Introduction
“Data analytics have become an essential part of the microfinancing industry. New advancements in data analytics like machine learning, blockchain, forecasting, artificial intelligence have enabled us to see the microfinancing industry in a new light, helping us to operate more effectively.
The list of positive impacts of data analytics in the microfinance industry is ever growing. The study of behavioral patterns of customers, population forecasting, pointing out profitable customers, accurate trend analysis for future operations, helping to make risk-free credit decisions by running background checks on borrowers, creating tailor-made financial services for targeted customers all point to data analytics proving its worth.”
Today there are more than 10000 microfinance institutions globally. By 2027, statistics show microfinancing lending reaching 394 billion dollars.
Ex-Citibank executive Samith Ghosh, the founder of Ujjivan Microfinance; ex-Barclays and Standard Chartered executive Suresh Gurumani, the CEO of SKS Microfinance, among others, have quit their jobs to expand the microfinance industry.
Data analytics have become an essential part of the microfinancing industry. New advancements in data analytics like machine learning, blockchain, forecasting, artificial intelligence have enabled us to see the microfinancing industry in a new light, helping us to operate more effectively.
“The above graph shows the quarterly growth of the amount disbursed and the number of loans disbursed for Jun 2020 – March 2021. The graph itself indicates the growth of the microfinance industry and its influence in the world.”
The Magic of Big Data:
Big Data refers to exponentially growing volumes of data that overburden businesses daily. Examples are stock exchanges, jet engines. Analysis of Big Data provides much-needed insights and helps make better business strategies.
In the world of microfinance, Big Data decides a customer’s creditworthiness. Big Data includes information collected from online activity on social media, emails, banking history or a list of phone calls and their frequencies. This information determines the customer’s ability to repay their loans provided by a microfinancing company.
The ability of Big Data to reach various customer pain points is tremendous and guides companies to create better services and products. Focus on the five essential aspects of data science- machine learning, architecture, simulation, markets, optimization helps explore and create a better and more effective platform.
Banks can use Big Data technologies to obtain meaningful and deeper insights into their MSME ventures. It can help deal with the issue of default risk by understanding the patterns in the customer’s behaviors. The identification of defaulters, the creation of tailor-made loans for specific types of customers is also possible.
Importance Of Data Analytics In The Microfinance Industry:
According to Forbes, the microfinance industry has over 200 million customers across the country. It results in the availability of a massive amount of granular data leading to the evolvement of the following four aspects of banking:-
Data Based Credit Decisions: Big Data consists of personal information, income tax statements, repayment history, among others, all of which helps the lenders to gain insights into the customer’s creditworthiness.
Big Data can act as a personal auditor who invests time in running background checks and verification of information.
Development of Products: Big Data uses machine learning, artificial intelligence and statistical methods to provide customers with tailor-made schemes for their financial needs in the required time. Optimization of interest rates and predicting defaults by studying portfolio behaviors get done more effectively due to the usage of Big Data technology.
Big Data-Driven Model and Psychometric Evaluations: The Big Data churned model is helpful for psychometric evaluations. Psychometric devices help gain insights into the client’s answers, gaining information that can predict default risk, summing up the client’s history, attitudes, and creditworthiness.
Product Build-Up And Service Positioning: Big Data can help companies find the right products for the right customers in the right places. Effective brand positioning is done by having customer information, predicting their needs and finding solutions to their pain points. It improves sales in the market and also is responsible for creating competition.
Leverage Data Analytics To Grow The Microfinance Industry:
Data analytics are the new helping hands of businesses in the modern world. It helps to make reliable credit decisions, understand the patterns in a customer’s loan-repayment history and make product and service positioning highly effective.
Below are the types of analytics offered available for optimization of the microfinance industry:
Behavioral Analytics: It helps to understand behavioral patterns determining their likeness to default. Lenders can use such analytics to point out defaulters, showing similar behavior. Thus, the selection of customers, deciding interest rates and tenures become easier.
Geospatial Analytics: Having demographic data of a particular location can help lenders cater to customer needs and point out the competition. It can also help show future trends and select future customers.
Predictive Analysis: It helps optimize solutions to reduce the possible variations for better operation of microfinance industries. Companies use predictive analysis to understand fundamental problems and predict future profitable ventures.
Customer Analytics: Customer Analytics uses Big Data stirred information to identify customers and target them for future products. They also help identify a customers’ share of wallet. Share of wallet is the amount spent on the services gained from the microfinance industries.
Machine Learning In The Microfinance Industry:
The introduction of machine learning and artificial technology in the microfinancing industry has brought a revolution. Some of the positive impacts are:
Better Experience For Customers: Machine learning and artificial intelligence have made the lending process easier, faster and more appealing. Loans are approved much faster. AI provides faster customer service- quick resetting passwords, obtaining financial details and voice-based authentication.
Automation: Artificial intelligence has made automation possible in almost every aspect of the microfinancing industry. It saves time and makes the process cost-efficient. JPMorgan Chase saved more than 360,000 hours of work by effectively analyzing 12,000 documents in a few seconds.
Effective Credit Assessment: Checking customers’ banking history and credit score. Artificial intelligence enables lenders to run financial checks quickly, obtaining important in-depth details and insights.
Reducing Fraud: An efficient and robust fraud detection system is possible today due to artificial intelligence. Financial frauds like money laundering are easily detected and prevented.
Investment Predictions For Future Ventures: Artificial intelligence and machine learning enables easier handling of big data, pointing out the characteristics and needs of customers to invest in well-targeted services.
“The above graph shows credit scoring by combining social network information of customers by using machine learning. This can help analyze and predict loan default risk faster and more effectively.”
Important KPIs In The Microfinance Industry:
Microfinance industries need to have intricate knowledge of customer feedback, how effectively their companies are running and estimate the productivity of their staff.
The following key performance indicators help to make the overall process highly effective:
Cost To Income Ratio: Indicates the efficiency of the microfinance company. A decreasing ratio indicates declining profitability.
Borrower Per Loan Officer: It shows the number of customers per loan officer. It also indicates the efficiency of loan officers and staff productivity. Companies should identify the opportunities to divide the workload effectively.
Portfolio Assets: Low levels indicate ineffective use of money. High levels indicate a lack of liquidity.
Level Of Poverty Of Customer: Indicates the financial status of the customer. It helps assess their financial needs and capacity for repayment more effectively.
Collection Performance: Indicates how effectively a microfinance company is collecting its loans.
“The above chart is an example with the summary of the outreach of the microfinance companies in South Asia, their effectiveness and the type of customers the company is targeting for operation.”
Conclusion:
Today microfinance industries can improve the quality of their operations and predict the outcome of future ventures by indulging in the benefits of advanced data analytics. Company executives can access data on their mobiles from any part of the globe.
Big Data will ensure that microfinance companies reach a higher population and cater to their financial needs with tailor-made financial services and interest rates.
Big Data relies on the words “work smart, don’t work hard” to deliver meaningful and much-needed insights.