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

Probability of Default for Microfinance Institutions

May 2014

Probability of Default Modeling 2

Overview

1


Probability of Default by Moody’s Grade

Importance of Calculating PD

Pricing loans

Investor return

Portfolio risk


Hurts Accuracy

Helps Accuracy

4

Developing a ModelM

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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 Preparation

2


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: 868

Unique MFIs: 293

Defaults: 16 (1.84%)

Basic Checks1

34 (3.9%) statements dropped

FINAL SAMPLE FOR MODEL DEVELOPMENT


Unique MFIs: 292


» 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 Analysis

3


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

LiabtoEBITDATotal_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_Prev

GrossPortfolioGrowth (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_Volumesum(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_Loanssum(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 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%

GrowthTotal_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)

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 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


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%

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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%

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Frequencies and Default Rates for PAR_180_Ratio

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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

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CAP Curve of Avg_outstanding_loansize

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Frequencies and Default Rates for Avg_outstanding_loansize


Candidate Quantitative Factors: Multi Factor Analysis

4


Starting with 21 Candidate Factors from SFA

Section Factor Name ARDefault 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

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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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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 Factors

5


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


Unique MFIs: 161


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 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

Total Statements : 161

Unique MFIs: 161

Defaults: 10Qualitative 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


27

Rejected Social Variables

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

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Frequencies and Default Rates for Pricing Transparency Practices

Pricing Transparency

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Frequencies and Default Rates for Policies included in Code of Ethics

Code of Ethics


28

Accepted Social Variables

Debt Collection Practices

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0102030405060708090

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Less thanequal to 0.1 0.1 to 0.45 0.45 to 0.9

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Frequencies and Default Rates for Debt Collection Practices

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CAP Curve of Debt Collection Practices

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Frequencies and Default Rates for Range of Products offered

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CAP Curve of Range of Products offered

Range of Products Offered


29

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 Component

6


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

FranchiseOperating

EnvironmentSystems

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

Economy & Finance

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