Analytics for Microfinance Institutions
“When women make money, families see the benefit” goes an old adage. This is the basic premise on which the Microfinance Institutions (MFIs) are run. The target clients are women, who come from the lower strata of the society, who may not find it convenient to get loans from established Banks. The MFIs demand no security from their customers but insist on group borrowing to ensure there is sufficient guarantee in case of a default. The customers are then progressed to the higher loan category based on past history of loan repayment. In a nutshell, the loans at the lowest level are advanced to a small batch of customers while both the disbursement and collection happens collectively as a group. This concept has ensured low loan default rate thanks to societal pressures. MFIs have revolutionized the way “financial inclusion” has been adopted in developing countries. India, for example, has perhaps the largest “unbanked” population in the world and the statistics is really alarming. The unbanked adult population stands at roughly 40% (Urban) and 60% (Rural) while only 14% (Urban) and 10% (Rural) households have loan accounts. The MFIs catering to this large unbanked population, have indirectly created self-employment opportunities especially for women, who have become independent or sole bread winners in the family.  MFIs offering loans to Micro and Small & Medium Enterprises (MSMEs) indirectly support creation of employment opportunities thereby extending the benefit to a larger community. The growth of MFIs indirectly support economic growth and importantly more power to women. To support growth of MFIs, Analytics has to be optimally leveraged and this is what the white paper is all about. Leveraging Analytics to grow the MFIs MFIs need to map their customer base to their products and this is possible only if demographic data is available to the Branch Manager of a MFI, of a particular area or region. This will enable him to target potential customers residing in that area or region.  Geospatial analytics can support this requirement. The many applications of geospatial analysis include human population forecasting by filtering out relevant data and applying it to provide accurate trend analysis, modeling and predictions.  By using this analytical technique, the Customer Sourcing Executives can expand their customer base by targeting those who have not been tapped yet. Predictive Analytics plays a key role in coming up with behavioral patterns to determine whether a Customer is likely to default. Every MFI has to submit the KYC (Know Your Customer) data to an external agency which collates the data from all MFIs operating in the country. They also collate data of all loan defaulters. Analytics performed on this database can come up with patterns with respect to Customer behavior so that the MFIs can target only those, who have a clean track record. This will reduce delinquencies to a great extent. Profitability based Analytics: Most of the MFIs are upgrading to small Banks to cater more or less to their existing customer base. If the MFIs used analytics to identify the profitable customers, they could also become target customers for the small Bank as well. Customer Lifetime Value or CLTV is a measure used for deriving the value of a customer relationship, based on the NPV or Net Present Value of the projected future cash flows from the customer relationship. Customers with a better NPV are the profitable ones. With the support of Analytics, the MFI can track such customers to take them to the next level. Those with negative NPV should be ignored in this respect while effort should be made to improve recoveries from such Customers. Analytics can also help derive a Customer’s SOW or Share of the Wallet, which is the amount of the customer’s total spending on the products and services that the MFI offers. SOW can be increased by cross-selling other products or services thereby creating a loyalty factor. Dashboard and Scorecarding for better business insight: Dashboards offer a bird’s eye of KPIs (Key Performance Indicators) relevant to a particular business process through effective use of visual charts or graphs. Analytical Reporting is perhaps the most critical part presenting the required information in different forms to the top management. This tool will enable multi-dimensional analysis, what if analysis and data drill down analysis Scorecarding helps in measuring and monitoring the KPIs against the defined strategic goals and performance milestones, through effective use of traffic light indicators. Key KPIs used in MFIs The focus of MFIs is to track their Customers over a period of time, wherein they move from a lower loan category to higher loan category based on repayment history. Thus MFIs need to measure Customer retention, as one of their key KPIs. Customer retention ensures increase in Interest income as customers move to higher loan category while newly referenced Customers are added over a period of time. Some of the other key KPIs are related to Sourcing, Disbursement, Outstanding loan portfolio and Collection. MFIs would like to measure these KPIs against the goals or targets set at the start of the year. Measuring these KPIs at regular intervals, will help MFIs monitor the performance of their Branches, Regions or Zones. Using forecasting models, MFIs can ensure proactive performance monitoring. This will help the Organization, well in advance, to take preventive steps and avoid negative scenarios. Leverage the power of Mobile Analytics Most of the MFIs are funded by venture capitalists or FIIs (Foreign Institutional Investors), who track the performance of their investment very diligently. Assume a situation where the CFO of a MFI has a critical meeting with the Investors at Mumbai and while he waits at the Bengaluru airport, he wants to have a quick look at the financial numbers, to prepare himself for the meeting. He relies on his mobile device to do a quick analyses and finds out, that one of the Regions has not done well in the last Quarter. While drilling down further he finds out that one of the States in that region has slipped badly on the targeted numbers while other States have done well. The State had suffered heavily due to floods in the last Quarter, which affected business for more than a month. The CFO is now well prepared to meet the Investors. This is possible only through mobile based analytics, which has vastly reduced dependency on offline support. Conclusion To summarize, Business Analytics can drive profitable growth of the MFIs through effective use of Dashboards and Scorecarding, offering better insights into the data, which supports the organization to take meaningful decisions. With the help of Predictive analytics and Profitability based analytics, MFIs can improve the quality of loans and achieve higher profitability. Mobile and Cloud-Based analytics ensures access from anywhere on any mobile device benefitting the senior Executives of the organization.
“When women make money, families see the benefit” goes an old adage. This is the basic premise on which the Microfinance Institutions (MFIs) are run. The target clients are women, who come from the lower strata of the society, who may not find it convenient to get loans from established Banks. The MFIs demand no security from their customers […]
To support growth of MFIs, Analytics has to be optimally leveraged and this is what the article is all about.
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["Go", "API", "programming_languages:R", "AI", "venture capital", "predictive analytics", "RAG", "analytics", "GAN", "R"]
["AI", "analytics", "RAG", "predictive analytics", "R", "Go", "API", "GAN", "venture capital", "programming_languages:R"]
Arvind has led the Finance &amp; Performance Management Competency at IBM. He has 24+ years of total experience including 10+ years in the Financial Analytics domain. He has extensive experience in Strategy and Performance Management with functional expertise in financial processes and domain expertise in Banking &amp; Financial Services. He played a key role in a Financial Transformation engagement for a leading India based MFI, as a Lead Consultant and SME. He has in the past ideated and designed solutions around Profitability Management for Retail Banks; CFO Dashboard for Financial analytics; ABC (Activity Based Costing) solution etc. He has written several White Papers and Point of Views on Financial &amp; Performance Management subjects. Arvind is a Cost and Management Accountant and MBA in Banking. Prior to IBM, he was a BI Practice Leader at Hewlett Packard and Infosys. He can be contacted at arvindakamath@gmail.com