score used by U.S. lenders

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Credit scores are designed to measure the risk of default by taking into account various factors in a person's financial history. Although the exact formulas for calculating credit scores are closely-guarded secrets, FICO has disclosed the following components and the approximate weighted contribution of each:

* 35% — Payment History – Late payments on bills, such as a mortgage, credit card or automobile loan, can cause a consumer’s FICO score to drop. Paying bills as agreed over time will improve a consumer’s FICO score.

* 30% — Credit Utilization – The ratio of current revolving debt (such as credit card balances) to the total available revolving credit (credit limits). Consumers can improve their FICO scores by paying off debt and lowering their utilization ratio. Alternatively, applying for and receiving the credit limit increase will also drive down the utilization ratio. Closing of existing revolving accounts will typically adversely affect this ratio and therefore have a negative impact on their FICO score.

* 15% — Length of Credit History – As consumer’s credit history ages, assuming they pay their bills, it can have a positive impact on their FICO score.

* 10% — Types of Credit Used (installment, revolving, consumer finance, mortgage) – Consumers can benefit by having a history of managing different types of credit.

* 10% — recent search for credit and/or amount of credit obtained recently – Multiple credit inquiries for a consumer seeking to open new credit, such as credit cards, retail store accounts, and personal loans, can hurt an individual’s score. Applying for lots of new credit in a short period of time is also viewed as risky and can cause a drop in an individual’s score. However, individuals shopping for a mortgage or auto loan over a short period will likely not experience a decrease in their scores as a result of the types of inquiries.

Credit scores are not the sole underwriting factor used by lenders. Lenders use their own internal scoring models as well as other loss mitigation tools and data to gauge an individual's creditworthiness.[16] For instance, current income and employment history, which are not part of a score, are weighed when applying for credit, along with tenancy status (rent or own) in some cases. An unemployed individual with no sources of income will not usually be approved for a home mortgage, regardless of his or her FICO scores.

There are other special factors which can weigh on the FICO score.

* Any money owed because of a court judgment, tax lien, or similar carry an additional negative penalty, especially when recent.

* Having one or more consumer finance credit accounts may also carry a negative weight (critics say that this causes a vicious cycle, locking people into continuing to use consumer finance companies).

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* The number of recent credit checks for consumers seeking new credit, such as credit cards or retail store cards, can have a negative impact on the FICO score. However, for consumers shopping for a home or a car, credit inquiries generated for those activities are grouped together as one credit inquiry, and therefore, these inquiries do not impact a consumer’s score.

* While all credit inquiries are recorded and displayed on your credit report for a period of time, credit inquiries that were made yourself (to check your credit), by your employer (for employee verification) or by companies initiating prescreened offers of credit or insurance do not have any impact on your credit score.

(Source: FICO, www.fico.com)

Credit scoring uses quantitative measures of the performance and characteristics of past loans to predict the future performance of loans with similar characteristics. For lenders in rich countries in the past decade, scoring has been one of the most important sources of increased efficiency. Lenders in rich countries, however, score potential borrowers based on comprehensive credit histories from credit bureau and on the experience and salary of the borrower in formal wage employment. Most microfinance lenders, however, do not have access to credit bureau, and most of their borrowers are poor and self-employed. The two chief innovations of microfinance—loans to groups whose members use their social capital to screen out bad risks and loans to individuals whose loan officers get to know them well enough to screen out bad risks—rely fundamentally on qualitative information kept in the heads of people. Scoring, in contrast, relies fundamentally on quantitative information kept in the computers of a lender. Can microfinance lenders use scoring to cut the costs of arrears and of loan evaluations so as to improve efficiency and thus both outreach and profitability? Experiments in Bolivia and Colombia (Schreiner 2000, 1999a, 1999b) suggest that scoring for microfinance can indeed improve the judgment of risk and thus cut costs. For example, scoring may save a Colombian microfinance lender about $75,000 per year (Schreiner, 2000). In present value terms, this approaches $1 million. Scoring is probably the next important technological innovation in microfinance, but scoring will not replace loan groups or loan officers, and it will never be as effective as it is in rich countries because much of the risk of microloans is unrelated to characteristics that can be quantified inexpensively. Still, scoring can still be useful in microfinance because some risk is related to characteristics that are inexpensive to quantify, and current microfinance technologies do not seem to take advantage of this as much as they could. This paper describes how scoring works, what it can and cannot do, and how microfinance lenders should prepare themselves to implement it. Other good, general introductions to scoring are Mays (1998), Hand and Henley (1997), Mester (1997), Viganò (1993), and Lewis (1990).

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Chapter - 4Findings and Discussion

4.1 List of documents required before the loan is provided Application personal detail Social &Economic detail Photo ID for main applicant Residence proof House ownership document Bank/Informal saving passbook Guarantor photo ID Security checks Telephone Bill/ Electricity Bill LPG Connection Bill

4.2 Proposed Loan Process

(Flow Chart 3: Proposed Loan Process)

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Sales Officer identifies

potential client through field

visits and references.

Sales Officer fills loan

application and sends to Verification officer (VO)

VO visits the field for

appraisal & forwards

application to Credit

Executive (CE)

CE applies Credit Scoring Tool (CST) and forwards to

Business Head

After BH approves MIS enters data, Back office

sends demand to Finance.

Finance sends the cheque to the branch for disbursement.

Disapproves

4.3 Financial analysis:It is the duty of the credit executive to generate grades out of the credit tool and once the form is filled by the field staff, the credit executive is the one who verifies the validation of the information provided in the form and only after his approval, the form is passed onto the senior authority for actual sanction of the loan. The tool itself generates the rating of the person.

Effects on the profits:

Rejecting “High Risk” case means:

Avoiding some “Bad” (Benefit)

Losing some “Good” (Cost)

Cost of giving loan to 1 “Bad” client worth losing profits of 5 “Good” clients.

For e.g.: Take a case

Suppose the company give loan to a “good” client = Rs.30,000@20% for 1 year

Then the company will be profited by Rs.6,000, that is the “good” client will repay Rs.36,000.

Now, suppose the company give loan to a “bad” client = Rs.30,000

Then there is no chance to recover the disbursed loan from the “bad” client.

That is the company will bear the loss of 5 “good” clients (5*Rs.6,000 = Rs.30,000)

“Hence, the benefit of a credit tool can be easily seen”.

This tool will generate a grade depending upon savings, family details, income and expense, credit worthiness, business information, housing details, repayment history and reference check, which would help to evaluate the client’s creditworthiness and minimize the risk and loss arising from the loan. Prior to the use of this tool the organization was using the Micro Enterprise Loan (MEL) tool which didn’t give specific details of the clients business and housing details in particular it was more of tool for the manufacturing units and failed to cater the other units. Hence the tool is a more specific tool for the organization and the organization can now clearly identify the not so good customers. The tool also suggests the amount of loan recommended for the client depending upon the credit rating received after entering the details in the tool. The low rating received of the tool means that the client has a low repayment capacity as per the various parameters; it is basically predicting loan-default chances or estimating the future repayments.

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4.4 Technical Analysis:The model is technically sound as after preparation of the model, the model was verified by entering data of the existing clients and the grades achieved was either A or B, which meant low risk and regular.

Flow Chart 4: Mathematical model used for the tool

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Give weightage to parameters and sub-parameters

Give ratings for sub-parameters through 1 to 5(1 is for worst and

5 is for best)

Enter values (from a list of options for sub-parameters)

Get the achieved score by some formulae related to the entered

values

Get the total score out of 100

If the total score is >80

Grade A (Low Risk)

If the 60<total score<80

Grade B (Regular)

If the 40<total score<60

Grade C (Risky)

If the total score is <40

Grade D (High Risk)

Parameters and Sub-parameters

Chapter- 5Case Study

5.1 Personal DetailsName :KusnumaYear of Birth: 1982 (28 years)Education: IlliterateMarital Status: Married Husband name :Sharafat

LocationAddress: Noor BastiBranch: Beribag, Saharanpur

Business DetailsType of Business: Tailoring and Tea ShopYear of start of business: 3 years backPeak season: Throughout yearLean season: NoIncome from business per month: Rs. 3000 from Tailoring and Rs. 6000 from Tea shopTransaction mode: CashProperty of Business: OwnedAssets owned: 1) Sewing machine (1)Book of accounts: Not maintainedMargin in business: 50%Saving: 25%

Present Credit HistoryCredit institution: MIMO FinanceAmount: Rs. 12,000 Purpose: Expansion of businessRepayment up to present: no installment paid(new loan)

Previous Credit HistoryCredit institution: MIMO FinanceAmount: Rs. 6,000 (JLG loan)Purpose: Expansion of businessRepayment up to present: Paid

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Credit Worthiness:References/ Guarantors: AvailableBank Account: Not Available

Loan Requirement:Purpose of loan: Business expansion (more inventories)Loan amount: Rs. 20,000 - Rs. 30,000Preferred frequency of installment: MonthlyLoan tenure preferred: 18 months

STORY

Kusnuma is engaged in Tailoring & her husband is engaged in “Tea shop” since 3 years. Earlier her business was not growing. But after getting JLG loan from MIMO Finance, her business started growing at a faster rate. She has got loan from MIMO Finance twice & invested in the business. Kusnuma and her husband’s business run throughout the year. She has one sewing machines & her husband has a tea shop as assets of their business. Her husband was working as a worker in another tea shop. Previously she had taken loan for buying of sewing machine and now she has taken loan to start their own tea shop. She gets Rs.50/sewing of one salwar suit and she normally stitches two to three suits per day. So, she earns Rs.100 – Rs.150 per day and her husband is getting Rs.200 from the tea shop. They have a joint family and don’t have a self owned house. There are times wh en she is out of work due to no order but these type of days are very few. Her business incurs 50% margin & 25% saving. As she has earlier good relationship with MIMO Finance, so she is seeking for micro-enterprise loan of Rs. 30,000 from MIMO to expand the business. She would have liked more money and believes “more money, more business, & then more customers”. She trusts MIMO and thinks women who create businesses with the loan get more respect and can manage more “low times”. In future she plans to take out micro-enterprise loan to expand her business & to keep pace with time.

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5.2 Personal DetailsName: NushratYear of Birth: 1963 (47 years)Education: 10th passedMarital Status: Married Husband name: Md. AliChildren: 2 (1 son & 1 daughter)

LocationAddress: NoorbastiBranch: Beribag, Saharanpur

Business detailsType of Business: DairyYear of start of business: 2005Peak season: Throughout the yearLean season: NoIncome from business per month: Rs. 30,000 – Rs. 35,000Transaction mode: Cash Property of Business: OwnedAssets owned: Two cows and one buffaloBook of accounts: Not maintainedMargin in business: 20 -25%Saving: 30%

Present Credit HistoryCredit institution: MIMO FinanceAmount: Rs. 12,000 Purpose: Expansion of businessRepayment up to present: no installment paid

Previous Credit HistoryCredit institution: MIMO FinanceAmount: Rs. 6,000 (JLG loan) Purpose: Expansion of businessRepayment up to present: Paid

STORY

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Nushrat at Saharanpur represents determination. She is standing on her own feet. She was brought up in family of 3 brothers & 2 sisters. Later she got married. Her husband was engaged in a paper mill factory. After sometimes, she gave birth to a boy baby and two year later she gave birth to a girl baby. Their family was running smoothly. One day while coming from the factory her husband met with an accident and lost his right leg. As he was the only earning member of her family, it was difficult to survive as her husband was unable to work. Then she came to know about Mimo and took a loan of Rs.6000 to buy a cow. Then she started the milk business by selling the milk produced from the cow. As she told the first month of the business was so difficult to maintain, but after few months she was able to maintain the business properly. The business was running successfully. She sold the milk by Rs.25 to Rs.30 per liter and the cow gave 8 to 9 liters of milk per day in two shifts. In this way her monthly income became Rs.5000 to Rs.6000. After two months, she was able to buy another cow and continued her business successfully. Her business keeps on growing. MIMO came to know her determination towards her business & gave her a loan of Rs. 12,000. Now with the help of money, she bought another buffalo and now her income increased by 3 times. Now she sells the milk to wholesaler. Now her business is growing at a much faster pace. She is now economically independent and fulfilling her dreams.

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Chapter – 6Conclusion & Recommendations

6.1 ConclusionAfter completing the credit scoring tool for the MESO department for the Individual Business

Loan and Individual Home Loan it would be more convenient and easy for the credit executive

in particular to access the repayment capacity of the applicant and also as a part of the tool it has

been suggested the amount of loan to be given as per the applicants marks received. This would

save time on the part of the officer to estimate his/her (applicant) credit worthiness and also

increase the rate the loan could be disbursed. After the tool this would reduce the personal

biasness and every client would be treated on the same platform and also the client could get the

loan as per his/her credit worth.

6.2 Recommendations for loan amount

The loan amount should be given as per the score received in the tool for future when the loan amount increases.

6.2.1 Recommendation for the policy Loans should be given even to unmarried people (whose business is running sound) A small loan (say Rs. 10000/-) should be given to people with sound business plan and

appears to have a sound repayment history. Reduce the number of formalities required to get the loan and also a guarantor as it gets

very difficult for the client to produce them.

6.3 Important guidelines Please keep the original copy of the tool safely as damage to it in any form means loss of

the entire tool Data in reference sheet should not be altered during filling up the tool

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Annexure - 1: Questionnaire

For business loanApplicant Name: __________________

Borrower Monthly Gross Income in 2009 Borrower Occupation Cash flow (detailed income & detailed expenditure) Pre and post position of the family and business after the loan Adult person income generation (no minor income to be taken individually it is to be

clubbed in the family income) Bank account details Income of the family No of Business transactions per day(in Rupees) Permanent address of business List of creditors n debtors(above Rs.1000/-)

Property Information

Type of Property(owned or rented) Condition of the property Assets present value (house and the other assets) How many years have you been staying in this house

Family details

Family size Earning members Age of the working member Dependents Adults in the family Physical health of the members of the family

Expenses

Groceries Monthly Expense Utilities Monthly Expenses Cell Phone Monthly Expense Electricity expenses MISC. Expenses

Savings

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Monthly savings Repayment weightage based on the savings Duration of saving?

OTHERS

Any other loan taken from memo? Any default in repayment? Personal behavior (to be accessed)? No. of adults into permanent job? Do you maintain books of accounts? What is the loan amount that you require?

Business details

No. of machines (in case of hosiery) Business experience Business cycle (inventory) Bank accounts (number) Does your business primarily serve local customers, local businesses, state-owned

enterprises or foreign markets? Are any of the owners of this business a woman? Compared to last year, has the number of employees in your firm increased, decreased or

stayed the same? Compared to last year, has your company earnings increased, decreased or stayed the

same? What is the primary payment method you use in purchasing goods and services for your

business? Do you have a landline and electricity connection at business?

For Home loan Applicant Name: __________________

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Borrower Monthly Gross Income in 2009 Borrower Occupation Cash flow (detailed income & detailed expenditure) Pre and post position of the family and business after the loan Adult person income generation (no minor income to be taken individually it is to be

clubbed in the family income) Bank account details Income of the family No of Business transactions per day(in Rupees) Permanent address of business List of creditors n debtors(above Rs.1000/-)

Property Information

Type of Property Condition of the property Property Occupied by Assets present value (house and the other assets) How many years have you been staying in this house?

Family details

Family size Earning members Age of the working member Dependents Adults in the family Physical health of the members of the family

Expenses

Groceries Monthly Expense Utilities Monthly Expenses Cell Phone Monthly Expense Electricity expenses MISC. Expenses

Savings

Monthly savings

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Repayment weightage based on the savings Duration of saving? Do you maintain books of accounts?

OTHERS

Any other loan taken from MIMO? Any default in repayment? Personal behavior (to be accessed)? Bank accounts?(detail) No. of adults into permanent job

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Annexure – 2: Document Checklist cum Sequence of DocumentsTable 2 :Checklist cum Sequence of Documents

Product (BL / MEL / DL /HL)Name of App & Co-App

Pre Appraisal DocumentsSr. No. Document Yes / No

1 Document Check list 2 Application Form (App. & Co-App. with Joint Photos) 3 Socio Economic Details 4 Income Expense Sheet 5 Customer Authorization Letter 6 Vernacular Form 7 Vendor verification Form (In Case of BL / MEL) 8 ID Proof (Applicant) 9 ID Proof (Co-Applicant)

10 Residence Proof (App/ Co-App.) 11 House Ownership Proof 12 DPN (Signed & Stamped) 13 Security Cheques 4 14 Bank Statement/ Passbook (App or Co-App) 15 Previous Track record (If any) 16 Guarantor Information Form (With Photograph) 17 ID proof (Guarantor) 18 Residence Proof (Guarantor) 19 House Ownership Proof (Guarantor) 20 Road MAP

Signature of Sales Officer

Post Approval Documents1 Appraisal Sheet 2 PD Sheet (In Case of BL / MEL / DL) 3 Scoring Tool 4 Photographs of Business / Setup/ HL 5 Approval Mails

Signature of Appraisal Officer

Post Disbursal Documents1 Agreement Set (App & Co-App) 2 Agreement Set (Guarantor) 3 Insurance & PF Receipt 4 Loan Disbursement Sheet 5 Loan Utilization Report

Signature of MIS Assistant

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Annexure – 3(Parameters and Sub parameters for Home Improvement Loan)

Table 3: Parameters and Sub parameters for Home Improvement Loan

Parameters SubparametersFamily details Family size DependentsOccupation detail Occupation Stability in business/service No. of adult earning members Electricity Area of house No. of Rooms Present Condition of home Personal contribution of client House Registration Loan for type of improvement Ownership of house Stability in current house Availability of toilet Market Value of the house Type of houseRepayment History No. of loans repaid Timely repayment current liability(EMI) no. of installment delay no of cheques bounced

Reference checksTwo neighbors opinion about the client(in terms of financial position)

Behavior/attitude

Others Present market value of all other assets Bank account Monthly saving

Annexure – 4(Parameters and Sub parameters for Home Rental Loan)

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Table 4: Parameters and Sub parameters for Home Rental Loan

Parameters SubparametersFamily details Family size DependentsOccupation detail Occupation Stability in business/service No. of adult earning membersHousing Details Electricity Area of the house No. of Rooms Personal contribution of client Previously rented out rooms Availability of tenants House Registration Ownership of house Stability in current house Rent(to be received after the loan) Accessibility to market Type of house Market value of house

Repayment History No. of loans repaid Timely repayment current liability(EMI) No. of installments delayed No. of cheques bouncedReference checks Neighbors opinion about the client Behavior/attitudeOthers Present market value of all other assets Type of Bank account Monthly saving

Annexure – 5(Parameters and Sub parameters for Business Loan)

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Table 5: Parameters and Sub parameters for Business Loan

Parameters SubparametersFAMILY DETAILS Family size Number of adult earning members DependentsBUSINESS INFORMATION Number of years in a current business Present market value of the shop Peak season Value of the assets in the shop Number of transactions per day(in Rs.) Purchasing pattern Sales pattern Number of employees compared to last year Business profit margin(Net) No. of suppliers No. of buyers No. of present employees Ownership Type of business Business registration Monthly turnover Average monthly profit Average stock Locality of business Arrangement of shop If home and shop are same Do you maintain books of accounts Secondary Business Shop InsuranceRepayment History No. of loans repaid Timely repayment current liability(EMI) no. of installment delay no of cheques bouncedReference checks Neighbors opinion about the client Vendors opinion about the client Behavior/attitudeOthers Present market value of all other assets Bank account Monthly saving

Annexure – 6 : Score card of Home Improvement Loan

Table 6: Score card of Home Improvement Loan

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ParametersMax score

Achieved score Sub parameter

Max score

Score Achieved

Family details 3 0.8 Family size 2 0.4 Dependents 1 0.4Occupation detail 10 6.8 Occupation 4 3.2 Stability in business/service 3 0.6 No. of adult earning members 3 3Housing Details 45 36.2 Electricity 3 3 Area of house 5 3 No. of Rooms 3 1.8 Present Condition of home 4 2.4 Personal contribution of client 6 3.6 House Registration 2 0.4 Loan for type of improvement 4 4 Ownership of house 3 3 Stability in current house 5 5 Availability of toilet 3 3 Market Value of the house 4 4 Type of house 3 3Repayment History 24 24 No. of loans repaid 7 7 Timely repayment 4 4 current liability(EMI) 4 4 no. of installment delay 5 5 no of cheques bounced 4 4Reference checks 6 6

Neighbors opinion about the client 3 3

Behavior/attitude 3 3

Others 12 12Present market value of all other assets 3 3

Bank account 2 2 Monthly saving 7 7

Total Score 100 85.8 Low risk,Reward,recommend amount of loan Rs.30,000 Grade A

Annexure – 7: Score card of Home Rental Loan

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Table 7: Score card of Home Rental Loan

Parameters Max scoreAchieved score Sub parameter

Max score

Score Achieved

Family details 3 1 Family size 2 0.4 Dependents 1 0.6Occupation detail 9 4.2 Occupation 3 1.2 Stability in business/service 3 1.2 No. of adult earning members 3 1.8Housing Details 47 29.4 Electricity 2 2 Area of the house 4 1.6 No. of Rooms 3 1.2 Personal contribution of client 5 2 Previously rented out rooms 4 4 Availability of tenants 5 5 House Registration 2 0.4 Ownership of house 3 1.8 Stability in current house 4 0.8 Rent(to be received after the loan) 4 1.6 Accessibility to market 4 4 Type of house 3 1.8 Market value of house 4 3.2Repayment History 23 4.6 No. of loans repaid 6 1.2 Timely repayment 4 0.8 current liability(EMI) 4 0.8 No. of installments delayed 5 1 No. of cheques bounced 4 0.8Reference checks 6 4.2 Neighbors opinion about the client 3 2.4 Behavior/attitude 3 1.8

Others 12 7.8 Present market value of all other assets 3 2.4 Type of Bank account 3 1.8 Monthly saving 6 3.6Total Score 100 51.2

Risky,No Loan Grade C

Annexure – 8: Score card of Business Loan

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Table 8: Score card of Business Loan

ParametersMax. score

Achieved score Sub parameter

Max. score

Score Achieved

FAMILY DETAILS 5 2 Family size 2 0.4 Number of adult earning members 2 1.2 Dependents 1 0.4BUSINESS INFORMATION 57 35.4 Number of years in a current business 4 4 Present market value of the shop 2 2 Peak season 3 0.6 Value of the assets in the shop 4 1.6 Number of transactions per day (in Rs.) 3 0.6 Purchasing pattern 2 1.6 Sales pattern 2 0.4

Number of employees compared to last year 1 0.4

Business profit margin(Net) 4 2.4 No. of suppliers 2 0.8 No. of buyers 2 0.8 No. of present employees 1 0.2 Ownership 2 2 Type of business 1 1 Business registration 1 1 Monthly turnover 4 4 Average monthly profit 4 4 Average stock 3 1.2 Locality of business 3 1.8 Arrangement of shop 2 0.8 If home and shop are same 2 1.2 Do you maintain books of accounts 2 0.4 Secondary Business 2 2 Shop Insurance 1 0.6Repayment History 18 7.4 No. of loans repaid 4 0.8 Timely repayment 3 3 current liability(EMI) 3 1.2 no. of installment delay 4 0.8 no of cheques bounced 4 1.6Reference checks 8 4.4 Neighbors opinion about the client 2 0.8 Vendors opinion about the client 4 3.2 Behavior/attitude 2 0.4Others 12 8 Present market value of all other assets 3 0.6 Bank account 2 0.4 Monthly saving 7 7Total Score 100 57.2

Risky, No loan Grade C

Annexure 9: Tool test ResultsFigure 3: Tool Testing Results

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Reference: Google.com Wikipedia

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Investopedia.com file:///C:/Documents%20and%20Settings/User/Desktop/junk/rest/The%20Importance%20of

%20Credit%20Reports%20and%20Credit%20Scores.htm CRISIL SME connect Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J. and

Vanthienen, J. (2003). Benchmarking state-of-the-art classi_cation algorithms for credit scoring.Journal of the Operational Research Society

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