Analytics for Financial Inclusion in Microfinance Sector

Analytics for Financial Inclusion

 

Data is the new oil and data-driven insights are the helping hand of modern-day businesses. Financial services are no exception, robust development in technology, new entrants, mobile phone penetration are redefining the whole landscape.

Microfinance sector amounts to a huge number of client accounts and is the center for unstructured, web and big data.

This data set contains customer details including age, income, expenses, loan purpose, occupation, attendance at meeting centers, etc leading to the increased demand for analytics to extract actionable insights.

Let’s understand the types of analytics currently being offered by the industry

Geospatial Analytics

Demographic data of the customers of a particular area or region helps to map them with viable products. This data helps to target potential customers residing in that area or region. This requirement is fulfilled by Geospatial analytics.

It helps to display customers’ location on a map of the MFI branch. Moreover, it forecasts the human population by filtering out relevant data and applying it to provide trend analysis, modeling and predictions. It analyzes and monitors the performance of the MFI branches and reviews by defining a trade area and figuring out nearby competitors.

Behavioral Analytics

It plays a vital role in determining behavioral patterns to identify whether the customer is likely to default. Accurate collection models can be a driving factor behind a product’s collection rate and efficiency. Simple classification models or scorecards can be developed on the past data to assist the collection team to pin down the group of customers in the current portfolio who exhibit a similar pattern to the ones who defaulted earlier.

The collection team can focus more on the customers with a high probability of default and align their efforts accordingly to reduce the delinquencies. This model runs at the beginning of every collection cycle, and it’s frequency will be the same as the repayment frequency (weekly, monthly or fortnightly).

Predictive Analytics

Predictive analytics delivers capabilities to mine insight from historical data and optimize solutions to reduce variability and improve operations.

It helps MFIs to reduce costs by converting raw data into business insights for better decision-making. It enables combining data across industrial sources and quickly discovering problems, identifying fundamental reasons, and determining future performance.

MFIs are using it to make better decisions for continual improvement of quality, productivity, and operations delivery. Branch-wise data is churned to determine the performance of each branch, and the executives take corrective measures to fix the laggards.

It can pinpoint the root cause of low performance and suggest the required course of action well in advance.

 

Predictive Analytics
Dashboard and Scorecard
Analytics Dashboard

Dashboards offer a bird’ eye view of KPIs (Key Performance Indicators) relevant to the MFI business process through constructive use of visual charts and graphs.

Analytical reporting is the most important element in presenting the desired information in different forms to the top management. This tool enables multi-dimensional analysis, what-if analysis, and data drill-down analysis.

A scorecard helps to measure and monitor the KPIs concerning the defined strategic goals and performance milestones, through effective use of traffic light indicators.

The comprehensive console provides exhaustive details to top management helping them understand trends, patterns in their business growth. With this console, executives can track, decide, and on various aspects of the business for eg. detailed analytics of expenses and revenue that helps to determine the level and cause for change in ROE

Customer Analytics

MFIs use analytics to identify the profitable customers who could also become target customers for other products and individual loans.

Customer Lifetime Value or CLTV is a measure used for calculating the value of a customer relationship, based on the NPV or Net Present Value of the projected future cash flows from the customer. Those with negative NPV should not be considered in this respect while consistent effort should be made to improve recoveries from such customers. Profitability and the lifetime value of the customers facilitate comparison with peer groups and indicate customer sentiment and the probability of attrition.

Analytics can also assist to determine a customer’s SOW or Share of the Wallet, which is the amount of the customer’s total spending on the products and services offered by the MFI. SOW can be enhanced by up selling and cross-selling other products or services thereby creating loyalty.

Conclusion

Through the assistance of predictive analytics and profitability-based customer analytics, MFIs can improve the quality of loans and reduce delinquencies. Moreover, mobile and cloud-Based analytics enable access from anywhere on any mobile device to the senior executives of the organization.

Big Data will help the Microfinance industry to reach a milestone where they will have the facility to provide products that customers exactly need. MFIs will be capable of serving customers with just-in-time financial needs, with the best-suited loan ticket size and insurance schemes.

As the famous saying goes “work smart don’t work hard”. Big data works on the same mechanism to structure the unstructured data and deliver useful insights.

About Craft Silicon

Craft Silicon is a leading financial technology solution provider and recognized as one of the most tech-savvy software groups globally. Craft Silicon supports 300+ financial Institutions by delivering value in over 30 countries.  Craft Silicon provides robust solutions that include Core Banking, Loan Management, Channel Banking etc. –   Managing over $5.6Bn of Loan Portfolio, 57Mn customers & 1Bn transactions per year in Asia region.

BR Analytics –

BR Analytics, using inbuilt deep domain knowledge, provides analytical solutions to improve business decisions & optimize performance. An intuitive system, it allows for interactive /visualizations of business insights & predictive analytics capabilities, converting raw data to actionable insights –

  • Reads multiple sources – big data, web data & unstructured data
  • Convenient plug & play system with core BR.NET allowing quick deployment
  • Real-time data analytics for business insights across industries
  • Detailed reporting includes KPIs, dashboards
Divya
Author: Divya Jyothi   |   Assistant Marketing Manager

Divya is a goal driven Marketing and Product design professional with over 6 years of corporate experience. She oversees end-to-end marketing management by developing and executing integrated marketing strategies to advance sales and customer engagement. Functioned as a Meticulous product designer balancing multiple deadlines while maintaining an organized yet, creative approach towards user research, product interaction design, UX & UI design.

Author: Udit Divyanshu   | Business Development
Udit is an upbeat person and finance enthusiast with an MBA from IIM Ranchi. He has an inquisitive outlook towards the finance domain and core competencies lies in financial modeling, product demonstrations and customer engagement. His passion lies in helping people understand complex concepts and bringing about constructive change.

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