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

THE ROLE OF A MICROFINANCE BUREAU Regional Conference on Credit Reporting Systems in Africa. Co-organized by the World Bank and the New Partnership for Africa’s Development. Lilian Simbaqueba LiSim Group President. OBJECTIVE.

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

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  1. THE ROLE OF A MICROFINANCE BUREAURegional Conference on Credit Reporting Systems in Africa Co-organized by the World Bank and the New Partnership for Africa’s Development Lilian Simbaqueba LiSim Group President

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

  3. Are the Microfinances a profitable market?. Benchmarking. Differences between Microfinances, Personal loans and Commercial markets. The added value of a Bureau in the knowledge of risk. Why a Microfinance Bureau?. What characteristics should a Microfinance bureau have?: Type of information: quantity and quality. Technology. Data or Information provider: the use of scoring bureau. To walk in the business direction: strategic recommendations. Practical case: Information Sharing in Nicaragua. AGENDA

  4. Are Microfinances a profitable market?.

  5. In the last years, in different parts of the world new actors are entering to a new and unknown market for them: the Microfinances. Beyond the social impact that they recognize are going to generate and the importance given by different governmental institutions, we find the acknowledgment that it is a profitable market for different actors.

  6. INDICATORS OF MICROFINANCE INSTITUTIONS IN THE WORLD Source: The Mixmarket www.themix.org . Benckmarking of the Microfinance in Central America 2004.

  7. INDICATORS OF MICROFINANCE INSTITUTIONS IN THE WORLD Source: The Mixmarket www.themix.org . Benckmarking of the Microfinance in Central America 2004.

  8. INDICATORS OF MICROFINANCE INSTITUTIONS IN THE WORLD Source: The Mixmarket www.themix.org . Benckmarking of the Microfinance in Central America 2004.

  9. ROA % OVERDUE PORTFOLIO % PROVISIONS BANKS 1.53% 7.40% 10.50% MICROFINANCE INST. REG 1.96% 6.47% 6.61% MICROFINANCE INST. NO REG 4.04% 6.38% 4.84% PROFITABILITY IN THE MICROFINANCE MARKET Source: Interamerican Bank for Development, Microfinance in Latin America. 2006. “In particular, we show that MFIs fare much better than banks in the risk-return relationship, as MFIs show higher ROA levels at no extra cost, and in fact, this higher ROA is obtained with lower provisioning levels. Furthermore, we show that as the share of microfinance in any financial institution increases, both its ROA and ROE increase, while the proportion of non-performing loans stay the same and the provisioning levels actually decrease”. (IADB. Navajas, Navarrete, Simbaqueba, “Risk and profitability in Microfinances” 2006).

  10. MICROFINANCE INSTITUTIONS IN AFRICA (General Description) Number of MFIs, by type and by region * Total population. Source: The World Factbook. U.S. Central Intelligence Agency, Washington, DC: 2005 All values are estimates for July 2004. Source: The Mixmarket, Anne-Lucie Lafourcade, Jennifer Isern, Patricia Mwangi, and Matthew Brown. Overview of the Outreach and Financial Performance of Microfinance Institutions in Africa. 2005

  11. MICROFINANCE INSTITUTIONS IN AFRICA (General Description) Summary of financial volume and outreach indicators for African MFIs, by region

  12. In Latin America we find more productivity (borrowers/loan officer) than in Africa. In Latin America the portfolio at risk > 30 days is lower than in Africa and the profitability is higher. In Africa we find less cross selling experiences but more support on savings, as follows: AFRICA VS. LATIN AMERICA

  13. AFRICA VS. LATIN AMERICA Source: The Mixmarket, Anne-Lucie Lafourcade, Jennifer Isern, Patricia Mwangi, and Matthew Brown. Overview of the Outreach and Financial Performance of Microfinance Institutions in Africa. 2005

  14. Differences between Microfinances, Personal loans and Commercial markets.

  15. The successful experiences of MFIs have a common topic: understanding that the microfinance market is more than a hybrid of a personal loans market and a commercial (large enterprises) market, with regard to: - Characteristics of the population - Processes. - Required information and evaluation. - Type of required products. - The confluence of long run relationships with the profitability of the business. - The importance of the productivity indicators.

  16. MICROFINANCE VS. PERSONAL LOANS MARKETDIFFERENT CHARACTERISTICS AND PROCESSES MICROS PERSONS High credit Rotations Short term loans Payment dates according to the business cycle of the microentrepreneur Credit Analysts are both advisors and evaluators Credit officers are the heart of the relationship with the client: continuity, surveillance, trust, training, motivation Close relation with the client and follow up visits Zones Credit and information technologies Long and Medium length terms Analysts are Verifiers rather than advisors No Visits Payment dates according to income flow of the employed population Short Response Times

  17. MICROFINANCE VS. PERSONAL LOANS MARKETCOMMON CHARACTERISTICS AND PROCESSES MICROS • SEGMENTATION METHODS • Risk Management • Risk Pricing • Loans characteristics (amounts, terms, etc) according to Risk profile • Cost reduction • Cross Selling – Up Selling • Credit Bureau enquiries PERSONS

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

  19. The added value of a Bureau in the knowledge of risk.

  20. Allows the financial and non-financial market the control and management of the credit risk. Growth and expansion in the coverage of the financial services, specially in microfinance markets and populations with no financial access. Promotion of good payment habits (on-time payment culture), since it gives motivation to credit users to make their payments on time. The institution that uses a Bureau Score Report acquires a new capacity of analysis and the credit decision is made faster and better, since it enables a greater and better verification of data and analysis with a projection of payment behavior (Bureau Score).

  21. Sharing information facilitates decision making and guarantees profitability in the global credit system. Knowing the credit history of all institutions ( financial and non-financial) to avoid over-debt by customers. Reject clients who deliberately apply for credit in many institutions and end up in delinquency or bad debt.

  22. FOR EXAMPLE: Expansion of the access to financial services In a WB Nicaragua project it was shown that sharing information allowed for greater potential credit growth and considerable reductions in credit risk levels. For example: in the Commercial Sector there are 28.868 additional potential customers (consequence of information sharing); and risk level would be reduced from 46.17% to 26.39%, and the number of customers would increase by 48%.

  23. APPLICATIONS OF A CREDIT BUREAU - CUSTOMERSLIFE CYCLE Fraud Detection Collection strategies Provision Calculation Credit Approval Maintenance and retention of loyal customers Acquisition Of new products

  24. APPLICATIONS OF A CREDIT BUREAU - CUSTOMERS LIFE CYCLE • Reinforcement of approval or non-approval decisions based on historical payment behavior of the customer with other institutions. • Additional information about the client’s consolidated debt. • Forecast of payment behavior for approval or rejection as well as approval under specific credit conditions according to the risk level through Bureau Scoring. • Detection and rejection of customers who have several credits in different institutions without payment. Start and Fraud detection

  25. APPLICATIONS OF A CREDIT BUREAU - CUSTOMERS LIFE CYCLE • Bureau inquiries enables the development of preventive collection strategies by detecting changes on payment behavior or delinquency with other institutions. • Payment behavior in Bureaus can be included in collection Scorecards, if obtained periodically. Collection strategies

  26. APPLICATIONS OF A CREDIT BUREAU - CUSTOMERS LIFE CYCLE Just like in collections, looking to provision according to the probability of default of a customer, Credit Bureau’s information allows to know and use negative payment behavior with other institutions. Provision Calculation

  27. Why a Microfinance Bureau?.

  28. A number of MFIs (regulated and no regulated) with invisible risk knowledge about the debt level in the market of their potential clients. Regulated institutions that share information about their clients but not receive information about the MFIs clients. Qualification of Microfinance segment according to it’s specific characteristics (terms, Sociodemographic information, etc.). To differentiate credit bureau inquiry prices according to the Microfinance institutions capability. To evaluate the microfinance customer with different parameters than other customer segment.

  29. What characteristics should a Microfinance bureau have?.

  30. Payment delays in daily or weekly groups, not only by month. For Regulated and No-regulated institutions. Not just historical information, but also to consider Sociodemographic and business data. Strategies for each institution according to risk profile and other characteristics. To make recommendations to the institutions using the collected information.

  31. MICROFINANCE BUREAU APPLICATIONS (Micro-Scoring Bureau Methodology) Practical Case, Scoring In Nicaragua.

  32. BEHAVIOR ANALYSIS: Credit history When analyzing population behavior recurrence is observed in 37.15% of customers; the remaining 62.85% registers only one credit in their credit history in all analyzed sectors.

  33. GOODS AND BADS INDICATOR • The Goods and Bads definition is developed in the following scenario: • Goods: those having accounts 30 days or less past due. • Bads: those having past due accounts greater than 30 days. Under this definition, 69.83% of the population is labelled as GOODS and 30.17% as BADS.

  34. MATURITY ANALYSIS: Nicaragua The blue line shows the amount of customers approved each month, whereas the bars show the rate of customers registering a delinquent account greater than 30 days. We can see that behavior for the analyzed population stabilizes around 35%.

  35. FROM TOTAL POPULATION TOWARDS TARGETED POPULATION 18th month 7th month Window Analysis DLC (date of last credit approved) Targeted population 89.267 clients Period of forecast: Goods and Bads indicator • Historical behavior: • Bad debt with the institution • Bad debt with the Sector • Bad debt with other sectors • Credit Counters

  36. BEHAVIOR ANALYSIS: Maximum historical delinquent accounts Customers who had no prior credit history performed slightly worse than the population at large. Customers with a good credit history are more likely to repay on time, as compared with customers without a positive credit history. A bad credit history increased a borrower’s risk of delinquency by more than 43%.

  37. ANALYZED VARIABLES BUREAU SCORING Customer variables Product variables Behavior variables

  38. SUMMARY OF RELEVANT VARIABLES FOR THE MODEL A Scorecard is a table of scores for each of the variables relevant to the analysis. These scores are given by a statistical model and they represent the probability of good payment behavior by a customer.

  39. SCORECARD DISTRIBUTION OF A BUREAU SCORING

  40. SCORECARD EVALUATION: Design of Strategies Automatic rejections Follow-up High Risk Follow-up Low Risk Automatic approval When population is segmented according to the risk level, specific strategies can be designed for each segment : AUTOMATIC REJECTION for those clients who have a very low interval of Score; FOLLOW-UP for those clients with a medium level of risk; whereas clients with high score will be rewarded with Automatic approval.

  41. REDUCTION OF THE LEVEL OF RISK WITH THE APPLICATION OF THE MODEL Ascending accumulate distribution If the Cut-off is determined on the second interval of Score, 18.51% of applicants will be rejected; the reduction in risk, considering only this interval is reflected by 29% of bad customers. Moreover, the percentage of bad customers within automatic approvals would be 12.46%, with 33.34% of the population. Descending accumulate distribution

  42. SCORECARD APPLICATION: Design of Strategies Population is segmented by Score and delinquencies. From this segmentation, strategies are designed for collection, delinquency prevention, reactivation of customers and marketing. Intense Collection Preventive Collection Legal Action Intermediate collection Marketing and reactivation

  43. Validity Tests of the Model KS GINI COEFFICIENT The maximum separation between goods and bads is known as KS, for this model we obtain a KS of 31.62%. The Gini coefficient measures the degree of concentration on a table of frequencies; just like the KS, it measures whether the values of the variables are normally distributed along the sample of the model.

  44. IMPORTANCE OF INCLUDING DEMOGRAPHIC INFORMATION We observe that the level of discrimination is lower without considering demographic information. KS = 23.49% Gini Coefficient= 30.79%

  45. PREDICTIVE POWER USING POSITIVE AND NEGATIVE INFORMATION A model in which positive information is not included presents a lower level of differentiation, KS = 15.70% Gini = 23.43%.

  46. IMPORTANCE OF A MULTI-SECTOR BUREAU A Bureau Scoring model developed for only one sector presents a lower level of discrimination KS = 21.88% Gini = 26.80%.

  47. SUMMARY OF OBTAINED RESULTS We observe how for the total population there is a greater level of differentiation in the Full model, in which positive, negative and demographic information is included; also, different economic sectors are considered. This reveals the importance of sharing information at a Multisectorial level; also it is important, to emphasize the need to follow up the quality of data.

  48. SUMMARY OF OBTAINED RESULTS A simulation of 1.000 customers with an average balance of US$1.000, allows to see that the Full model enables greater amount of credit approvals maintaining the level of risk and better results in collections. When we have information of credit rejections it is also possible to calculate the impact of growth in the credit portfolio maintaining the levels of risk.

  49. Day by day Microfinance institutions are growing in number, mainly as the result of a higher profitable market. MFI´s require support in the knowledge of the historical behavior and potential risk of their clients; because of this a Microfinance bureau is required, but this bureau must understand the specific characteristics of the market and must be able to give data and information to the institutions. The presence of these bureaus helps reduce the market information asymmetry and allow the entrance of new actors to the microfinance market. CONCLUSIONS

  50. LILIAN SIMBAQUEBALiSim Group Presidentlsimbaqueba@lisim.biz www.lisim.com THANKS FOR YOUR ATTENTION.

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