Probability of Default for Microfinance Institutions May 2014 SPTF Annual Meeting - Senegal.

Probability of Default for Microfinance Institutions

May 2014

SPTF Annual Meeting - Senegal

Probability of Default Modeling 2

2

Moody’s has been a supporter of the SPTF’s efforts in obtaining and reporting information on the social performance of MFIs

» SPTF was represented on the Moody’s task force in the development of our Social Performance Assessment (SPA)

» Moody’s has served on various working groups on social standards for the industry

» Moody’s is committed to obtaining information on social performance issues that are standardized in order to report information and model risks of poor social performance

» The Moody’s research on PD in microfinance is consistent with the data being collected through the SPI4

» Moody’s is interested in working with Cerise and the SPI4 to continue to model social data and look at its impact both on clients and the credit risk of MFIs

Moody’s and SPI4


Agenda

Modeling probability of default (PD)

Social performance and default probability


Overview1


Probability of Default by Moody’s Grade Importance of Calculating PD Pricing loans

Investor return

Portfolio risk


Hurts Accuracy

Helps Accuracy

6

Developing a ModelM

argi

nal C

ontr

ibut

ion

to A

ccur

acy

4 8 12 16 20 n

Number of Factors in Scorecard

0

Pos

Neg

Recommended Range

Deciding on Number of Factors for Scorecard

For model building purposes, we may want to have more

factors initially, with understanding that some will

be discarded


Data Preparation2


Overview of Data Preparation

Data preparation involves collection of the required data, and deciding sources and systems to extract data. It also involves cleansing the data by removing financial statements that do not satisfy the following criteria:» Ratio checks: running the dataset through a series of data cleansing rules

» Default definition: consistent definition of default has to be determined to properly classify the obligors of the underlying data into defaulters and non-defaulters

» Determine the default horizon: determining a time window to classify the financial statements into defaults and non-defaults

Above criteria ensure that the data contains information of all obligors and the information is consistent with the business segment for which the model is being built .


Defining DefaultMethodology for tagging financial statements as default

» If financial statements were less than 3 month before default event then these statements were removed from the model development

» If 2 statements were available from 4 to 21 months before default event then statement closer to default event was kept and tagged as default and other statement was dropped

» If a defaulted obligor had a statement that was more than 21 months before default event then the statement was tagged as non-default

> 21 months

Tag as non-default Remove statement Tag as default Remove statement

4 - 21 months <= 3 months Default Event

Financial Statement

Financial Statement

Financial Statement

Financial Statement


Basic ChecksAll statements were passed through a series of filtering criteria

» Total Assets <=0

» Total Liabilities < =0

» Total Revenue <=0

» Total assets do not match to the sum of total liabilities and total equity reserves (a threshold of 2% was used)

» Cash and Equivalents < 0

1. Refer appendix 11 for details of basic check analysis

FINAL DATA SAMPLE (Before Basic Checks)Total Statements: 868Unique MFIs: 293Defaults: 16 (1.84%)

Basic Checks1

34 (3.9%) statements dropped

FINAL SAMPLE FOR MODEL DEVELOPMENTTotal Statements: 834Unique MFIs: 292Defaults: 16 (1.92%)

» Total Current Assets < 0

» Total Non Current Assets < 0

» Depreciation and Amortization < 0

» Total Operating Expenses < 0

» Total Long Term Liabilities < 0


Candidate Quantitative Factors: Single Factor Analysis3


The available data yields 46 potential factors for single factor analysis

Category Factor Name Calculation

Sustainability/Profitability

GrossMargin (Total_Revenue - Financial_Costs) / Total_Revenue

OperatingMargin (Total_Revenue - Financial_Costs - Loan_Loss_Provision - Operating_Expense)/Total_Revenue

ROE (Total_Revenue - Financial_Costs - Loan_Loss_Provision - Operating_Expense)/(Total_Assets-Total_Liabs)

ROA (Total_Revenue - Financial_Costs - Loan_Loss_Provision - Operating_Expense)/Total_Assets

Operational_self_sufficiency Total_Revenue/(Financial_Costs + Loan_Loss_Provision + Operating_Expense)InterestCoverage Total_Revenue/Interest and fee expense on all funding liabilities (v3210 )CashtoLiabs Cash & Cash Equivalents – Audited (v1110)/Total_Liabs

Asset/Liability Management

Yield_on_Loan_Portfolio (Total_Revenue - Financial_Costs - Loan_Loss_Provision - Operating_Expense)/Gross_Loan_Portfolio

Gross_Yield_on_Loan_Portfolio (Total_Revenue + Non_Operating_Income - Financial_Costs - Loan_Loss_Provision - Operating_Expense - Non_Operating_Expense)/Gross_Loan_Portfolio

CurrentRatio Current_Assets/Current_LiabsFunding_expense_ratio Interest and fee expense on all funding liabilities (v3210 )/Gross_Loan_PortfolioLiabtoNetWorth Total_Liabs/(Total_Assets-Total_Liabs)LiabtoAssets Total_Liabs/Total_Assets

LiabtoEBITDA Total_Liabs/(Total_Revenue - Financial_Costs - Loan_Loss_Provision - Operating_Expense + Depreciation and Amortization(v3530))

RevenuetoTotalAsts Total_Revenue/Total_Assets

GrowthTotal_RevenueGrowth (Total_Revenue-Total_Revenue_Prev)/Total_Revenue_PrevGrossPortfolioGrowth (Gross_Loan_Portfolio-Gross_Loan_Portfolio_Prev)/Gross_Loan_Portfolio_Prev

SizeLoanPortfolio_CPIAdj (229.601/CPI_INDEX)*Gross_Loan_PortfolioTotal_Assets_CPIAdj (229.601/CPI_INDEX)*Total_AssetsAvg_outstanding_loansize (229.601/CPI_INDEX)*Gross_Loan_Portfolio/nb outstanding loans (v8040)

Different sources were considered to come up with a list of candidate factors for model development» Microfinance Handbook by Joanna Ledgerwood

» Microfinance Consensus Guidelines Published by CGAP/The World Bank Group, September 2003


The available data yields 46 potential factors for single factor analysis (cont’d)

Category Factor Name Calculation

Efficiency/Productivity

Loan_officer_productivity number of active borrowers (v8050)/ number of loan officers (v8010)

Personnel_productivity number of active borrowers (v8050)/ Number of employees (v8020)Branch_Productivity number of active borrowers (v8050)/ Number of branches (v8030)

PBT_per_loan_officer(229.601/CPI_INDEX)*(Total_Revenue + Non_Operating_Income - Financial_Costs - Loan_Loss_Provision - Operating_Expense - Non_Operating_Expense)/number of loan officers (v8010)

PBT_per_employee(229.601/CPI_INDEX)*(Total_Revenue + Non_Operating_Income - Financial_Costs - Loan_Loss_Provision - Operating_Expense - Non_Operating_Expense)/ Number of employees (v8020)

PBT_per_branch(229.601/CPI_INDEX)*(Total_Revenue + Non_Operating_Income - Financial_Costs - Loan_Loss_Provision - Operating_Expense - Non_Operating_Expense)/Number of branches (v8030)

loans_per_borrower Number of loans outstanding(v8040)/number of active borrowers (v8050)Operating_expense_ratio Operating_Expense/Gross_Loan_PortfolioFinancial_Expense_ratio Financial_Costs/Gross_Loan_PortfolioCost_per_borrower (229.601/CPI_INDEX)*Operating_Expense/number of active borrowers (v8050)Avg_portfolio_per_credit_officer (229.601/CPI_INDEX)*Gross_Loan_Portfolio/number of loan officers (v8010)

Portfolio quality

PAR_30_Ratio Portfolio at risk above 30 days (v7030)/Gross_Loan_PortfolioPAR_180_Ratio Of which portfolio at risk above 180 days (v7100)/Gross_Loan_PortfolioOnTime_Portfolio On-time portfolio (v7010)/Gross_Loan_PortfolioWriteoff_Ratio Write offs (v7140)/Gross_Loan_PortfolioRisk_coverage_ratio Loan loss reserve – Audited (v1220)/ Portfolio at risk above 30 days (v7030)LoanLossReserve_Ratio Loan loss reserve – Audited (v1220)/Gross_Loan_PortfolioArrears_rate Portfolio in arrears (v7130)/Gross_Loan_PortfolioPct_Refinanced reprogrammed and refinanced loans (v7115)/Gross_Loan_Portfolio

Others

Avg_maturity_of_loans mean(v8174,v8184,v7914,v7924,v7934,v7944)

Pct_Urban_Clients_Volume sum(Urban clients - volume of portfolio (v8410), Semi-Urban clients - volume of portfolio (v8420),0)/Gross_Loan_Portfolio

Pct_Female_Clients_Volume Female clients - volume of portfolio (v8320)/Gross_Loan_PortfolioPct_Revenue_From_Investments Financial revenue from investments – Audited (v3120)/Total_Revenue

Pct_Group_Loans sum(Self-help groups (v8250), Solidarity groups (v8260), Communal banks loans/Self-help groups – volume (v8270))/Gross_Loan_Portfolio

Type_Of_Loans 6-nmiss(v8110,v8120,v8130,v8140,v8150,v8160)

Loans_to_Ind_Types 10-nmiss(v8510,v8520,v8530,v8540,v8542,v8544,v8546,v8548,v8549,v8550)


In general, factors are evaluated on the following set of criteria

» Position Analysis: There must be enough observations. Observations where many values are missing typically indicate that the information is difficult to obtain. This information should therefore not be included in the final model

» Factors must be intuitive. Experienced credit analysts should be familiar with the factor and its relationship with credit risk given the credit culture in which they operate

» Factors must be consistent with expectations. Factor behaviour should be consistent with business judgment and any deviations in expectations should be easily explained

» Factors must be powerful. The ultimate list of factors incorporated into the model should exhibit a high degree of discriminatory power on the basis of credit risk


Single Factor Analysis Performance: 21 factors recommended for further exploration in MFA

*AR = Accuracy Ratio

Category Factor Name AR* Default Rate Relationship

Missing %

Recommendation Comments


GrossMargin 36% Good 2% OperatingMargin -13% Counterintuitive 2% ROE -5% Counterintuitive 3% ROA -7% Counterintuitive 3% Operational_self_sufficiency -11% Counterintuitive 2% InterestCoverage 37% Good 2%


Yield_on_Loan_Portfolio -5% Counterintuitive 2% Gross_Yield_on_Loan_Portfolio -9% Counterintuitive 2% CurrentRatio -28% Counterintuitive 2% Funding_expense_ratio 39% Strong 1% High correlation with LiabtoAssetsFinancial_Expense_ratio 46% Strong 1% LiabtoNetWorth 12% Good 2% High correlation with LiabtoAssetsLiabtoAssets 13% Good 2% LiabtoEBITDA -7% Counterintuitive 2% CashtoLiabs 19% Good 0%

GrowthTotal_RevenueGrowth 39% High missing %GrossPortfolioGrowth 38% High missing %

SizeLoanPortfolio_CPIAdj -13% Counterintuitive 0% Total_Assets_CPIAdj -14% Counterintuitive 2% Avg_outstanding_loansize 4% Weak 5% Used as a proxy for Income level of the borrowers


Single Factor Analysis Performance : 21 factors recommended for further exploration in MFA (cont’d)

Category Factor Name AR* Default Rate Relationship

Missing %

Recommendation Comments


Loan_officer_productivity 23% Good 5% Personnel_productivity 27% Good 5% Branch_Productivity 18% Good 6% PBT_per_loan_officer -8% Counterintuitive 6% PBT_per_employee -17% Counterintuitive 6% PBT_per_branch 3% Moderate 7% RevenuetoTotalAsts 12% Moderate 2% Operating_expense_ratio 28% Good 0% Cost_per_borrower 19% Good 5% Avg_portfolio_per_credit_officer 6% Good 4%

Portfolio Quality

PAR_30_Ratio -8% Counterintuitive 4% PAR_180_Ratio -32% Counterintuitive 8% OnTime_Portfolio 1% Good 4% Writeoff_Ratio 8% Moderate 7% Risk_coverage_ratio 11% Moderate 6% LoanLossReserve_Ratio -1% Moderate 2% Arrears_rate -2% Weak 9% Pct_Refinanced 14% High missing %

Others

Avg_maturity_of_loans 23% High missing %loans_per_borrower 32% Strong 6% Used as a proxy for Debt to Income ratio of borrowersPct_Urban_Clients_Volume 23% Good 0% Pct_Female_Clients_Volume 29% Good 5% Pct_Revenue_From_Investments -1% Counterintuitive 1% Pct_Group_Loans 20% High missing %Type_Of_Loans 3% Moderate 0% Low diversity of responses and very low accuracy ratio Loans_to_Ind_Types 10% Good 0% Used as a proxy for portfolio diversity


PAR 30 RatioKey statistics: Relative Entropy 0.96, Accuracy Ratio -8%

» This factor performs inadequately with no discriminatory power

» Counterintuitive relationship between the responses and the default rate

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

0

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100

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250missing 0.05 to High 0.025 to 0.05 0.01 to 0.025 0 to 0.01

Defau

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Answer

Frequencies and Default Rates for PAR_30_Ratio

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CAP Curve of PAR_30_Ratio


PAR 180 RatioKey statistics: Relative Entropy 0.96, Accuracy Ratio -32%

» Counterintuitive relationship between the responses and the default rate

0.0%0.5%1.0%1.5%2.0%2.5%3.0%3.5%4.0%4.5%5.0%5.5%6.0%6.5%7.0%7.5%

0

50

100

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250missing 0.012 to High 0.003 to 0.012 >0 to 0.003 0 to 0

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Frequencies and Default Rates for PAR_180_Ratio

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0.5

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0 0.25 0.5 0.75 1

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CAP Curve of PAR_180_Ratio


Avg_outstanding_loansizeKey statistics: Relative Entropy 0.95, Accuracy Ratio 4% ?

» This factor performs inadequately with low discriminatory power

» Weak relationship between the responses and the default rate i.e. higher the score lower the default rate

0

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CAP Curve of Avg_outstanding_loansize

0.0%0.5%1.0%1.5%2.0%2.5%3.0%3.5%4.0%

0

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100

150

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250missing < 500 500 to 1500 1500 to 2500 2500 to 4000 4000 to High

Defau

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Frequencies and Default Rates for Avg_outstanding_loansize


Candidate Quantitative Factors: Multi Factor Analysis4


Starting with 21 Candidate Factors from SFASection Factor Name AR Default Rate

Relationship Comments


GrossMargin 36% Good InterestCoverage 37% Good


Financial_Expense_ratio 46% Strong

LiabtoAssets 13% Good CashtoLiabs 19% Good

Size Avg_outstanding_loansize 4% Weak Used as a proxy for Income level of the borrowers


Loan_officer_productivity 23% Good Personnel_productivity 27% Good Branch_Productivity 18% Good PBT_per_branch 3% Moderate RevenuetoTotalAsts 12% Moderate Operating_expense_ratio 28% Good Cost_per_borrower 19% Good

Avg_portfolio_per_credit_officer 6% Good

Portfolio QualityOnTime_Portfolio 1% Good Writeoff_Ratio 8% Moderate Risk_coverage_ratio 11% Moderate

Others

loans_per_borrower 32% Strong Used as a proxy for Debt to Income ratio of borrowers

Pct_Urban_Clients_Volume 23% Good

Pct_Female_Clients_Volume 29% Good

Loans_to_Ind_Types 10% Good Used as a proxy for portfolio diversity

» As number of defaults are very low i.e. 16, we kept all the factors with positive accuracy ratio for MFA

» Return ratios e.g. ROA and ROE are not present in the candidate factors list because MFIs typically operate on low return and higher base i.e. large assets


Pct_Female_Clients_VolumeKey statistics: Relative Entropy 0.88, Accuracy Ratio 29%

» This factor performs adequately with moderate discriminatory power

» Good relationship between the responses and the default rate i.e. higher the score lower the default rate

0

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CAP Curve of Pct_Female_Clients_Volume

0.0%0.5%1.0%1.5%2.0%2.5%3.0%3.5%4.0%4.5%5.0%

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300

400

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600missing 0 to 0.35 0.35 to High

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Frequencies and Default Rates for Pct_Female_Clients_Volume


Candidate Factor Correlation Matrix

GrossMargin

InterestCoverage

CashtoLiabs

Funding_expense_ratio

LiabtoNetWorth

LiabtoAssets

RevenuetoTotalAsts

Avg_outstanding_loansize

Loan_officer_productivity

Personnel_productivity

Branch_Productivity

PBT_per_branch

loans_per_borrower

Operating_expense_ratio

Financial_Expense_ratio

Cost_per_borrower

portfolio_per_credit_officer

OnTime_Portfolio

Writeoff_Ratio

Risk_coverage_ratio

Pct_Urban_Clients_Volume

Pct_Female_Clients_Volume

Loans_to_Ind_Types

GrossMargin 100% 62% 10% 34% 48% 48% 50% -27% 11% 22% 9% 1% 21% -10% 58% 27% 29% 1% -10% 11% 0% 15% -4%

InterestCoverage 62% 100% 27% 73% 18% 17% 13% 5% 10% 13% 8% 13% 5% -19% 53% 5% -9% -11% -7% 14% 15% 2% -3%

CashtoLiabs 10% 27% 100% 13% 5% 6% -2% -2% 6% 1% 4% -6% -3% -12% -1% -15% -7% -9% -10% -7% 13% -7% 7%

Funding_expense_ratio 34% 73% 13% 100% 3% 3% -32% 26% 8% 5% 6% 15% -7% -9% 75% -7% -30% -4% 12% 14% 11% -8% -1%

LiabtoNetWorth 48% 18% 5% 3% 100% 97% 34% -32% -3% 4% -7% -18% 26% -7% 25% 13% 30% 1% -5% -5% -7% 14% -4%

LiabtoAssets 48% 17% 6% 3% 97% 100% 34% -30% -1% 6% -4% -15% 24% -7% 26% 13% 30% 0% -5% -5% -6% 15% -4%

RevenuetoTotalAsts 50% 13% -2% -32% 34% 34% 100% -41% 3% 20% 12% 0% 22% -8% -15% 33% 46% -5% -24% 4% -2% 20% -8%

Avg_outstanding_loansize -27% 5% -2% 26% -32% -30% -41% 100% -8% -14% -16% 14% -19% 3% 7% -29% -48% -17% -1% 0% 15% -41% -10%Loan_officer_productivity 11% 10% 6% 8% -3% -1% 3% -8% 100% 47% 22% 18% -6% 2% 10% 23% -4% -6% -2% 3% -1% 7% -4%

Personnel_productivity 22% 13% 1% 5% 4% 6% 20% -14% 47% 100% 26% 18% 2% 6% 9% 35% 7% -7% -9% 7% -3% 13% -10%

Branch_Productivity 9% 8% 4% 6% -7% -4% 12% -16% 22% 26% 100% 18% -13% -2% 11% 17% 3% 1% -10% 10% 3% 17% -1%

PBT_per_branch 1% 13% -6% 15% -18% -15% 0% 14% 18% 18% 18% 100% -15% 5% 7% 22% -17% 11% 16% 26% 5% 2% -8%loans_per_borrower 21% 5% -3% -7% 26% 24% 22% -19% -6% 2% -13% -15% 100% -10% 3% 12% 33% 14% 8% 2% -19% 5% -1%

Operating_expense_ratio -10% -19% -12% -9% -7% -7% -8% 3% 2% 6% -2% 5% -10% 100% -5% 0% 11% 4% 0% 2% -2% -1% 6%

Financial_Expense_ratio 58% 53% -1% 75% 25% 26% -15% 7% 10% 9% 11% 7% 3% -5% 100% 5% -7% 7% 16% 16% 3% 5% -3%

Cost_per_borrower 27% 5% -15% -7% 13% 13% 33% -29% 23% 35% 17% 22% 12% 0% 5% 100% 24% 7% -3% 16% -13% 26% -9%

portfolio_per_credit_officer 29% -9% -7% -30% 30% 30% 46% -48% -4% 7% 3% -17% 33% 11% -7% 24% 100% 16% -4% 1% -13% 20% 1%

OnTime_Portfolio 1% -11% -9% -4% 1% 0% -5% -17% -6% -7% 1% 11% 14% 4% 7% 7% 16% 100% 43% 43% -13% 9% 9%

Writeoff_Ratio -10% -7% -10% 12% -5% -5% -24% -1% -2% -9% -10% 16% 8% 0% 16% -3% -4% 43% 100% 14% -9% -8% -3%

Risk_coverage_ratio 11% 14% -7% 14% -5% -5% 4% 0% 3% 7% 10% 26% 2% 2% 16% 16% 1% 43% 14% 100% 1% 1% 5%

Pct_Urban_Clients_Volume 0% 15% 13% 11% -7% -6% -2% 15% -1% -3% 3% 5% -19% -2% 3% -13% -13% -13% -9% 1% 100% -2% -4%Pct_Female_Clients_Volume 15% 2% -7% -8% 14% 15% 20% -41% 7% 13% 17% 2% 5% -1% 5% 26% 20% 9% -8% 1% -2% 100% 2%

Loans_to_Ind_Types -4% -3% 7% -1% -4% -4% -8% -10% -4% -10% -1% -8% -1% 6% -3% -9% 1% 9% -3% 5% -4% 2% 100%


Logistic Regression Models

1. For estimated coefficients and p value refer appendix 1

2. AR = Accuracy Ratio

Model Number of Factors Significance Level1 AR2 Comments

Model 1 5 P Value <= 0.05 69.5%

Model 2 8 P Value <= 0.1 77.8%

Model 3 6 P Value <= 0.1 73.4% Best model after dropping Pct_Urban_Clients_Volume

Model 4 4 P Value <= 0.05 65.5% Best model after dropping Avg_outstanding_loansize

Model 5 5 P Value <= 0.1 69.4% Best model after dropping Avg_outstanding_loansize

» Due to low number of defaults we also considered models with 90% significance level of estimated coefficients

» Pct_Urban_Clients_Volume represents percentage of urban and semi-urban borrowers of an MFI’s portfolio. Though this factors comes significant at 90% significance but we recommend not to include this factor in the model because MFIs typically have semi-urban and rural borrowers. Model should not penalize an MFI for having large base of rural clients

» Avg_outstanding_loansize was used as a proxy for income level of borrowers of MFIs. But given low accuracy ratio of this factor we also considered models after dropping this factor which resulted in a drop of 6% in AR for model 4 and 11% for model 5 compared to model 1 and model 2 respectively


Beta Model – Factor WeightsSection Factor Name Factor AR Model 1 Model 2 Model 3 Model 4 Model 5

Sustainability/ProfitabilityGrossMargin 36% InterestCoverage 37%

Asset/Liability ManagementFinancial_Expense_ratio 46% 22.4% 14.0% 18.2% 32.1% 26.7%LiabtoAssets 13% CashtoLiabs 19% 7.9% 11.6%

Size Avg_outstanding_loansize 4% 15.5% 13.7% 14.8%


Loan_officer_productivity 23% Personnel_productivity 27% Branch_Productivity 18% 8.6% PBT_per_branch 3% RevenuetoTotalAsts 12% Operating_expense_ratio 28% 17.8% 14.0% 16.0% 25.1% 21.2%Cost_per_borrower 19% Avg_portfolio_per_credit_officer 6%

Portfolio QualityOnTime_Portfolio 1% Writeoff_Ratio 8% Risk_coverage_ratio 11%

Others

loans_per_borrower 32% 17.6% 14.4% 15.2% 19.5% 18.2%Pct_Urban_Clients_Volume 23% 7.8% 14.6%Pct_Female_Clients_Volume 29% 26.7% 19.5% 24.2% 23.4% 19.3%Loans_to_Ind_Types 10%

Number of Factors 5 8 6 4 5Model AR 69.5% 77.8% 73.4% 65.5% 69.4%

» All models do not give any weight to sustainability/profitability and portfolio quality factors


Candidate Social Factors5


New Data Preparation

Quantitative (non SPA Data)Total Statements: 731Unique MFIs: 249Defaults: 16

Qualitative (SPA Data)Total Statements : 167Unique MFIs: 167Defaults: 10

Total Statements: 506 Unique MFIs: 161Defaults: 10 (1.98%)

Quantitative model prepared as before. Data for ‘Total Revenue Growth’ and ‘Gross Portfolio Growth’ updated for missing values

Remove statements from the quantitative data where MFI’s are not common to SPA (Qualitative) data 225 (30.8%) statements droppedCombined Model has been estimated on this data

6 MFI dropped due to no exact match with quant data

Merging two datasets

1. Quantitative Models have been estimated on 731 records and 16 defaults

2. Qualitative Models for have been estimated on 161 records and 10 defaults

3. The combined model uses 506 records and 10 defaults

Total Statements : 161Unique MFIs: 161Defaults: 10

Qualitative Model was prepared on this data


Candidate Social Factors

Variable ProbChiSq AR

Pricing Transparency Practices 0.463 6%

Disclosure of components of pricing 0.383 9%

Manner of communication of pricing 0.106 16%

Debt Collection Practices 0.059 27%

Specific debt collection policies 0.218 17%

Definition of acceptable and unacceptable collection practices 0.218

17%

Voluntarily adopted consumer protection standards 0.060

27%

Range of Products offered 0.159 24%

Policies included in Code of Ethics 0.351 15%

Written policies on hiring women 0.111 18%

Corruption Score 0.098 19%

Probability of chance occurrence is high

Low AR

Candidate social factors were based on availability of reliable data. Data sourced from the MIX and analyzed with Moody’s SPA


29

Rejected Social Variables

0%

5%

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20%

0102030405060708090

100

Less than equal to0.5 0.5 to 0.9 Greater than 0.9

Def

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Freq

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Answer

Frequencies and Default Rates for Pricing Transparency Practices

Pricing Transparency

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01020304050607080

Less thanequal to 0.2 0.2 to 0.6 0.6 to 0.9

Greater than0.9

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Answer

Frequencies and Default Rates for Policies included in Code of Ethics

Code of Ethics


30

Accepted Social VariablesDebt Collection Practices

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0102030405060708090

100

Less thanequal to 0.1 0.1 to 0.45 0.45 to 0.9

Greater than0.9

Defa

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ate

Freq

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y

Answer

Frequencies and Default Rates for Debt Collection Practices

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CAP Curve of Debt Collection Practices

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01020304050607080

Less thanequal to 0.2 0.2 to 0.4 0.4 to 0.6 0.6 to 0.8

Greaterthan 0.8

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Answer

Frequencies and Default Rates for Range of Products offered

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CAP Curve of Range of Products offered

Range of Products Offered


31

Combined Model

Combining the Quantitative and Qualitative factors give an AR of 79.0%

Section Section Weight Factor Factor Weight Final WeightCash to Liabilities 13.77% 8.9%Loans per borrower 16.48% 10.6%Operating expense ratio 22.62% 14.6%Financial Expense ratio 26.19% 16.9%Percent Female Clients Volume 20.94% 13.5%Debt Collection Practices 38.9% 13.9%Range of Products offered 61.1% 21.8%

Qualitative Score 35.6%

Quantitative Score 64%


Structural Component6


Qualitative factors are not necessarily judgmental, but cannot be empirically confirmed by the data

Franchise Operating Environment Systems

» Market position and sustainability

» Market size and geographic diversification

» Asset concentration and earnings diversification

» Macroeconomic stability

» Regulatory strength

» Legal system and corruption

» Audit process

» Board independence and governance

» Financial reporting and transparency

» Strength of credit scoring and risk management

» Access to alternative funding sources


Impact of Probability of Default on Recovery from Social Events7


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From 2008-2010, a series of repayment crises associated with social disruptions struck some of the world’s oldest and most advanced microfinance markets, such as India and Nicaragua. Tens of thousands of borrowers defaulted, institutions buckled, apparent development gains were reversed, and outside investors suffered severe losses, dampening confidence in what had looked like a fast-growing and resilient market.

The waves of default involved social phenomena on the ground, sudden changes of attitude associated with cultural shifts, and political movements. Scholars and practitioners identified a number of these “social default” repayment crises in the Andhra Pradesh region of India, in Nicaragua, Bosnia-Herzegovina, Morocco, Pakistan, Kazakhstan, and others. They have sought to understand these events and the kinds of practices and institutional arrangements that led to them.

*Based on research by Columbia university SIPA Program

Impact of Probability of Default on Recovery from Social Events*


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» How does a repayment crisis caused by a social event affect the financial performance of MFIs?

» Do strong social performance practices mitigate the severity of those financial effects?

Issues


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Database

Social performance data were obtained from MIX and then cleaned and sorted by Moody’s according to the SPA.


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Relationship Between PD and SPA Grade

No direct relationship between PD and SPA


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Relationship Between PD and SPA Grade

MFIs that scored best in social performance tended to show the most improvement in PD following a social event. Human resources was the social area that was most indicative of PD recovery


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Probability of Default for Microfinance Institutions May 2014 SPTF Annual Meeting - Senegal.

Documents

Transcript of Probability of Default for Microfinance Institutions May 2014 SPTF Annual Meeting - Senegal.