Download Probability of Default for Microfinance Institutions

Probability of Default for
Microfinance Institutions
SPTF Annual Meeting - Senegal
May 2014
2
Moody’s and SPI4
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
Probability of Default Modeling
2
Agenda
Modeling probability of default (PD)
Social performance and default probability
Probability of Default Modeling
3
1
Overview
Probability of Default Modeling
4
Probability of Default by Moody’s Grade
Importance of Calculating PD

Pricing loans

Investor return

Portfolio risk
Probability of Default Modeling
5
6
Developing a Model
Deciding on Number of Factors for Scorecard
Marginal Contribution to Accuracy
Pos
Helps Accuracy
Recommended Range
0
For model building purposes,
we may want to have more
factors initially, with
understanding that some will
be discarded
Hurts Accuracy
Neg
4
8
12
16
20
n
Number of Factors in Scorecard
Probability of Default Modeling
6
2
Data Preparation
Probability of Default Modeling
7
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
8
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
9
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)
» Total Current Assets < 0
» Total Non Current Assets < 0
» Depreciation and Amortization < 0
» Total Operating Expenses < 0
»
Total Long Term Liabilities < 0
» Cash and Equivalents < 0
FINAL DATA SAMPLE (Before Basic Checks)
Total Statements:
868
Unique MFIs:
293
Defaults:
16 (1.84%)
Basic Checks1
34 (3.9%) statements dropped
FINAL SAMPLE FOR MODEL DEVELOPMENT
Total Statements:
834
Unique MFIs:
292
Defaults:
16 (1.92%)
1. Refer appendix 11 for details of basic check analysis
Probability of Default Modeling
10
3
Candidate Quantitative Factors:
Single Factor Analysis
Probability of Default Modeling
11
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
Category
Sustainability/Profitability
Factor Name
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_AssetsTotal_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
CashtoLiabs
RevenuetoTotalAsts
Total_RevenueGrowth
Total_Revenue/Interest and fee expense on all funding liabilities (v3210 )
Cash & Cash Equivalents – Audited (v1110)/Total_Liabs
(Total_Revenue - Financial_Costs - Loan_Loss_Provision Operating_Expense)/Gross_Loan_Portfolio
(Total_Revenue + Non_Operating_Income - Financial_Costs - Loan_Loss_Provision Operating_Expense - Non_Operating_Expense)/Gross_Loan_Portfolio
Current_Assets/Current_Liabs
Interest and fee expense on all funding liabilities (v3210 )/Gross_Loan_Portfolio
Total_Liabs/(Total_Assets-Total_Liabs)
Total_Liabs/Total_Assets
Total_Liabs/(Total_Revenue - Financial_Costs - Loan_Loss_Provision - Operating_Expense +
Depreciation and Amortization(v3530))
Total_Revenue/Total_Assets
(Total_Revenue-Total_Revenue_Prev)/Total_Revenue_Prev
GrossPortfolioGrowth
(Gross_Loan_Portfolio-Gross_Loan_Portfolio_Prev)/Gross_Loan_Portfolio_Prev
LoanPortfolio_CPIAdj
Total_Assets_CPIAdj
Avg_outstanding_loansize
(229.601/CPI_INDEX)*Gross_Loan_Portfolio
(229.601/CPI_INDEX)*Total_Assets
(229.601/CPI_INDEX)*Gross_Loan_Portfolio/nb outstanding loans (v8040)
Yield_on_Loan_Portfolio
Gross_Yield_on_Loan_Portfolio
Asset/Liability
Management
CurrentRatio
Funding_expense_ratio
LiabtoNetWorth
LiabtoAssets
LiabtoEBITDA
Growth
Size
Calculation
Probability of Default Modeling
12
The available data yields 46 potential factors for single
factor analysis (cont’d)
Category
Factor Name
Loan_officer_productivity
Calculation
number of active borrowers (v8050)/ number of loan officers (v8010)
Personnel_productivity
Branch_Productivity
Type_Of_Loans
number of active borrowers (v8050)/ Number of employees (v8020)
number of active borrowers (v8050)/ Number of branches (v8030)
(229.601/CPI_INDEX)*(Total_Revenue + Non_Operating_Income - Financial_Costs Loan_Loss_Provision - Operating_Expense - Non_Operating_Expense)/number of loan officers
(v8010)
(229.601/CPI_INDEX)*(Total_Revenue + Non_Operating_Income - Financial_Costs Loan_Loss_Provision - Operating_Expense - Non_Operating_Expense)/ Number of employees
(v8020)
(229.601/CPI_INDEX)*(Total_Revenue + Non_Operating_Income - Financial_Costs Loan_Loss_Provision - Operating_Expense - Non_Operating_Expense)/Number of branches
(v8030)
Number of loans outstanding(v8040)/number of active borrowers (v8050)
Operating_Expense/Gross_Loan_Portfolio
Financial_Costs/Gross_Loan_Portfolio
(229.601/CPI_INDEX)*Operating_Expense/number of active borrowers (v8050)
(229.601/CPI_INDEX)*Gross_Loan_Portfolio/number of loan officers (v8010)
Portfolio at risk above 30 days (v7030)/Gross_Loan_Portfolio
Of which portfolio at risk above 180 days (v7100)/Gross_Loan_Portfolio
On-time portfolio (v7010)/Gross_Loan_Portfolio
Write offs (v7140)/Gross_Loan_Portfolio
Loan loss reserve – Audited (v1220)/ Portfolio at risk above 30 days (v7030)
Loan loss reserve – Audited (v1220)/Gross_Loan_Portfolio
Portfolio in arrears (v7130)/Gross_Loan_Portfolio
reprogrammed and refinanced loans (v7115)/Gross_Loan_Portfolio
mean(v8174,v8184,v7914,v7924,v7934,v7944)
sum(Urban clients - volume of portfolio (v8410), Semi-Urban clients - volume of portfolio
(v8420),0)/Gross_Loan_Portfolio
Female clients - volume of portfolio (v8320)/Gross_Loan_Portfolio
Financial revenue from investments – Audited (v3120)/Total_Revenue
sum(Self-help groups (v8250), Solidarity groups (v8260), Communal banks loans/Self-help groups –
volume (v8270))/Gross_Loan_Portfolio
6-nmiss(v8110,v8120,v8130,v8140,v8150,v8160)
Loans_to_Ind_Types
10-nmiss(v8510,v8520,v8530,v8540,v8542,v8544,v8546,v8548,v8549,v8550)
PBT_per_loan_officer
Efficiency/Productivity
PBT_per_employee
PBT_per_branch
Portfolio quality
loans_per_borrower
Operating_expense_ratio
Financial_Expense_ratio
Cost_per_borrower
Avg_portfolio_per_credit_officer
PAR_30_Ratio
PAR_180_Ratio
OnTime_Portfolio
Writeoff_Ratio
Risk_coverage_ratio
LoanLossReserve_Ratio
Arrears_rate
Pct_Refinanced
Avg_maturity_of_loans
Pct_Urban_Clients_Volume
Others
Pct_Female_Clients_Volume
Pct_Revenue_From_Investments
Pct_Group_Loans
Probability of Default Modeling
13
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
Probability of Default Modeling
14
Single Factor Analysis Performance: 21 factors
recommended for further exploration in MFA
Category
Sustainability/
Profitability
Asset/Liability
Management
Growth
Size
Factor Name
AR*
Default Rate
Relationship
Missing Recommend
%
ation
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%
Financial_Expense_ratio
46%
Strong
1%
LiabtoNetWorth
12%
Good
2%
LiabtoAssets
13%
Good
2%
LiabtoEBITDA
-7%
Counterintuitive
2%
CashtoLiabs
19%
Good
0%
Total_RevenueGrowth
39%
GrossPortfolioGrowth
38%
LoanPortfolio_CPIAdj
-13%
Counterintuitive
0%
Total_Assets_CPIAdj
-14%
Counterintuitive
2%
4%
Weak
5%
Avg_outstanding_loansize




















Comments
High correlation with LiabtoAssets
High correlation with LiabtoAssets
High missing %
High missing %
Used as a proxy for Income level of the borrowers
*AR = Accuracy Ratio
Probability of Default Modeling
15
Single Factor Analysis Performance : 21 factors
recommended for further exploration in MFA (cont’d)
Category
Efficiency/
Productivity
Factor Name
AR*
Default Rate
Relationship
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%
PAR_30_Ratio
-8%
Counterintuitive
4%
PAR_180_Ratio
-32%
Counterintuitive
8%
1%
Good
4%
Writeoff_Ratio
8%
Moderate
7%
Risk_coverage_ratio
11%
Moderate
6%
LoanLossReserve_Ratio
-1%
Moderate
2%
Arrears_rate
-2%
Weak
9%
OnTime_Portfolio
Portfolio
Quality
Others
Missing Recommend
%
ation
Pct_Refinanced
14%
Avg_maturity_of_loans
23%
loans_per_borrower
32%
Strong
6%
Pct_Urban_Clients_Volume
23%
Good
0%
Pct_Female_Clients_Volume
29%
Good
5%
Pct_Revenue_From_Investments
-1%
Counterintuitive
Pct_Group_Loans
1%
20%
Type_Of_Loans
3%
Moderate
0%
Loans_to_Ind_Types
10%
Good
0%


























Comments
High missing %
High missing %
Used as a proxy for Debt to Income ratio of borrowers
High missing %
Low diversity of responses and very low accuracy ratio
Used as a proxy for portfolio diversity
Probability of Default Modeling
16
PAR 30 Ratio

Key statistics: Relative Entropy 0.96, Accuracy Ratio -8%
Frequencies and Default Rates for PAR_30_Ratio
0.025 to 0.05
0.01 to 0.025
0 to 0.01
3.0%
200
2.5%
2.0%
150
1.5%
100
1.0%
50
0.5%
0
0.0%
1
0.75
% Default
0.05 to High
250
Default Rate
Frequency
missing
CAP Curve of PAR_30_Ratio
0.5
0.25
0
0
Answer
0.25
0.5
0.75
1
% Population
» This factor performs inadequately with no discriminatory power
» Counterintuitive relationship between the responses and the default rate
Probability of Default Modeling
17
PAR 180 Ratio

Key statistics: Relative Entropy 0.96, Accuracy Ratio -32%
Frequencies and Default Rates for PAR_180_Ratio
0.003 to 0.012
0 to 0
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%
200
Frequency
>0 to 0.003
150
100
50
0
1
0.75
% Default
0.012 to High
Default Rate
missing
250
CAP Curve of PAR_180_Ratio
0.5
0.25
0
0
Answer
0.25
0.5
0.75
1
% Population
» Counterintuitive relationship between the responses and the default rate
Probability of Default Modeling
18
Avg_outstanding_loansize
?
Key statistics: Relative Entropy 0.95, Accuracy Ratio 4%
Frequencies and Default Rates for Avg_outstanding_loansize
500 to 1500
2500 to 4000 4000 to High
1
4.0%
3.5%
200
Frequency
1500 to 2500
3.0%
2.5%
150
2.0%
100
1.5%
1.0%
50
0.75
% Default
< 500
Default Rate
missing
250
CAP Curve of Avg_outstanding_loansize
0.5
0.25
0.5%
0
0.0%
0
0
Answer
0.25
0.5
0.75
1
% Population
» 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
Probability of Default Modeling
19
4
Candidate Quantitative Factors:
Multi Factor Analysis
Probability of Default Modeling
20
Starting with 21 Candidate Factors from SFA
Section
Sustainability/
Profitability
Asset/Liability
Management
Size
Efficiency/
Productivity
Portfolio Quality
GrossMargin
InterestCoverage
36%
37%
Default Rate
Relationship
Good
Good
Financial_Expense_ratio
46%
Strong
LiabtoAssets
CashtoLiabs
13%
19%
Good
Good
Avg_outstanding_loansize
4%
Weak
Loan_officer_productivity
Personnel_productivity
Branch_Productivity
PBT_per_branch
RevenuetoTotalAsts
Operating_expense_ratio
Cost_per_borrower
23%
27%
18%
3%
12%
28%
19%
Good
Good
Good
Moderate
Moderate
Good
Good
Avg_portfolio_per_credit_officer
6%
Good
OnTime_Portfolio
Writeoff_Ratio
Risk_coverage_ratio
loans_per_borrower
1%
8%
11%
32%
Good
Moderate
Moderate
Strong
Pct_Urban_Clients_Volume
23%
Good
Pct_Female_Clients_Volume
29%
Good
Loans_to_Ind_Types
10%
Good
Factor Name
AR
Comments
Used as a proxy for Income level of the borrowers
Used as a proxy for Debt to Income ratio of borrowers
Others
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
Probability of Default Modeling
21
Pct_Female_Clients_Volume

Key statistics: Relative Entropy 0.88, Accuracy Ratio 29%
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%
500
Frequency
0.35 to High
400
300
200
100
0
1
0.75
% Default
0 to 0.35
Default Rate
missing
600
CAP Curve of Pct_Female_Clients_Volume
0.5
0.25
0
0
Answer
0.25
0.5
0.75
1
% Population
» 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
Probability of Default Modeling
22
1%
4%
-6%
-3% -12% -1% -15% -7%
Funding_expense_ratio
34% 73% 13% 100% 3%
3%
-32% 26%
8%
5%
6%
15%
-7%
-9%
75%
LiabtoNetWorth
48% 18%
5%
3% 100% 97% 34% -32% -3%
4%
-7% -18% 26%
-7%
25% 13% 30%
LiabtoAssets
48% 17%
6%
3%
6%
-4% -15% 24%
-7%
26% 13% 30%
RevenuetoTotalAsts
50% 13%
-2% -32% 34% 34% 100% -41%
Avg_outstanding_loansize
-27%
5%
-2%
26% -32% -30% -41% 100% -8% -14% -16% 14% -19%
3%
7%
Loan_officer_productivity
11% 10%
6%
8%
-3%
-1%
3%
-8% 100% 47% 22% 18%
-6%
2%
10% 23%
-4%
-6%
Personnel_productivity
22% 13%
1%
5%
4%
6%
20% -14% 47% 100% 26% 18%
2%
6%
9%
35%
7%
-7%
Branch_Productivity
9%
8%
4%
6%
-7%
-4%
12% -16% 22% 26% 100% 18% -13% -2%
11% 17%
3%
1%
PBT_per_branch
1%
13%
-6%
15% -18% -15%
0%
7%
loans_per_borrower
21%
5%
-3%
-7%
3%
12% 33% 14%
8%
Operating_expense_ratio
-10% -19% -12% -9%
0%
11%
4%
0%
Financial_Expense_ratio
58% 53%
-1%
-5% 100% 5%
-7%
Cost_per_borrower
27%
5%
-15% -7%
5% 100% 24%
portfolio_per_credit_officer
29%
-9%
-7% -30% 30% 30% 46% -48% -4%
OnTime_Portfolio
1%
-11% -9%
97% 100% 34% -30% -1%
20% 12%
0%
22%
14% 18% 18% 18% 100% -15%
5%
2%
-13% -15% 100% -10%
-7%
-7%
-8%
3%
2%
6%
-2%
5%
75% 25% 26% -15%
7%
10%
9%
11%
7%
-10% 100% -5%
3%
13% 13% 33% -29% 23% 35% 17% 22% 12%
1%
0%
-5% -17% -6%
Writeoff_Ratio
-10% -7% -10% 12%
-5%
-5% -24% -1%
Risk_coverage_ratio
11% 14%
Pct_Urban_Clients_Volume
0%
Pct_Female_Clients_Volume
Loans_to_Ind_Types
-2%
-9% -11% -7%
15%
-4%
2%
-3%
14% 15%
-9% -10% -7%
13%
-7%
7%
12% 14% 11%
-8%
-1%
1%
-5%
-5%
-7%
14%
-4%
0%
-5%
-5%
-6%
15%
-4%
-5% -24%
4%
-2%
20%
-8%
-29% -48% -17% -1%
0%
15% -41% -10%
-2%
3%
-1%
7%
-9%
7%
-3%
13% -10%
-10% 10%
3%
17%
-1%
22% -17% 11% 16% 26%
5%
2%
-8%
2%
-19%
5%
-1%
2%
-2%
-1%
6%
7%
16% 16%
3%
5%
-3%
-7% -30% -4%
-4%
7%
-3%
16% -13% 26%
-9%
3%
-17% 33% 11%
-7%
24% 100% 16%
-4%
1%
1%
-7%
1%
11% 14%
4%
7%
7%
16% 100% 43% 43% -13%
9%
9%
8%
0%
16%
-3%
-4%
43% 100% 14%
-9%
-8%
-3%
2%
2%
16% 16%
1%
43% 14% 100% 1%
-9% -10% 16%
14%
-5%
-5%
4%
0%
3%
7%
10% 26%
-7%
-6%
-2%
15%
-1%
-3%
3%
15%
2%
-7%
-8%
14% 15% 20% -41%
7%
13% 17%
2%
5%
-4%
-3%
7%
-1%
-4%
-8% -10% -4% -10% -1%
-8%
-1%
5%
0%
0%
7%
15% 13% 11%
-4%
5%
-8% -15% 33% 46%
26% 24% 22% -19% -6%
-4%
-7%
3%
-19% 53%
Loans_to_Ind_Types
6%
Pct_Female_Clients_Volu
me
5%
-2%
-10% 11%
Pct_Urban_Clients_Volum
e
13%
-2%
1%
Writeoff_Ratio
8%
6%
OnTime_Portfolio
10% 13%
5%
Cost_per_borrower
5%
10% 27% 100% 13%
21% -10% 58% 27% 29%
Risk_coverage_ratio
portfolio_per_credit_officer
Financial_Expense_ratio
Operating_expense_ratio
62% 100% 27% 73% 18% 17% 13%
CashtoLiabs
loans_per_borrower
PBT_per_branch
InterestCoverage
LiabtoAssets
1%
LiabtoNetWorth
9%
CashtoLiabs
100% 62% 10% 34% 48% 48% 50% -27% 11% 22%
InterestCoverage
GrossMargin
GrossMargin
Branch_Productivity
Personnel_productivity
Loan_officer_productivity
Avg_outstanding_loansize
RevenuetoTotalAsts
Funding_expense_ratio
Candidate Factor Correlation Matrix
-13% 20%
1%
5%
3%
-13% -13% -13% -9%
1% 100% -2%
-4%
-1%
5%
26% 20%
9%
-8%
1%
-2% 100% 2%
6%
-3%
-9%
9%
-3%
5%
-4%
-19% -2%
1%
2% 100%
Probability of Default Modeling
23
Logistic Regression Models
Model
Number of Factors
Significance Level1
AR2
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
Comments
» 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
1. For estimated coefficients and p value refer appendix 1
2. AR = Accuracy Ratio
Probability of Default Modeling
24
Beta Model – Factor Weights
Section
Sustainability/Profitability
Asset/Liability Management
Size
Efficiency/
Productivity
Portfolio Quality
Others
Factor Name
GrossMargin
InterestCoverage
Financial_Expense_ratio
LiabtoAssets
CashtoLiabs
Avg_outstanding_loansize
Loan_officer_productivity
Personnel_productivity
Branch_Productivity
PBT_per_branch
RevenuetoTotalAsts
Operating_expense_ratio
Cost_per_borrower
Avg_portfolio_per_credit_officer
OnTime_Portfolio
Writeoff_Ratio
Risk_coverage_ratio
loans_per_borrower
Pct_Urban_Clients_Volume
Pct_Female_Clients_Volume
Loans_to_Ind_Types
Number of Factors
Model AR
Factor AR
36%
37%
46%
13%
19%
4%
23%
27%
18%
3%
12%
28%
19%
6%
1%
8%
11%
32%
23%
29%
10%
Model 1
Model 2
Model 3
Model 4
Model 5
22.4%
14.0%
18.2%
32.1%
26.7%
15.5%
7.9%
13.7%
11.6%
14.8%
8.6%
17.8%
14.0%
16.0%
25.1%
21.2%
17.6%
15.2%
19.5%
26.7%
14.4%
7.8%
19.5%
24.2%
23.4%
18.2%
14.6%
19.3%
5
69.5%
8
77.8%
6
73.4%
4
65.5%
5
69.4%
» All models do not give any weight to sustainability/profitability and portfolio quality factors
Probability of Default Modeling
25
5
Candidate Social Factors
Probability of Default Modeling
26
New Data Preparation
Qualitative (SPA Data)
Quantitative (non SPA Data)
Total Statements:
Unique MFIs:
Defaults:
731
249
16
Quantitative model prepared
as before. Data for ‘Total
Revenue Growth’ and ‘Gross
Portfolio Growth’ updated for
missing values
Merging two datasets
Total Statements:
Unique MFIs:
Defaults:
(1.98%)
506
161
10
Total Statements :
Unique MFIs:
Defaults:
167
167
10
6 MFI dropped due to no
exact match with quant data
Total Statements :
Unique MFIs:
Defaults:
161
161
10
Qualitative Model was
prepared on this data
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
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
Probability of Default Modeling
27
Candidate Social Factors
Candidate social factors were based on availability of reliable data. Data sourced from
the MIX and analyzed with Moody’s SPA
Variable
ProbChiSq
Pricing Transparency Practices
0.463
AR
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
Voluntarily adopted consumer protection
standards
0.060
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%
17%
27%
Low AR
Probability of
chance
occurrence is
high
Probability of Default Modeling
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29
Rejected Social Variables
Pricing Transparency
Code of Ethics
Greater than 0.9
20%
15%
10%
5%
0%
Answer
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
15%
Default Rate
0.5 to 0.9
Frequencies and Default Rates for Policies included in
Code of Ethics
Frequency
100
90
80
70
60
50
40
30
20
10
0
Less than equal to
0.5
Default Rate
Frequency
Frequencies and Default Rates for Pricing
Transparency Practices
10%
5%
0%
Answer
Probability of Default Modeling
29
30
Accepted Social Variables
Range of Products Offered
Debt Collection Practices
Frequencies and Default Rates for Range of Products
offered
0.1 to 0.45
0.45 to 0.9
Greater than
0.9
20%
10%
5%
0%
Answer
CAP Curve of Debt Collection Practices
Frequency
Default Rate
15%
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
10%
5%
Default Rate
Less than
equal to 0.1
100
90
80
70
60
50
40
30
20
10
0
0%
Answer
CAP Curve of Range of Products offered
1
1
0.75
0.75
% Default
% Default
Frequency
Frequencies and Default Rates for Debt Collection
Practices
0.5
0.25
0.5
0.25
0
0
0
0.25
0.5
% Population
0.75
1
0
0.25
0.5
% Population
0.75
1
Probability of Default Modeling
30
31
Combined Model
Combining the Quantitative and Qualitative factors give an AR of 79.0%
Section
Quantitative Score
Qualitative Score
Section Weight Factor
Cash to Liabilities
Loans per borrower
64%
Operating expense ratio
Financial Expense ratio
Percent Female Clients Volume
Debt Collection Practices
35.6%
Range of Products offered
Factor Weight Final Weight
13.77%
8.9%
16.48%
10.6%
22.62%
14.6%
26.19%
16.9%
20.94%
13.5%
38.9%
13.9%
61.1%
21.8%
Probability of Default Modeling
31
6
Structural Component
Probability of Default Modeling
32
Qualitative factors are not necessarily judgmental, but
cannot be empirically confirmed by the data
Franchise
Operating
Environment
Systems
» Market position and
sustainability
» Macroeconomic
stability
» Market size and
geographic
diversification
» Regulatory strength
» Board independence
and governance
» Legal system and
corruption
» Financial reporting and
transparency
» Asset concentration
and earnings
diversification
» Audit process
» Strength of credit
scoring and risk
management
» Access to alternative
funding sources
Probability of Default Modeling
33
7
Impact of Probability of Default on
Recovery from Social Events
Probability of Default Modeling
34
35
Impact of Probability of Default on Recovery from Social
Events*
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
Probability of Default Modeling
35
36
Issues
» 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?
Probability of Default Modeling
36
37
Database
Social performance data were obtained from MIX and then cleaned and sorted by Moody’s according to the SPA.
Probability of Default Modeling
37
38
Relationship Between PD and SPA Grade
No direct relationship between PD and SPA
Probability of Default Modeling
38
39
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
Probability of Default Modeling
39
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Probability of Default Modeling
40