objective

THE ROLE OF A MICROFINANCE BUREAURegional Conference on Credit Reporting Systems in Africa


LILIAN SIMBAQUEBA.

LISIM GROUP PRESIDENT.

By : Lilian Simbaqueba G. of 29October 2006


INDEX

OBJECTIVE........................................................................................................................3ARE THE MICROFINANCES A PROFITABLE MARKET?..........................................3

Indicators of Microfinance Institutions in The World.....................................................3Profitability in the Microfinance Market.....................................................................5Microfinance Institutions in Africa (General Description).........................................5Africa vs. Latin America.............................................................................................6

THE ADDED VALUE OF A BUREAU IN THE KNOWLEDGE OF RISK..................10Expansion of the Access To Financial Services............................................................11Applications of a Credit Bureau - The Life Cycle of Customers..................................11

WHY A MICROFINANCE BUREAU?...........................................................................13WHAT CHARACTERISTICS SHOULD A MICROFINANCE BUREAU HAVE?......14MICROFINANCE BUREAU APPLICATIONS..............................................................15CONCLUSIONS...............................................................................................................26



OBJECTIVE

To show the necessity and importance that the microfinance market has in the development of Bureaus that include their own population; understanding their specific characteristics and in consequence if they are able to give added value to the risk analysis.

ARE THE MICROFINANCES A PROFITABLE MARKET?

A strong tendency has taken place specially in the last years for the Microfinance formalization. On one hand, many NGOs have been turning into banks or into regulated MFIs; on the other hand, many banks have found incentives to enter in this interesting and growing market, many times motivated for different governments. Facing this tendency, there exist two new challenges: the superintendences are facing an unknown market that must be regulated and the investors see an interesting potential, but a doubtful profitability, especially under the current regulation parameters.

Far beyond the social well-being generated and the importance given to Microfinance by many governments, we show enough indexes that ensures the profitability for many actors in this market.

This document presents a brief description of the presentation content for the Regional Conference on Credit Reporting Systems in South Africa.

Indicators of Microfinance Institutions in The World1

CA CENTRAL AMERICA LAC No CA SOUTH AMERICAMENA MIDDLE EAST AND NORTH AFRICA ECA EAST EUROPE AND CENTRAL AFRICAAsia ASIAÁfrica AFRICA

1 Source: The Mixmarket www.themix.org . Benckmarking of the Microfinance in Central America 2004.



Equity/Assets (%)Total Adjusted Equity / Total Adjusted Assets

42.4 20.7 72.4 56 27.9 33.5

Commercial Dues Ratio (%)

Total Liabilities at "Market Price"/ Gross Portfolio of Loans

48.5 79.5 8 14.6 62.7 54.6

Debt / Capital Ratio (%)Total Adjusted Liabilities / Total Adjusted Equity

1.4 3.7 0.4 0.8 2.1 2

Gross Portfolio / Total Assets (%)

Adjusted Gross Loan Portfolio / Adjusted Total Assets

83.3 81.4 71.1 87.4 73.9 69

Asia ÁfricaFINANCIAL STRUCTURE Definitions CALAC No

CAMENA ECA

The financial structure shows that Middle East and North Africa has higher portion of Equity respect the total Assets followed by Africa. Latin America has a bigger Dues Ratio followed by Africa, the smaller is the Middle East and North Africa Ratio. The more debt leveraged continent is Latin America, and the one with a higher level of own resources is Middle East and North Africa.

ROA (%)Adjusted Operative Revenue After Taxes / Average of the Total Adjusted Assets

1.4 3.4 2.4 0.9 1.4 -1.1

ROE (%)Adjusted Operative Revenue After Taxes / Average of the Total Adjusted Equity

3.3 16.2 3.4 3.2 7.6 -3.9

Operational Self-sufficiency (%)

Financial Incomes / Financial Expenses + Provision for Uncollectible + Operative Expenses

117.8 118.6 128 128 116 111

Financial Self-sufficiency (%)

Financial Incomes / Financial Expenses + Net Provision for Uncollectible + Adjusted

104.9 115.1 112 108 110 98

PROFITABILITY AND SUSTAINABILITY

Definitions CALAC No

CAMENA ECA Asia África

The more profitability continent in accordance with the Microfinance market is Latin America (may be owed to its leverage level) against Africa with the lower profitability rate. In a financial way, the more self-sufficient region is Latin America followed by Middle East and North America.

Portfolio at Risk > 30 Days (%)

Balance of Delayed Loans > 30 Days / Adjusted Gross Portfolio

5.7 2.4 0.5 1.1 3.2 3.4

Portfolio at Risk > 90 Days (%)

Balance of Delayed Loans > 90 Days / Adjusted Gross Portfolio

2 1.3 0.2 0.3 1.4 1.2

Ratio of Written-off Portfolio (%)

Adjusted Value of Written-off Loan / Average of the Gross Portfolio

1.9 2.7 0.4 0.8 0.9 1.5

Risk Covering Ratio (%)Adjusted Reserve for Uncollectible Loans / Portfolio at Risk > 30 Days

0.6 1.3 0.8 1.3 0.8 0.7

Non Productive Liquid Assets / Total Assets (%)

Adjusted Cash and Banks / Adjusted Total Assets

7 7.3 5.5 4.1 9.7 12.8

ECA Asia ÁfricaSOLVENCY AND RISK Definitions CALAC No

CAMENA

The more risky region is Central America while the less risky is Meddle East and North Africa, is important to notice that the higher level of Written-off portfolio belongs to Latin America, in accordance with this, the higher level of provision also belongs to Latin America (beside Europe and Central Africa).



Profitability in the Microfinance Market2

Microfinance Institutions in Africa (General Description)

This chart shows a comparison between banks and IMFs (as regulated as not) where is clear that IMFs has higher profitability index with even lower provisions levels and overdue portfolio.

Microfinance Institutions in Africa (General Description)3

In the present apart the characteristics of Microfinance market in Africa are described in order to see the actual state of this segment.

MFI Type Central Africa East Africa Indian Ocean Southern Africa West Africa TotalCooperative 10 4 8 2 32 56Regulated 3 23 1 18 26 71Unregulated 5 15 0 8 8 36Total 18 42 9 28 66 163

West Africa has the higher amount of IMFs, and the Regulated IMF is the more common institution that offers microfinancial services in whole Africa. (Anyway has to be considered the demographic concentration for each region)

VOLUME Central Africa East Africa Indian Ocean Southern Africa West Africa Total

Number of MFIs 18 42 9 28 66 163Total Assets (USD) 45,607,461 484,563,870 36,795,203 255,998,021 489,621,707 1,312,586,262GLP (USD) 24,462,485 191,356,028 17,632,778 211,199,064 297,958,426 742,608,781

Total Savings (USD) 25,052,031 281,177,765 15,209,429 127,440,634 264,331,390 713,211,249

OUTREACHNumber of Borrowers

60,226 1,090,558 37,664 494,463 730,066 2,412,977

Number of Savers 116,939 3,314,651 146,819 578,785 2,166,401 6,323,595Total Population* 85,333,661 162,865,905 17,501,871 97,740,653 226,084,020 589,526,110Total Assets (USD) 0.53 2.98 2.10 2.62 2.17 2.23GLP (USD) 0.29 1.17 1.01 2.16 1.32 1.26

Total Savings (USD) 0.29 1.73 0.87 1.30 1.17 1.21

OUTREACHNumber of Borrowers

0.071% 0.670% 0.215% 0.506% 0.323% 0.409%

Number of Savers 0.137% 2.035% 0.839% 0.592% 0.958% 1.073%

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2 Source: Interamerican Bank for Development, Microfinance in Latin America. 2006.3 Source: The Mixmarket, Anne-Lucie Lafourcade, Jennifer Isern, Patricia Mwangi, and Matthew Brown. Overview of the Outreach and Financial Performance of Microfinance Institutions in Africa. 2005


ROA % OVERDUE PORTFOLIO % PROVISIONSBANKS 1.53% 7.40% 10.50%MICROFINANCE INST. REG 1.96% 6.47% 6.61%MICROFINANCE INST. NO REG 4.04% 6.38% 4.84%

ROA % OVERDUE PORTFOLIO % PROVISIONSBANKS 1.53% 7.40% 10.50%MICROFINANCE INST. REG 1.96% 6.47% 6.61%MICROFINANCE INST. NO REG 4.04% 6.38% 4.84%


West Africa has a major amount of Assets nearly followed by East Africa and also has the higher level of savings. Against this, the lower level of assets is presented by Central Africa, the same region with the lower level of savings.

Africa vs. Latin America

Comparing some characteristics of Latin America and Africa Microfinance market is concluded:

In Latin America we find more productivity (borrowers/loan officer) than in Africa.

In Latin America the portfolio at risk > 30 days is lower than in Africa and the profitability is showed as higher.

In Africa we find less cross selling experiences but more support on savings, as follows:



DIFFERENCES BETWEEN MICROFINANCES, CONSUME AND COMMERCIAL MARKETS

The successful experiences of MFIs have a common topic: understanding that the microfinance market is more than a hybrid of a personal loans market and a commercial (large enterprises) market, with regard to:

Characteristics of the population Processes. Required information and evaluation. Type of required products. The confluence of long run relationships with the profitability of

the business. The importance of the productivity indicators.

Different Processes

The Following chart presents the own characteristics and processes of the Microfinance market different in the personal loans market.

MICROFINANCE CHARACTERISTICS AND PROCESSESHigh credit Rotations Short term loans

Payment dates according to the business cycle of the microentrepreneur Credit Analysts are both assessors and evaluators Credit officers are the heart of the relationship with the client: continuity, surveillance, trust, training, motivation Close relation with the client and follow up visits Zones



The next chart (for contrasting with the previous) presents the own characteristics and processes of the Personal loans market, different in the Microfinance market.

PERSONAL LOANS CHARACTERISTICS AND PROCESSESCredit and information technologies Long and Medium length termsAnalysts are Verifiers rather than advisorsNo Visits Payment dates according to income flow of the employed populationShort Response Times

Common characteristics and processes.

This processes are commonly used by both markets, as Microfinance as Personal loans.

COMMON CHARACTERISTICS AND PROCESSES

SEGMENTATION METHODS Risk Management Risk Pricing Product characteristics according to Risk profile Cost reductionCross Selling – Up SellingCredit Bureau enquiries



Differences in the type of analyzed variables among Micros, Personal loans and SMEs.


TYPES OF VARIABLES PERSONAL LOANS MICROS SME’S Demographics of the Applicant Yes Yes No(Age, Marital Status , Sex, Profession, residential status,Education status, employment and micro enterprise history)Related to client (Credit Behavior y Loan Portfolio) Yes Yes Yes( No. and types of Previous Credits, Historical Arrears,)Related to Micro Enterprise: No Yes Yes(Business Type, size, history, profits destiny, business property)Related to client and family’s financial statements No Yes No( payment Capacity, other family incomes and expenses, etc)Related to micro enterprise financial statements: No Yes Yes( Profit Margin, net profit, assets, liabilities, expenses, equity, inventory turnover, etc)Related to credit Yes Yes Yes(Region, terms, amount, credit destiny)Applicant status/customer in other Institutions Yes Yes Yes( Information of Credit Bureaus)

TYPES OF VARIABLES PERSONAL LOANS MICROS SME’S Demographics of the Applicant Yes Yes No(Age, Marital Status , Sex, Profession, residential status,Education status, employment and micro enterprise history)Related to client (Credit Behavior y Loan Portfolio) Yes Yes Yes( No. and types of Previous Credits, Historical Arrears,)Related to Micro Enterprise: No Yes Yes(Business Type, size, history, profits destiny, business property)Related to client and family’s financial statements No Yes No( payment Capacity, other family incomes and expenses, etc)Related to micro enterprise financial statements: No Yes Yes( Profit Margin, net profit, assets, liabilities, expenses, equity, inventory turnover, etc)Related to credit Yes Yes Yes(Region, terms, amount, credit destiny)Applicant status/customer in other Institutions Yes Yes Yes( Information of Credit Bureaus)


THE ADDED VALUE OF A BUREAU IN THE KNOWLEDGE OF RISK

Allows the financial and non-financial market the control and management of the credit risk.

Growth and expansion in the coverage of the financial services, specially in microfinance markets and populations with no financial access.

Promotion of good payment habits (on-time payment culture), since it gives motivation to credit users to make their payments on time.

The institution that uses a Bureau Score Report acquires a new capacity of analysis and the credit decision is made faster and better, since it enables a greater and better verification of data and analysis with a projection of payment behavior (Bureau Score).

Sharing information facilitates decision making and guarantees profitability in the global credit system.

Knowing the credit history of all institutions ( financial and non-financial) to avoid over-debt by customers.

Reject clients who deliberately apply for credit in many institutions and end up in delinquency or bad debt.



Expansion of the Access To Financial Services

Improved risk control tools, such as credit scoring, enable lenders to reduce the risk of their portfolios, while they also provide opportunities to expand lending and access to finance. As an exercise, we simulated the potential for credit expansion and risk reduction taking the customers who had loans from the commercial, or retail stores in the sample. The default rate for this group in the sample was 46.17%. By rejecting the worst customers and approving, instead, those customers with the best scores, they could increase the number of good customers by 58%, while reducing the default rate from 46.17% to 26.39%.

35,965 53,164

46.17%

26.39%

-

10,000

20,000

30,000

40,000

50,000

60,000

Present Effect

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

ClientsRisk

28.868

11.66911.669

Applications of a Credit Bureau - The Life Cycle of Customers

The following picture shows the Life Cycle of the Micro-entrepreneur beginning with the Approving stage (Start) and ending with the Cross-sell (Acquisition of new products)



Start

Fraud Detection Collection

strategies

Provision Calculation

Maintenance and retention of loyal

customers

AcquisitionOf new products

Start

Fraud Detection Collection

strategies

Provision Calculation

Maintenance and retention of loyal

customers

AcquisitionOf new products

Each stage of the Customer Cycle is explained in the following graphs.

Start and Fraud detection

• Reinforcement of approval or non-approval decisions based on historical payment behavior of the customer with other institutions.

• Additional information about the client’s consolidated debt.

• Forecast of payment behavior for approval or rejection as well as approval under specific credit conditions according to the risk level through Bureau Scoring.

• Detection and rejection of customers who have several credits in different institutions without payment.

Start and Fraud detection

• Reinforcement of approval or non-approval decisions based on historical payment behavior of the customer with other institutions.

• Additional information about the client’s consolidated debt.

• Forecast of payment behavior for approval or rejection as well as approval under specific credit conditions according to the risk level through Bureau Scoring.

• Detection and rejection of customers who have several credits in different institutions without payment.



• Bureau inquiries enables the development of preventive collection strategies by detecting changes on payment behavior or delinquency with other institutions.

• Payment behavior in Bureaus can be included in collection Scorecards, if obtained periodically.

Collection strategies

• Bureau inquiries enables the development of preventive collection strategies by detecting changes on payment behavior or delinquency with other institutions.

• Payment behavior in Bureaus can be included in collection Scorecards, if obtained periodically.

Collection strategies

Provision (reserve)Calculation

Just like in collections, looking to provision according to the probability of default of a customer, Credit Bureau’s information allows to know and use negative payment behavior with other institutions.

Provision (reserve)Calculation

Just like in collections, looking to provision according to the probability of default of a customer, Credit Bureau’s information allows to know and use negative payment behavior with other institutions.



WHY A MICROFINANCE BUREAU?

A number of MFIs (regulated and no regulated) with invisible risk knowledge about the debt level in the market of their potential clients.

Regulated institutions that share information about their clients but not receive information about the MFIs clients.

Qualification of Microfinance segment according to it’s specific characteristics (terms, Sociodemographic information, etc.).

To differentiate credit bureau inquiry prices according to the Microfinance institutions capability.

To evaluate the microfinance customer with different parameters than other customer segment.



WHAT CHARACTERISTICS SHOULD A MICROFINANCE BUREAU HAVE?

Payment delays in daily or weekly groups, not only by month.

For Regulated and No-regulated institutions.

Not just historical information, but also to consider Sociodemographic and business data.

Strategies for each institution according to risk profile and other characteristics.

To make recommendations to the institutions using the collected information.



MICROFINANCE BUREAU APPLICATIONS(Micro-Scoring Bureau Methodology) Practical Case

Scoring In Nicaragua

In the following sections are shown the results from a research presented in 2003 as part of the background work for the World Bank loan, “Broad Based Access to Financial Services”. The primary goal of this loan is to reform and strengthen the operating environment for microfinance institutions so that they are better able to provide financial services to low-income consumers and micro and small enterprises. One of the constraints to access which the project identified was the lack of credit information in Nicaragua. In 2003, no bureau had yet initiated operations and due to the absence of a clear legal and regulatory framework, neither was there another private credit bureau for the banking sector. Although a public registry existed in the Superintendent of Banks (SIBOIF), it was not automated and was only being used in a very marginal way by the financial industry. This research project was conceived to provide empirical evidence of the importance of credit information sharing in Nicaragua, both to support efforts in the microfinance community to share data and to promote the development of private credit reporting for the banking sector.

Behavior Analysis: Credit history

The Gathered information for the research has different way for being analyzed, once of those are by the number of duties at the data so that the total performance of individual borrowers could be followed and monitored. Slightly less than two-thirds of the borrowers in the sample (62.85%) – about 100,000 borrowers - had only one loan in the database. Another 26,702 borrowers or 16.67% of the sample, had two loans reported and nearly 90% of the sample had no more than four loans included in the database. There were, however, a significant number of multiple-transaction customers who had more than ten different credits reported in the database; this group included more than 12,000 different borrowers and approximately 7.5% of the sample



Credit counter customers %1 100,693 62.85%2 26,702 16.67%3 9,909 6.19%4 4,769 2.98%5 2,726 1.70%6 1,467 0.92%7 861 0.54%8 496 0.31%9 315 0.20%10 199 0.12%Greater 10 12,073 7.54%Grand Total 160,210 100.00%

Maturity Analysis with Good and Bad Definition: Nicaragua

In order to develop an empirical model or scorecard, a period of observation or maturity had to be established. Customers were distributed over time, and their repayment behavior compared, in order to determine at what point over the life of the loan we could safely say that all of the customers who were likely to default had probably already done so.

Data were coded to represent month one through thirty for each institution’s sample. Months 19 to 30 provided a period of observation for identifying who were ultimately the good and bad customers. “Good” customers were defined as those borrowers who had no late payments in excess of 30 days or defaults. “Bad” customers had late payments in excess of 30 days or other irregularities such as default, bankruptcy, etc…. Months one to six provided data to be used in predicting the likelihood of a customer becoming delinquent or “bad” and the window of analysis was a twelve month period between months 7 and 18. Table 8 below indicates the distribution of classified customers between “good” and “bad”. The total number of customers in the empirical sample was 89,267, of which 62,332 or approximately 70% were good, having no late payments in excess of 30 days. These borrowers took loans in months 7 to 18 and also were reported upon in months 19 to 30 so they could be identified as “good” or “bad” borrowers.

Customer Classification Number of Customers Percent of classified sampleGood 62,332 69.83Bad 26,935 30.17TOTAL classified customers 89,267 100

Classification of “Good” and “Bad” Customers



Normally the maturity point is between 6 and 12 months for micro credits. The percentage of loans that have defaulted after the maturity point indicates the real default rate for a population. For Nicaragua, for the time period and institutions analyzed, the real default rate was between 40% and 45%. This is an extremely high level of default and is likely related to the absence of accurate, reliable and timely credit information in the Nicaraguan economy and with the limited use of associated risk mitigation technologies, such as credit scoring in the microfinance sector. In other Latin American countries default rates are much lower, averaging between 10% and 15%. Graph 1 below shows the evolution of loan performance in Nicaragua. As expected, only one or two months after a loan is taken, default rates are relatively low, but after six months nearly 20% of loans are more than 30 days past due. This percentage continues to increase until the 12th month when it stabilizes – at more than 40% of the loan sample.


Total Distribution Clients by Date issued

39.05%44.70% 43.93%

28.50%

8.57%

42.47%

17.59%

0.00%6.74%

18.58%

49.79%

41.91%31.35%42.15%

52.38%

35.10%

39.69% 40.26%

50.70%

43.33%45.49%

45.37%

41.50% 26.12%

24.77%

2.83%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Date issued

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5,000

10,000

15,000

20,000

25,000

Am

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Cli

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% delinquency greater than 30Total Clients

Total Distribution Clients by Date issued

39.05%44.70% 43.93%

28.50%

8.57%

42.47%

17.59%

0.00%6.74%

18.58%

49.79%

41.91%31.35%42.15%

52.38%

35.10%

39.69% 40.26%

50.70%

43.33%45.49%

45.37%

41.50% 26.12%

24.77%

2.83%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Date issued

% H

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Am

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Cli

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% delinquency greater than 30Total Clients


From Total Population towards Targeted Population



Behavior Analysis: Maximum Historical Delinquent Accounts

Borrowers in the sample were also classified by their maximum previous late payment or default. Customers who had no prior credit history (for whom the first observed loan was in months 7 to 18) performed slightly worse than the population at large. Customers with a good credit history, by comparison, performed 36.22% better than the general population. This means that they are 36% more likely to repay on time, as compared with customers without a positive credit history. A bad credit history increased a borrower’s risk of having a loan go bad by more than 43%. These figures are described in detail in the following chart.

customers % customers %a.0 a 30 16,458 80.76% 3,922 19.24% 20,380 22.83% 36.22%b.31 a 60 2,131 56.84% 1,618 43.16% 3,749 4.20% -43.03%Greater 60 2,493 54.08% 2,117 45.92% 4,610 5.16% -52.19%No previous credit history 41,250 68.15% 19,278 31.85% 60,528 67.81% -5.56%Grand Total 62,332 69.83% 26,935 30.17% 89,267 100.00% 0.00%

Total % %RefMaximum delinquency

statusGood Bad Total

customers

Analyzed Variables Bureau Scoring

All the analyzed variables can be grouped in the customer information which refers to the data for the personal characteristic of the client as its age, its marital status, type of dwelling etc; other group regards the characteristics of the duties (products) as the entity where it was required, the amount required, frequency of payment etc; The final group of the analyzed variables refers to the behavior of the customer, this characterizes to the population in accordance with the payment habits and the way that the client search for financial resources.

Customer Product BehaviorAge Entity Number of creditsMarital Status Type of borrower Delinquency statusGender Source money Maximum historical delinquencyCity Amount Maximum delinquency in a semesterEducational level Maturity Maximum delinquency in a quarterNo. of dependants Type of Credit Maximum delinquency in last monthType of dwelling Frecuency of payment Number of credits up to date

Number of credits never past due 30 days or more

Summary of Relevant Variables for the Model



Through the treatment of the information, finally results a list of variables with its respective characteristics that are considered for the model estimation. From this group of variables is that at last come from the variables that finally belong to the model. The following chart shows the susceptible variables list:

NICARAGUA SCORECARD REWARD/ PUNISH

Clients older than 50 years REWARDMarried Clients REWARDMale Clients PUNISHClients from Chinandega or Granada or Masaya or Leon REWARDClients with no persons encharged REWARDClients who own dwelling REWARDClients whit credits in Cooperatives REWARDCredit amount granted between 1.000 and 5.000 Córdobas REWARDCredit maturity between 1 month and 5 months REWARDClients with annual maximum delinquency greater than 60 days PUNISHClients with maximum delinquency greater than 30 days in a semester PUNISHClients with maximum delinquency greater than 30 days in a quarter PUNISHClients with maximum delinquency in Cooperatives greater than 30 days REWARDClients with maximum delinquency in Micro financial associations greater than 30 days REWARDClients not ever 30+ days delinquency in more than 3 credits REWARD

Scorecard Distribution of a Scoring Bureau

When the model is estimated, a previous way to see the discrimination capability of it consist in sees how the model distributes. Also this way of presenting the model results helps in the determination of a cut-off for the strategies (see the next apart).

Interval of score Goods % Goods Bads % Bads Total % TotalTo 560 1,476 44.42% 1,847 55.58% 3,323 3.72%561 to 592 7,166 54.28% 6,036 45.72% 13,202 14.79%593 to 611 5,079 57.57% 3,744 42.43% 8,823 9.88%612 to 654 8,839 59.34% 6,056 40.66% 14,895 16.69%655 to 662 6,825 65.89% 3,533 34.11% 10,358 11.60%663 to 760 6,889 77.40% 2,011 22.60% 8,900 9.97%761 to 801 8,343 86.67% 1,283 13.33% 9,626 10.78%802 to 860 7,111 87.13% 1,050 12.87% 8,161 9.14%861 to 985 7,877 88.33% 1,041 11.67% 8,918 9.99%Greater 985 2,727 89.09% 334 10.91% 3,061 3.43%Grand Total 62,332 69.83% 26,935 30.17% 89,267 100.00%

Interval of score Goods % Goods Bads % Bads Total % TotalTo 560 1,476 44.42% 1,847 55.58% 3,323 3.72%561 to 592 7,166 54.28% 6,036 45.72% 13,202 14.79%593 to 611 5,079 57.57% 3,744 42.43% 8,823 9.88%612 to 654 8,839 59.34% 6,056 40.66% 14,895 16.69%655 to 662 6,825 65.89% 3,533 34.11% 10,358 11.60%663 to 760 6,889 77.40% 2,011 22.60% 8,900 9.97%761 to 801 8,343 86.67% 1,283 13.33% 9,626 10.78%802 to 860 7,111 87.13% 1,050 12.87% 8,161 9.14%861 to 985 7,877 88.33% 1,041 11.67% 8,918 9.99%Greater 985 2,727 89.09% 334 10.91% 3,061 3.43%Grand Total 62,332 69.83% 26,935 30.17% 89,267 100.00%



As was described before, the discrimination capability of the model can be considered as good owed to the tendency of the “Bads” percentage along the Score level increase, i.e. as higher the Score is, lower the risk will be.

Scorecard Evaluation: Design of Strategies

Clients % Clients %To 560 1,476 44.42% 1,847 55.58% 3,323 3.72%561 to 592 7,166 54.28% 6,036 45.72% 13,202 14.79%593 to 611 5,079 57.57% 3,744 42.43% 8,823 9.88%612 to 654 8,839 59.34% 6,056 40.66% 14,895 16.69%655 to 662 6,825 65.89% 3,533 34.11% 10,358 11.60%663 to 760 6,889 77.40% 2,011 22.60% 8,900 9.97%761 to 801 8,343 86.67% 1,283 13.33% 9,626 10.78%802 to 860 7,111 87.13% 1,050 12.87% 8,161 9.14%861 to 985 7,877 88.33% 1,041 11.67% 8,918 9.99%Greater 985 2,727 89.09% 334 10.91% 3,061 3.43%Total 62,332 69.83% 26,935 30.17% 89,267 100.00%

Total %Interval of ScoreGood Bad Total

Clients

Automatic rejections

Follow-up High Risk

Automatic approval

Follow-up Low Risk

Clients % Clients %To 560 1,476 44.42% 1,847 55.58% 3,323 3.72%561 to 592 7,166 54.28% 6,036 45.72% 13,202 14.79%593 to 611 5,079 57.57% 3,744 42.43% 8,823 9.88%612 to 654 8,839 59.34% 6,056 40.66% 14,895 16.69%655 to 662 6,825 65.89% 3,533 34.11% 10,358 11.60%663 to 760 6,889 77.40% 2,011 22.60% 8,900 9.97%761 to 801 8,343 86.67% 1,283 13.33% 9,626 10.78%802 to 860 7,111 87.13% 1,050 12.87% 8,161 9.14%861 to 985 7,877 88.33% 1,041 11.67% 8,918 9.99%Greater 985 2,727 89.09% 334 10.91% 3,061 3.43%Total 62,332 69.83% 26,935 30.17% 89,267 100.00%

Total %Interval of ScoreGood Bad Total

Clients

Automatic rejections

Follow-up High Risk

Automatic approval

Follow-up Low Risk

In accordance with the outputs of the Scorecard model, the strategies developed are in function to reduce the risk level associated to new customers, so the population is groped by the score in ranges so the risk levels in those segments can be a comparable measure with the risk of the total population. As shown in the chart upside, the red segment represents applications that will be automatically rejected because represents a significant higher risk level than the total population. The orange segment refers to the applications that, thought has a higher risk than the total population, are the 38.17% so is reasonable to make a deeper analysis in order to “catch” the good clients inside this segment.

A yellow segment is the population with a lower risk than the whole population (but very close) so a manual review will reduce significantly this risk. And finally, the green segment is a 33.34% of the actual population that is suggested to approve automatically because it has a very lower risk (in comparison with the total population risk).

The scores derived from these models can also be used for a variety of risk reduction collecting strategies by lenders. In addition to loan applications, scores can also be used to define collection strategies for different borrower types such as preventive collection for high risk customers even if they are only a few days past due or going more quickly to legal action. Identification of low risk customers via scoring presents other opportunities, such as marketing new products or increasing credit lines.



Interval of Score clients % clients % clients % clients %a. To 560 12,435 40.08% 5,553 17.90% 3,535 11.39% 9,501 30.62% 31,024 19.36%b.561 to 592 12,534 75.18% 1,810 10.86% 810 4.86% 1,519 9.11% 16,673 10.41%c.593 to 611 9,964 79.77% 1,174 9.40% 422 3.38% 931 7.45% 12,491 7.80%d.612 to 654 17,122 79.85% 2,052 9.57% 847 3.95% 1,422 6.63% 21,443 13.38%e.655 to 662 11,462 85.54% 1,035 7.72% 312 2.33% 590 4.40% 13,399 8.36%f.663 to 760 12,953 82.32% 1,264 8.03% 392 2.49% 1,126 7.16% 15,735 9.82%g.761 to 801 9,088 88.81% 554 5.41% 153 1.50% 438 4.28% 10,233 6.39%h.802 to 860 18,656 93.33% 716 3.58% 193 0.97% 425 2.13% 19,990 12.48%i.861 to 985 13,067 92.92% 570 4.05% 136 0.97% 290 2.06% 14,063 8.78%j.Greater 985 4,728 91.65% 261 5.06% 57 1.10% 113 2.19% 5,159 3.22%Grand Total 122,009 76.16% 14,989 9.36% 6,857 4.28% 16,355 10.21% 160,210 100.00%

Total % Totala.0 to 30 b.31 to 60 c.61 to 90 Greater than 90

Preventive Collection

Intense Collection

Marketing and reactivation

Intermediate collection

Legal Action

Interval of Score clients % clients % clients % clients %a. To 560 12,435 40.08% 5,553 17.90% 3,535 11.39% 9,501 30.62% 31,024 19.36%b.561 to 592 12,534 75.18% 1,810 10.86% 810 4.86% 1,519 9.11% 16,673 10.41%c.593 to 611 9,964 79.77% 1,174 9.40% 422 3.38% 931 7.45% 12,491 7.80%d.612 to 654 17,122 79.85% 2,052 9.57% 847 3.95% 1,422 6.63% 21,443 13.38%e.655 to 662 11,462 85.54% 1,035 7.72% 312 2.33% 590 4.40% 13,399 8.36%f.663 to 760 12,953 82.32% 1,264 8.03% 392 2.49% 1,126 7.16% 15,735 9.82%g.761 to 801 9,088 88.81% 554 5.41% 153 1.50% 438 4.28% 10,233 6.39%h.802 to 860 18,656 93.33% 716 3.58% 193 0.97% 425 2.13% 19,990 12.48%i.861 to 985 13,067 92.92% 570 4.05% 136 0.97% 290 2.06% 14,063 8.78%j.Greater 985 4,728 91.65% 261 5.06% 57 1.10% 113 2.19% 5,159 3.22%Grand Total 122,009 76.16% 14,989 9.36% 6,857 4.28% 16,355 10.21% 160,210 100.00%

Total % Totala.0 to 30 b.31 to 60 c.61 to 90 Greater than 90

Preventive Collection

Intense Collection

Marketing and reactivation

Intermediate collection

Legal Action

Reduction of the Level of Risk with the Application of the Model

As is known, one of the main objectives for an implementation of a Scoring model is to reduce the risk level in the total client population. So the diminishing is shown in accordance with the definition of a cut off (shown in the strategies apart).

Ascending Accumulative Distribution

Interval of Score Goods % Goods Bads % Bads Total % Total To 560 1,476 2.37% 1,847 6.86% 3,323 3.72%561 to 592 8,642 13.86% 7,883 29.27% 16,525 18.51%593 to 611 13,721 22.01% 11,627 43.17% 25,348 28.40%612 to 654 22,560 36.19% 17,683 65.65% 40,243 45.08%655 to 662 29,385 47.14% 21,216 78.77% 50,601 56.69%663 to 760 36,274 58.19% 23,227 86.23% 59,501 66.66%761 to 801 44,617 71.58% 24,510 91.00% 69,127 77.44%802 to 860 51,728 82.99% 25,560 94.90% 77,288 86.58%861 to 985 59,605 95.63% 26,601 98.76% 86,206 96.57%Greater 985 62,332 100.00% 26,935 100.00% 89,267 100.00%

Interval of Score Goods % Goods Bads % Bads Total % Total To 560 1,476 2.37% 1,847 6.86% 3,323 3.72%561 to 592 8,642 13.86% 7,883 29.27% 16,525 18.51%593 to 611 13,721 22.01% 11,627 43.17% 25,348 28.40%612 to 654 22,560 36.19% 17,683 65.65% 40,243 45.08%655 to 662 29,385 47.14% 21,216 78.77% 50,601 56.69%663 to 760 36,274 58.19% 23,227 86.23% 59,501 66.66%761 to 801 44,617 71.58% 24,510 91.00% 69,127 77.44%802 to 860 51,728 82.99% 25,560 94.90% 77,288 86.58%861 to 985 59,605 95.63% 26,601 98.76% 86,206 96.57%Greater 985 62,332 100.00% 26,935 100.00% 89,267 100.00%

If the Cut-off is allocated in the second score range, with the automatic rejection will be eliminated the 29.27% of the total Bads in the whole population rejecting a 18.51% (of total population) with a sacrifice of total Good population of a 13.86%.



Descending Accumulative Distribution

Interval of Score Goods % Goods Bads % Bads Total % Total Greater 985 2,727 89.09% 334 10.91% 3,061 3.43%861 to 985 10,604 88.52% 1,375 11.48% 11,979 13.42%802 to 860 17,715 87.96% 2,425 12.04% 20,140 22.56%761 to 801 26,058 87.54% 3,708 12.46% 29,766 33.34%663 to 760 32,947 85.21% 5,719 14.79% 38,666 43.31%655 to 662 39,772 81.13% 9,252 18.87% 49,024 54.92%612 to 654 48,611 76.05% 15,308 23.95% 63,919 71.60%593 to 611 53,690 73.81% 19,052 26.19% 72,742 81.49%561 to 592 60,856 70.81% 25,088 29.19% 85,944 96.28%To 560 62,332 69.83% 26,935 30.17% 89,267 100.00%

Interval of Score Goods % Goods Bads % Bads Total % Total Greater 985 2,727 89.09% 334 10.91% 3,061 3.43%861 to 985 10,604 88.52% 1,375 11.48% 11,979 13.42%802 to 860 17,715 87.96% 2,425 12.04% 20,140 22.56%761 to 801 26,058 87.54% 3,708 12.46% 29,766 33.34%663 to 760 32,947 85.21% 5,719 14.79% 38,666 43.31%655 to 662 39,772 81.13% 9,252 18.87% 49,024 54.92%612 to 654 48,611 76.05% 15,308 23.95% 63,919 71.60%593 to 611 53,690 73.81% 19,052 26.19% 72,742 81.49%561 to 592 60,856 70.81% 25,088 29.19% 85,944 96.28%To 560 62,332 69.83% 26,935 30.17% 89,267 100.00%

With the descending distribution, is possible to see that the risk level of the automatic approval segment is of 12.46% (very lower than the original risk level of 30.17%)

Validity Tests of the Model

The definition of a credit scoring model is that it rank-orders customers by risk, so a higher score is associated with a reduced likelihood of default. In order to validate the quality of the model there are statistical tests which can be performed. The Kolmogorov Smirnov test is one of the standard ways that the predictive power of credit scoring models is measured. The KS test measures the maximum separation between goods and bads and the Gini test measures the area between these two borrower types. A KS over 25% and a Gini over 35% are standard measures of good fit for credit scoring models. In this sample, the KS was 31.62% and the Gini was 37.79%, which is quite good considering the limitations in the database.



0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

To 560 561 to592

593 to611

612 to654

655 to662

663 to760

761 to801

802 to860

861 to985

Greater985

Interval of Score

%

% Goods% Bads

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

To 560 561 to592

593 to611

612 to654

655 to662

663 to760

761 to801

802 to860

861 to985

Greater985

Interval of Score

%

% Goods% Bads

Interval of Score Total IndexTo 560 0.001624 561 to 592 0.036376 593 to 611 0.049870 612 to 654 0.130869 655 to 662 0.109310 663 to 760 0.078646 761 to 801 0.061816 802 to 860 0.060255 861 to 985 0.069031 Greater 985 0.024258 Grand Total 0.622055

37.79%

Importance of Including Demographic Information

Performing the comparative analysis, where demographic information on borrowers is omitted from the model. The predictive power of the model falls, so that more bad loans are made at any targeted approval rate. For example, at a targeted approval rate of 60%, the model without demographic data produces a 22.86% default rate among accepted applicants compared to an 18.94% default rate for the full model, corresponding to an increase of 20.69% in defaults.

Approval rate Full modelWithout

demographic model

Percent increase in default rate on loan

with without demographic model

25% 11.94% 12.07% 1.13%35% 12.54% 15.74% 25.53%60% 18.94% 22.86% 20.69%100% 30.17% 30.17% 0.00%

Default rates estimates


K.S.=31.62

GINI =


Interval of Score Goods % Goods Bads % Bads Total % TotalTo 612 2,204 48.26% 2,363 51.74% 4,567 5.12%613 to 629 15,908 59.16% 10,980 40.84% 26,888 30.12%630 to 679 3,837 67.07% 1,884 32.93% 5,721 6.41%680 to 710 15,296 68.01% 7,194 31.99% 22,490 25.19%711 to 793 9,208 79.78% 2,334 20.22% 11,542 12.93%794 to 925 12,545 87.90% 1,727 12.10% 14,272 15.99%Greater 925 3,334 88.04% 453 11.96% 3,787 4.24%Grand Total 62,332 69.83% 26,935 30.17% 89,267 100.00%

As expected, the validity tests for the estimation without demographic data are lower what means that the discrimination capability is lesser (KS = 23.49% Gini Coefficient= 30.79)



Predictive Power Using Positive and Negative information

When the test is performed for a model using only negative payment history data, compared with a full model using both positive and negative payment history data. Positive data refers to information showing that a customer has paid a loan on time and in full as required by the loan contract. At a targeted approval rate of 60 percent, the negative-only data model produces a 24.41% default rate among accepted applicants compared to a 18.94% default rate for the full model. This translates into an increase of 28.87% in the default rate when only negative payment data is included in the model.

Approval rate Full modelNegative model

Percent increase in default rate on loan with Negative model

25% 11.94% 17.35% 45.38%35% 12.54% 19.84% 58.26%60% 18.94% 24.41% 28.87%100% 30.17% 30.17% 0.00%


Interval of Score Goods % Goods Bads % Bads Total % TotalTo 381 137 28.36% 346 71.64% 483 5.78%382 to 471 237 40.10% 354 59.90% 591 7.07%472 to 503 345 51.57% 324 48.43% 669 8.00%504 to 513 371 51.82% 345 48.18% 716 8.57%514 to 527 717 51.88% 665 48.12% 1,382 16.53%528 to 547 1,118 54.99% 915 45.01% 2,033 24.32%548 to 654 574 62.53% 344 37.47% 918 10.98%655 to 705 607 68.98% 273 31.02% 880 10.53%Greater 706 518 75.40% 169 24.60% 687 8.22%Grand Total 4,624 55.32% 3,735 44.68% 8,359 100.00%

With just negative data the KS is about 15.70% and the Gini is 23.43% what means that in comparison with this index for using both information (positive and negative) the discrimination capability of the model decrease.

Importance of a Multi-Sector Bureau

Using the full model to rank customers and, hypothetically approve them in the order of their credit scores, the approval rate could be approximately doubled with the same default rate. With data from the retail sector only, at an approval rate of 35%, the expected default rate is 18.59%. If the full data model is used, where data from microfinance NGOs and cooperatives is also available, then at an approval rate of 60%, defaults have only risen slightly to 18.94%. Put another way, at the given 60% approval



rate, the default rate using the single sector model is 15.88% higher than if the full model dataset is used.

Approval rate Full modelOnly one Sector model

Percent increase in default rate on loan with Only one Sector

model25% 11.94% 17.30% 44.96%35% 12.54% 18.59% 48.30%60% 18.94% 21.95% 15.88%100% 30.17% 30.17% 0.00%


Interval of Score Goods % Goods Bads % Bads Total % TotalTo 442 343 28.02% 881 71.98% 1,224 3.07%443 to 451 2,469 43.09% 3,261 56.91% 5,730 14.36%452 to 492 1,457 45.04% 1,778 54.96% 3,235 8.11%493 to 503 2,838 47.43% 3,145 52.57% 5,983 14.99%504 to 538 2,089 51.16% 1,994 48.84% 4,083 10.23%539 to 605 3,474 62.63% 2,073 37.37% 5,547 13.90%606 to 654 4,882 65.33% 2,591 34.67% 7,473 18.73%655 to 748 3,040 69.66% 1,324 30.34% 4,364 10.94%Greater 748 1,761 77.85% 501 22.15% 2,262 5.67%Grand Total 22,353 56.02% 17,548 43.98% 39,901 100.00%

The Gini and K.S. of this new model is 21.88% and 26.80% respectively.

Summary chartResults KS GINIGeneral Scorecard: Full model 31.62% 37.79%Negative Scorecard 15.70% 23.43%Without demographic information Scorecard 23.49% 30.79%Only one Sector Scorecard 21.88% 26.80%



CONCLUSIONS

Day by day Microfinance institutions are growing in number, mainly as the result of a higher profitable market.

MFI´s require support in the knowledge of the historical behavior and potential risk of their clients; because of this a Microfinance bureau is required, but this bureau must understand the specific characteristics of the market and must be able to give data and information to the institutions.

The presence of these bureaus helps reduce the market information asymmetry and allow the entrance of new actors to the microfinance market.


objective

Documents

microfinance market

microfinance formalization

credit bureau

south africa

africa objective

africa lilian simbaqueba

north africa ratio

credit reporting systems