Microfinance is all about the provisioning of financial services to small businesses and low-income groups, which typically lack access to these services. These individuals or businesses possess no collateral to post and lack the ability to qualify for a standard loan.

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Track transaction patterns, spending habits, and financial preferences…

Microfinance is all about the provisioning of financial services to small businesses and low-income groups, which typically lack access to these services. These individuals or businesses possess no collateral to post and cannot qualify for a standard loan.

With rapid technological advancement, novel players and massive investments arriving in the financial tech stack, mobile phone penetration is reshaping the financial services vista. With the improved penetration of mobile devices worldwide, the occasion to obtain an in-depth understanding of consumer behaviour, including how they spend their money and time, has typically improved. It’s now possible for them to target a distinct group of people using such knowledge.

The big data analytics market also heavily relies on different mobile apps as their information is stored in a data warehouse. Apart from the mobile industry; Big Data impacts other markets, such as microfinance institutions (MFIs). By integrating digitization, Big Data plays an inventive role in the exquisite ecosystem. It aids in satisfying emerging customer demands and anticipations.

The microfinance industry has been successful in witnessing drastic growth. This growth is now a reality, as a result of investors’ continued support of MFIs coupled with an equity infusion of about Rs. 9,443 CR which is a boost of about 55% from Q1 FY19-20. (Source: Business Today)

Challenges Encountered by Microfinance Institutions in Analytics:

  • Lack of information results in reliance on certain key people as informants coupled with directive management and micromanagement techniques, which further leads to distrust among co-workers, resulting in dependency on dubious versions often causing faulty judgements, particularly around human capital.
  • Primary data collection in an initial survey is at best a dipstick survey using basic and minimal interviewing techniques primarily limited to the proposed branch area, without considering the hinterland it caters to, which is where the main issue lies.
  • In-house correlative data possessing associated insights never go through deep study. It is similar to having curtains lying while driving a car since the greater dependency is on customized reports, which is posterior.
  • Deviation management concerning set paradigms may share early warning signals, but still lack the deployment, losing out on valuable insights and corrective operational risk management techniques. Even over time, the benchmarks need to change but it’s possible only when there’s a robust benchmarking initiated, based on trends.
  • Most of the Analytics in MFIs is primarily into Financial Analysis, which is undoubtedly at the heart of MFI operations. Operational data often lack employment. Or at times data thrown at operational people, are not merely actionable, leading to neglecting such raw data.

Solutions for the Challenges Encountered by the Banks in Microfinance

Analytics can deliver potential solutions for the challenges encountered by the banks in microfinance:

  • One probable approach is to adapt the notion of credit scoring to microloans. The inadequacy of credit information makes this a challenge as microfinance borrowers basically lie under this category of thin file. While using advanced analytics approaches, it’s possible to derive creditworthiness scores of these particular thin file cases.
  • Banks can deem big data as another possible solution. Because of the lack of conventional credit data, banks might consider supplementing with the use of unconventional data sources to improve the credit view of their prospective customers. This is particularly feasible as these prospects produce increasing amounts of data like transactions, social media, and geolocation information via their mobile phones. Banks can apply big data analytics to these datasets to acquire deeper insights into the MSME prospects.
  • Understanding behavioural patterns with the help of predictive analytics can further help in addressing the issue of default risk. Since microfinance is not a new phenomenon, the banks might have some data using which they can profile and develop archetypes of the kind of borrowers that they have provided service before.
  • Analytics plays an integral role in enhancing the efficiency and effectiveness of microloans, microcredit, and microinsurance, delivering significant benefits to both providers and beneficiaries. By leveraging data analytics, financial institutions can evaluate the creditworthiness of individuals with limited or no credit history, allowing them to make more informed lending decisions in the context of microfinance. Advanced algorithms can help analyze different data points, such as repayment history, income patterns, and even non-traditional indicators such as mobile phone usage, to develop more accurate risk profiles.
  • Analytics can also help optimize the loan approval process, reducing the time it takes for applicants to receive funds. Real-time data monitoring also facilitates early detection of potential defaults, allowing proactive intervention and risk mitigation strategies. In the context of microcredit, analytics helps in tailoring loan products to the specific needs of borrowers, fostering financial inclusion and economic empowerment. Microinsurance benefits from analytics by facilitating better risk assessment and pricing models
  • In the realm of money transfers, analytics allow financial institutions to detect patterns and anomalies, helping in the identification of potential fraud or suspicious activities. This ensures a secure and reliable transfer process for individuals and businesses alike.
  • Concerning individual business loans, analytics deliver lenders with valuable insights into the creditworthiness of applicants. By analyzing historical financial data, transaction patterns, and other relevant information, financial institutions can drive more informed decisions about loan approvals and interest rates.
  • In the context of energy loans, analytics contribute to optimizing resource allocation and assessing the viability of renewable energy projects. By assessing factors like energy consumption patterns, market trends, and environmental impact, financial institutions can drive data-driven decisions on loan approvals for sustainable energy initiatives.

Vital Factors of Big Data in Microfinance

With home-straight connectivity all set to reach a large number of clients, it’s becoming a potent channel to acquire one of the national choices- financial inclusion.

Taking into account, a huge amount of client accounts served under the microfinance sector, it is the primary hub of substantial granular data. This data set packed with customer information including age, income estimates, occupation attendance at meeting centres and others is paving the way for the growth of these vital factors:

Making Data-Based Credit Decisions

It can assist in the creation of application scorecards for making an impartial selection of customers related to funding. Customer behaviour patterns and vital information acquired from industry data integration into such scorecards can help to derive swift credit decisions. Within a fraction of a minute, at the front end, these credit decisions are acquirable through digital channels of the customer service officer’s handheld device.

Hence, it helps to save the time spent on verification, reference checks, and validation by the fulfilment executive. Besides, this Big Data provides customer loan approval, and request processing, coupled with disbursement of the loan amount.

Leverage Pscyhcometric Evaluations with Big Data-Driven Model

Big data-driven models can significantly help with psychometric evaluations. A lot of psychometric tools help assess the applicant’s answers which helps to seize information that might help to predict loan repayment behaviour, including applicants’ beliefs, performance, attitudes, and integrity.

Service Positioning and Product Build-up

Using big data, the microfinance industry will place itself in a position where it will be capable of providing products that customers require. Based on customer needs visible through analytics like price modelling, customer segmentation, and data modelling; MFIs will be able to serve customers with eleventh-hour financial needs, with the right loan ticket size and insurance schemes.

Microfinance companies will be proficient enough to predict portfolio behaviors at different geographies and thus, appropriately analyze default and credit losses more accurately. Hence, companies can select the right portfolio concerning their financial products.


With technology continuing to advance, the role of big data in microfinance will become more essential. Predictive analytics, artificial intelligence, and machine learning are likely to further improve the industry’s capabilities. These tech stacks will allow MFIs to not only react to customer behaviour but also predict future needs and trends more accurately.

Big data analytics is accompanying the new era of microfinance. By employing data for informed decision-making, microfinance institutions are well-equipped to serve a more huge and diverse clientele. This leads to more precise risk assessment, personalized financial services, and operational efficiency, all of which significantly contribute to the goal of sustainable growth and financial inclusion.

Big data when combined with different aspects of Artificial Intelligence (AI) has been already initiated to transition the decision-making process of not merely MFIs, but also other industries like manufacturing, automobile, healthcare, communications media, etc. As more and more data become available, the analytic models are further refinable, allowing greater accuracy in customer intelligence.

The application of analytics in microfinance is merely the tip of the iceberg. Insights acquired from analytics can help banks and personnel acquire greater visibility, versatility, veracity, and velocity. These would enable them to leap forward a more deliberate approach, manage risks better and design better services for their prospective microfinance customers.

With technology and data analytics continuing to emerge, the future of microfinance seems to be brighter than ever.

Willing to know more about big data solutions in microfinance? Reach out to Smartinfologiks at the earliest and streamline your business processes for greater efficiency.


As your single stop IT partner, Smartinfologiks has transformed businesses with strong and adaptable technological and digital solutions that suffice the prerequisites of today and unlock the benefits of tomorrow. Combining the various industrial expertise and cutting edge technologies, Smartinfologiks has trapped an honour of delivering reliable and scalable cross platform and enterprise software solutions for desktop, browser & mobile devices, & products that ideally suit the demands and behaviour of the end-users.