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Probability of Default for 
Microfinance Institutions 
May 2014
Probability of Default Modeling 2 
Overview 
1
Probability of Default Modeling 3 
Probability of Default by Moody’s Grade 
Importance of Calculating PD 
 Pricing loans 
 Investor return 
 Portfolio risk
Probability of Default Modeling 4 
Deciding on Number of Factors for Scorecard 
Helps Accuracy 
Hurts Accuracy 
4 
Developing a Model 
Marginal Contribution to Accuracy 
4 8 12 16 20 n 
Number of Factors in Scorecard 
Pos 
0 
Neg 
Recommended Range 
For model building purposes, 
we may want to have more 
factors initially, with 
understanding that some will 
be discarded
Data Preparation 
Probability of Default Modeling 5 
2
Probability of Default Modeling 6 
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 .
Probability of Default Modeling 7 
Defining Default 
Methodology 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
Probability of Default Modeling 8 
Basic Checks 
All 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 
FINAL DATA SAMPLE (Before Basic Checks) 
Total Statements: 868 
Unique MFIs: 293 
Defaults: 16 (1.84%) 
1. Refer appendix 11 for details of basic check analysis 
Basic Checks1 
34 (3.9%) statements dropped 
FINAL SAMPLE FOR MODEL DEVELOPMENT 
Total Statements: 834 
Unique MFIs: 292 
Defaults: 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 
Probability of Default Modeling 9 
Analysis 
3
The available data yields 46 potential factors for single 
factor analysis 
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 
Probability of Default Modeling 10 
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_Liabs 
Funding_expense_ratio Interest and fee expense on all funding liabilities (v3210 )/Gross_Loan_Portfolio 
LiabtoNetWorth 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 
Growth 
Total_RevenueGrowth (Total_Revenue-Total_Revenue_Prev)/Total_Revenue_Prev 
GrossPortfolioGrowth (Gross_Loan_Portfolio-Gross_Loan_Portfolio_Prev)/Gross_Loan_Portfolio_Prev 
Size 
LoanPortfolio_CPIAdj (229.601/CPI_INDEX)*Gross_Loan_Portfolio 
Total_Assets_CPIAdj (229.601/CPI_INDEX)*Total_Assets 
Avg_outstanding_loansize (229.601/CPI_INDEX)*Gross_Loan_Portfolio/nb outstanding loans (v8040)
The available data yields 46 potential factors for single 
factor analysis (cont’d) 
Probability of Default Modeling 11 
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_Portfolio 
Financial_Expense_ratio Financial_Costs/Gross_Loan_Portfolio 
Cost_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_Portfolio 
PAR_180_Ratio Of which portfolio at risk above 180 days (v7100)/Gross_Loan_Portfolio 
OnTime_Portfolio On-time portfolio (v7010)/Gross_Loan_Portfolio 
Writeoff_Ratio Write offs (v7140)/Gross_Loan_Portfolio 
Risk_coverage_ratio Loan loss reserve – Audited (v1220)/ Portfolio at risk above 30 days (v7030) 
LoanLossReserve_Ratio Loan loss reserve – Audited (v1220)/Gross_Loan_Portfolio 
Arrears_rate Portfolio in arrears (v7130)/Gross_Loan_Portfolio 
Pct_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_Portfolio 
Pct_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 
Probability of Default Modeling 12 
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 
Probability of Default Modeling 13 
Category Factor Name AR* 
*AR = Accuracy Ratio 
Default Rate 
Relationship 
Missing 
% 
Recommend 
ation 
Comments 
Sustainability/ 
Profitability 
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%  
Asset/Liability 
Management 
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 LiabtoAssets 
Financial_Expense_ratio 46% Strong 1%  
LiabtoNetWorth 12% Good 2%  High correlation with LiabtoAssets 
LiabtoAssets 13% Good 2%  
LiabtoEBITDA -7% Counterintuitive 2%  
CashtoLiabs 19% Good 0%  
Growth 
Total_RevenueGrowth 39%  High missing % 
GrossPortfolioGrowth 38%  High missing % 
Size 
LoanPortfolio_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) 
Probability of Default Modeling 14 
Category Factor Name AR* 
Default Rate 
Relationship 
Missing 
% 
Recommend 
ation 
Comments 
Efficiency/ 
Productivity 
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 borrowers 
Pct_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
CAP Curve of PAR_30_Ratio 
Probability of Default Modeling 15 
PAR 30 Ratio 
Key statistics: Relative Entropy 0.96, Accuracy Ratio -8% 
3.0% 
2.5% 
2.0% 
1.5% 
1.0% 
0.5% 
0.0% 
250 
200 
150 
100 
50 
0 
Frequencies and Default Rates for PAR_30_Ratio 
missing 0.05 to High 0.025 to 0.05 0.01 to 0.025 0 to 0.01 
Default Rate 
» This factor performs inadequately with no discriminatory power 
1 
0.75 
0.5 
0.25 
» Counterintuitive relationship between the responses and the default rate 
Frequency 
Answer 
 
0 
0 0.25 0.5 0.75 1 
% Default 
% Population
CAP Curve of PAR_180_Ratio 
Probability of Default Modeling 16 
PAR 180 Ratio 
Key statistics: Relative Entropy 0.96, Accuracy Ratio -32% 
7.5% 
7.0% 
6.5% 
6.0% 
5.5% 
5.0% 
4.5% 
4.0% 
3.5% 
3.0% 
2.5% 
2.0% 
1.5% 
1.0% 
0.5% 
0.0% 
250 
200 
150 
100 
50 
0 
Frequencies and Default Rates for PAR_180_Ratio 
missing 0.012 to High 0.003 to 0.012 >0 to 0.003 0 to 0 
Default Rate 
1 
0.75 
0.5 
0.25 
» Counterintuitive relationship between the responses and the default rate 
Frequency 
Answer 
 
0 
0 0.25 0.5 0.75 1 
% Default 
% Population
Avg_outstanding_loansize 
Key statistics: Relative Entropy 0.95, Accuracy Ratio 4% ? 
CAP Curve of Avg_outstanding_loansize 
0 0.25 0.5 0.75 1 
% Population 
Probability of Default Modeling 17 
% Default 
4.0% 
3.5% 
3.0% 
2.5% 
2.0% 
1.5% 
1.0% 
0.5% 
0.0% 
250 
200 
150 
100 
50 
0 
Frequencies and Default Rates for Avg_outstanding_loansize 
missing < 500 500 to 1500 1500 to 2500 2500 to 4000 4000 to High 
Default Rate 
» This factor performs inadequately with low discriminatory power 
1 
0.75 
0.5 
0.25 
0 
» Weak relationship between the responses and the default rate i.e. higher the score lower the default rate 
Frequency 
Answer
Candidate Quantitative 
Probability of Default Modeling 18 
Factors: 
4 Multi Factor Analysis
Starting with 21 Candidate Factors from SFA 
Probability of Default Modeling 19 
Section Factor Name AR 
Default Rate 
Relationship 
Comments 
Sustainability/ 
Profitability 
GrossMargin 36% Good 
InterestCoverage 37% Good 
Asset/Liability 
Management 
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 
Efficiency/ 
Productivity 
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 Quality 
OnTime_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_Volume 
Key statistics: Relative Entropy 0.88, Accuracy Ratio 29%  
CAP Curve of Pct_Female_Clients_Volume 
0 0.25 0.5 0.75 1 
% Population 
Probability of Default Modeling 20 
1 
0.75 
0.5 
0.25 
% Default 
Frequencies and Default Rates for Pct_Female_Clients_Volume 
5.0% 
4.5% 
4.0% 
3.5% 
3.0% 
2.5% 
2.0% 
1.5% 
1.0% 
0.5% 
0.0% 
600 
500 
400 
300 
200 
100 
0 
missing 0 to 0.35 0.35 to High 
Default Rate 
» This factor performs adequately with moderate discriminatory power 
0 
» Good relationship between the responses and the default rate i.e. higher the score lower the default rate 
Frequency 
Answer
Probability of Default Modeling 21 
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_Volum 
e 
Pct_Female_Clients_Volu 
me 
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%
Model Number of Factors Significance Level1 AR2 Comments 
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 
Probability of Default Modeling 22 
Logistic Regression Models 
Model 1 5 P Value <= 0.05 69.5% 
Model 2 8 P Value <= 0.1 77.8% 
1. For estimated coefficients and p value refer appendix 1 
2. AR = Accuracy Ratio
Probability of Default Modeling 23 
Beta Model – Factor Weights 
Section Factor Name Factor AR Model 1 Model 2 Model 3 Model 4 Model 5 
Sustainability/Profitability 
GrossMargin 36% 
InterestCoverage 37% 
Asset/Liability Management 
Financial_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% 
Efficiency/ 
Productivity 
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 Quality 
OnTime_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 5 
Model 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 Factors 
5 
Probability of Default Modeling 24
Probability of Default Modeling 25 
New Data Preparation 
Quantitative (non SPA Data) 
Total Statements: 731 
Unique MFIs: 249 
Defaults: 16 
Qualitative (SPA Data) 
Total Statements : 167 
Unique MFIs: 167 
Defaults: 10 
Total Statements: 506 
Unique MFIs: 161 
Defaults: 10 
(1.98%) 
Quantitative model prepared 
as before. Data for ‘Total 
Revenue Growth’ and ‘Gross 
Portfolio Growth’ updated for 
missing values 
Total Statements : 161 
Unique MFIs: 161 
Defaults: 10 
Remove statements from the 
quantitative data where MFI’s 
are not common to SPA 
(Qualitative) data 
225 (30.8%) statements dropped 
Combined 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 
Qualitative Model was 
prepared on this data
Candidate social factors were based on availability of reliable data. Data sourced from 
the MIX and analyzed with Moody’s SPA 
Low AR 
Probability of Default Modeling 26 
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
Code of Ethics 
Frequencies and Default Rates for Policies included in 
15% 
10% 
5% 
Code of Ethics 
Probability of Default Modeling 27 
Rejected Social Variables 
27 
20% 
15% 
10% 
5% 
0% 
100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 
Pricing Transparency 
Frequencies and Default Rates for Pricing 
Transparency Practices 
Less than equal to 
0.5 0.5 to 0.9 Greater than 0.9 
Default Rate 
Frequency 
Answer 
0% 
80 
70 
60 
50 
40 
30 
20 
10 
0 
Less than 
equal to 0.2 0.2 to 0.6 0.6 to 0.9 
Greater than 
0.9 
Default Rate 
Frequency 
Answer
Range of Products Offered 
Frequencies and Default Rates for Range of Products 
10% 
5% 
offered 
CAP Curve of Range of Products offered 
Probability of Default Modeling 28 
Accepted Social Variables 
28 
Debt Collection Practices 
Frequencies and Default Rates for Debt Collection 
20% 
15% 
10% 
5% 
0% 
100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 
Practices 
Less than 
equal to 0.1 0.1 to 0.45 0.45 to 0.9 
Greater than 
0.9 
Default Rate 
Frequency 
Answer 
1 
0.75 
0.5 
0.25 
0 
CAP Curve of Debt Collection Practices 
0 0.25 0.5 0.75 1 
% Default 
% Population 
0% 
80 
70 
60 
50 
40 
30 
20 
10 
0 
Less than 
equal to 0.2 0.2 to 0.4 0.4 to 0.6 0.6 to 0.8 
Greater 
than 0.8 
Default Rate 
Frequency 
Answer 
1 
0.75 
0.5 
0.25 
0 
0 0.25 0.5 0.75 1 
% Default 
% Population
29 
Probability of Default Modeling 29 
Combined Model 
Combining the Quantitative and Qualitative factors give an AR of 79.0% 
Section Section Weight Factor Factor Weight Final Weight 
Cash 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% 
Quantitative Score 64% 
Qualitative Score 35.6%
Structural Component 
6 
Probability of Default Modeling 30
Qualitative factors are not necessarily judgmental, but 
cannot be empirically confirmed by the data 
Probability of Default Modeling 31 
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
© 2012 Moody’s Analytics, Inc. and/or its licensors and affiliates (collectively, “MOODY’S”). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY 
COPYRIGHT LAW AND NONE OF SUCH INFORMATION MAY BE COPIED OR OTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED, 
DISSEMINATED, REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY SUCH PURPOSE, IN WHOLE OR IN PART, IN ANY FORM OR MANNER OR 
BY ANY MEANS WHATSOEVER, BY ANY PERSON WITHOUT MOODY’S PRIOR WRITTEN CONSENT. All information contained herein is obtained by MOODY’S from sources 
believed by it to be accurate and reliable. Because of the possibility of human or mechanical error as well as other factors, however, all information contained herein is provided “AS 
IS” without warranty of any kind. Under no circumstances shall MOODY’S have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting 
from, or relating to, any error (negligent or otherwise) or other circumstance or contingency within or outside the control of MOODY’S or any of its directors, officers, employees or 
agents in connection with the procurement, collection, compilation, analysis, interpretation, communication, publication or delivery of any such information, or (b) any direct, indirect, 
special, consequential, compensatory or incidental damages whatsoever (including without limitation, lost profits), even if MOODY’S is advised in advance of the possibility of such 
damages, resulting from the use of or inability to use, any such information. The credit ratings, financial reporting analysis, projections, and other observations, if any, constituting part 
of the information contained herein are, and must be construed solely as, statements of opinion and not statements of fact or recommendations to purchase, sell or hold any 
securities. NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY, TIMELINESS, COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR 
PURPOSE OF ANY SUCH RATING OR OTHER OPINION OR INFORMATION IS GIVEN OR MADE BY MOODY’S IN ANY FORM OR MANNER WHATSOEVER. Each rating or 
other opinion must be weighed solely as one factor in any investment decision made by or on behalf of any user of the information contained herein, and each such user must 
accordingly make its own study and evaluation of each security and of each issuer and guarantor of, and each provider of credit support for, each security that it may consider 
purchasing, holding, or selling. 
Probability of Default Modeling 32

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Moody's ---How Social Performance Impacts Financial Resilience and Default Probabilities

  • 1. Probability of Default for Microfinance Institutions May 2014
  • 2. Probability of Default Modeling 2 Overview 1
  • 3. Probability of Default Modeling 3 Probability of Default by Moody’s Grade Importance of Calculating PD  Pricing loans  Investor return  Portfolio risk
  • 4. Probability of Default Modeling 4 Deciding on Number of Factors for Scorecard Helps Accuracy Hurts Accuracy 4 Developing a Model Marginal Contribution to Accuracy 4 8 12 16 20 n Number of Factors in Scorecard Pos 0 Neg Recommended Range For model building purposes, we may want to have more factors initially, with understanding that some will be discarded
  • 5. Data Preparation Probability of Default Modeling 5 2
  • 6. Probability of Default Modeling 6 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 .
  • 7. Probability of Default Modeling 7 Defining Default Methodology 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
  • 8. Probability of Default Modeling 8 Basic Checks All 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 FINAL DATA SAMPLE (Before Basic Checks) Total Statements: 868 Unique MFIs: 293 Defaults: 16 (1.84%) 1. Refer appendix 11 for details of basic check analysis Basic Checks1 34 (3.9%) statements dropped FINAL SAMPLE FOR MODEL DEVELOPMENT Total Statements: 834 Unique MFIs: 292 Defaults: 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
  • 9. Candidate Quantitative Factors: Single Factor Probability of Default Modeling 9 Analysis 3
  • 10. The available data yields 46 potential factors for single factor analysis 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 Probability of Default Modeling 10 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_Liabs Funding_expense_ratio Interest and fee expense on all funding liabilities (v3210 )/Gross_Loan_Portfolio LiabtoNetWorth 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 Growth Total_RevenueGrowth (Total_Revenue-Total_Revenue_Prev)/Total_Revenue_Prev GrossPortfolioGrowth (Gross_Loan_Portfolio-Gross_Loan_Portfolio_Prev)/Gross_Loan_Portfolio_Prev Size LoanPortfolio_CPIAdj (229.601/CPI_INDEX)*Gross_Loan_Portfolio Total_Assets_CPIAdj (229.601/CPI_INDEX)*Total_Assets Avg_outstanding_loansize (229.601/CPI_INDEX)*Gross_Loan_Portfolio/nb outstanding loans (v8040)
  • 11. The available data yields 46 potential factors for single factor analysis (cont’d) Probability of Default Modeling 11 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_Portfolio Financial_Expense_ratio Financial_Costs/Gross_Loan_Portfolio Cost_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_Portfolio PAR_180_Ratio Of which portfolio at risk above 180 days (v7100)/Gross_Loan_Portfolio OnTime_Portfolio On-time portfolio (v7010)/Gross_Loan_Portfolio Writeoff_Ratio Write offs (v7140)/Gross_Loan_Portfolio Risk_coverage_ratio Loan loss reserve – Audited (v1220)/ Portfolio at risk above 30 days (v7030) LoanLossReserve_Ratio Loan loss reserve – Audited (v1220)/Gross_Loan_Portfolio Arrears_rate Portfolio in arrears (v7130)/Gross_Loan_Portfolio Pct_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_Portfolio Pct_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)
  • 12. 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 Probability of Default Modeling 12 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
  • 13. Single Factor Analysis Performance: 21 factors recommended for further exploration in MFA Probability of Default Modeling 13 Category Factor Name AR* *AR = Accuracy Ratio Default Rate Relationship Missing % Recommend ation Comments Sustainability/ Profitability 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%  Asset/Liability Management 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 LiabtoAssets Financial_Expense_ratio 46% Strong 1%  LiabtoNetWorth 12% Good 2%  High correlation with LiabtoAssets LiabtoAssets 13% Good 2%  LiabtoEBITDA -7% Counterintuitive 2%  CashtoLiabs 19% Good 0%  Growth Total_RevenueGrowth 39%  High missing % GrossPortfolioGrowth 38%  High missing % Size LoanPortfolio_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
  • 14. Single Factor Analysis Performance : 21 factors recommended for further exploration in MFA (cont’d) Probability of Default Modeling 14 Category Factor Name AR* Default Rate Relationship Missing % Recommend ation Comments Efficiency/ Productivity 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 borrowers Pct_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
  • 15. CAP Curve of PAR_30_Ratio Probability of Default Modeling 15 PAR 30 Ratio Key statistics: Relative Entropy 0.96, Accuracy Ratio -8% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 250 200 150 100 50 0 Frequencies and Default Rates for PAR_30_Ratio missing 0.05 to High 0.025 to 0.05 0.01 to 0.025 0 to 0.01 Default Rate » This factor performs inadequately with no discriminatory power 1 0.75 0.5 0.25 » Counterintuitive relationship between the responses and the default rate Frequency Answer  0 0 0.25 0.5 0.75 1 % Default % Population
  • 16. CAP Curve of PAR_180_Ratio Probability of Default Modeling 16 PAR 180 Ratio Key statistics: Relative Entropy 0.96, Accuracy Ratio -32% 7.5% 7.0% 6.5% 6.0% 5.5% 5.0% 4.5% 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 250 200 150 100 50 0 Frequencies and Default Rates for PAR_180_Ratio missing 0.012 to High 0.003 to 0.012 >0 to 0.003 0 to 0 Default Rate 1 0.75 0.5 0.25 » Counterintuitive relationship between the responses and the default rate Frequency Answer  0 0 0.25 0.5 0.75 1 % Default % Population
  • 17. Avg_outstanding_loansize Key statistics: Relative Entropy 0.95, Accuracy Ratio 4% ? CAP Curve of Avg_outstanding_loansize 0 0.25 0.5 0.75 1 % Population Probability of Default Modeling 17 % Default 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 250 200 150 100 50 0 Frequencies and Default Rates for Avg_outstanding_loansize missing < 500 500 to 1500 1500 to 2500 2500 to 4000 4000 to High Default Rate » This factor performs inadequately with low discriminatory power 1 0.75 0.5 0.25 0 » Weak relationship between the responses and the default rate i.e. higher the score lower the default rate Frequency Answer
  • 18. Candidate Quantitative Probability of Default Modeling 18 Factors: 4 Multi Factor Analysis
  • 19. Starting with 21 Candidate Factors from SFA Probability of Default Modeling 19 Section Factor Name AR Default Rate Relationship Comments Sustainability/ Profitability GrossMargin 36% Good InterestCoverage 37% Good Asset/Liability Management 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 Efficiency/ Productivity 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 Quality OnTime_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
  • 20. Pct_Female_Clients_Volume Key statistics: Relative Entropy 0.88, Accuracy Ratio 29%  CAP Curve of Pct_Female_Clients_Volume 0 0.25 0.5 0.75 1 % Population Probability of Default Modeling 20 1 0.75 0.5 0.25 % Default Frequencies and Default Rates for Pct_Female_Clients_Volume 5.0% 4.5% 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 600 500 400 300 200 100 0 missing 0 to 0.35 0.35 to High Default Rate » This factor performs adequately with moderate discriminatory power 0 » Good relationship between the responses and the default rate i.e. higher the score lower the default rate Frequency Answer
  • 21. Probability of Default Modeling 21 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_Volum e Pct_Female_Clients_Volu me 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%
  • 22. Model Number of Factors Significance Level1 AR2 Comments 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 Probability of Default Modeling 22 Logistic Regression Models Model 1 5 P Value <= 0.05 69.5% Model 2 8 P Value <= 0.1 77.8% 1. For estimated coefficients and p value refer appendix 1 2. AR = Accuracy Ratio
  • 23. Probability of Default Modeling 23 Beta Model – Factor Weights Section Factor Name Factor AR Model 1 Model 2 Model 3 Model 4 Model 5 Sustainability/Profitability GrossMargin 36% InterestCoverage 37% Asset/Liability Management Financial_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% Efficiency/ Productivity 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 Quality OnTime_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 5 Model 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
  • 24. Candidate Social Factors 5 Probability of Default Modeling 24
  • 25. Probability of Default Modeling 25 New Data Preparation Quantitative (non SPA Data) Total Statements: 731 Unique MFIs: 249 Defaults: 16 Qualitative (SPA Data) Total Statements : 167 Unique MFIs: 167 Defaults: 10 Total Statements: 506 Unique MFIs: 161 Defaults: 10 (1.98%) Quantitative model prepared as before. Data for ‘Total Revenue Growth’ and ‘Gross Portfolio Growth’ updated for missing values Total Statements : 161 Unique MFIs: 161 Defaults: 10 Remove statements from the quantitative data where MFI’s are not common to SPA (Qualitative) data 225 (30.8%) statements dropped Combined 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 Qualitative Model was prepared on this data
  • 26. Candidate social factors were based on availability of reliable data. Data sourced from the MIX and analyzed with Moody’s SPA Low AR Probability of Default Modeling 26 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
  • 27. Code of Ethics Frequencies and Default Rates for Policies included in 15% 10% 5% Code of Ethics Probability of Default Modeling 27 Rejected Social Variables 27 20% 15% 10% 5% 0% 100 90 80 70 60 50 40 30 20 10 0 Pricing Transparency Frequencies and Default Rates for Pricing Transparency Practices Less than equal to 0.5 0.5 to 0.9 Greater than 0.9 Default Rate Frequency Answer 0% 80 70 60 50 40 30 20 10 0 Less than equal to 0.2 0.2 to 0.6 0.6 to 0.9 Greater than 0.9 Default Rate Frequency Answer
  • 28. Range of Products Offered Frequencies and Default Rates for Range of Products 10% 5% offered CAP Curve of Range of Products offered Probability of Default Modeling 28 Accepted Social Variables 28 Debt Collection Practices Frequencies and Default Rates for Debt Collection 20% 15% 10% 5% 0% 100 90 80 70 60 50 40 30 20 10 0 Practices Less than equal to 0.1 0.1 to 0.45 0.45 to 0.9 Greater than 0.9 Default Rate Frequency Answer 1 0.75 0.5 0.25 0 CAP Curve of Debt Collection Practices 0 0.25 0.5 0.75 1 % Default % Population 0% 80 70 60 50 40 30 20 10 0 Less than equal to 0.2 0.2 to 0.4 0.4 to 0.6 0.6 to 0.8 Greater than 0.8 Default Rate Frequency Answer 1 0.75 0.5 0.25 0 0 0.25 0.5 0.75 1 % Default % Population
  • 29. 29 Probability of Default Modeling 29 Combined Model Combining the Quantitative and Qualitative factors give an AR of 79.0% Section Section Weight Factor Factor Weight Final Weight Cash 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% Quantitative Score 64% Qualitative Score 35.6%
  • 30. Structural Component 6 Probability of Default Modeling 30
  • 31. Qualitative factors are not necessarily judgmental, but cannot be empirically confirmed by the data Probability of Default Modeling 31 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
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