Unlocking the Promise of Big Data to Promote Financial Inclusion
Every half hour, the internet – combined with internet-connected devices such as phones, computers, and home appliances that use electronic sensors to record and transmit usage information – will generate digital data equal to all of the written works in human history.
In an effort to understand how that data might be used for financial inclusion, we wrote the report Unlocking the Promise of Big Data to Promote Financial Inclusion, summarizing our findings from working with financial service providers across the globe and interviewing more than 30 industry experts to showcase what’s currently being done – and what else FSPs can do to understand and capitalize on the opportunities that data affords.
Defining big data
As McKinsey pointed out in a 2011 report, the term big data is, “intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered big data.” Few definitions establish a minimum threshold. Gartner’s information technology dictionary definition of big data emphasizes complexity over size, with what it refers to as the three V’s: high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing, which enable enhanced insight, decision-making, and process automation.
To make matters more confusing, the term big data is often used interchangeably with alternative data in the context of fintech and financial inclusion. Alternative data refers to data acquired from what banks consider non-traditional sources (such as utility bill payments, social data, or call data records) that can then be used by a financial institution to assess a potential borrower.
How can data advance financial inclusion?
The rapid expansion of mobile services and access to the internet, globally, has opened new frontiers for understanding customer behavior. Simultaneously, while the amount of data available has increased and its sources have proliferated, the analytical tools for making sense of this data have also become more sophisticated thanks to, among other things, advances in machine learning and artificial intelligence. The 2015 Omidyar report suggests that, in the world’s six biggest emerging economies alone — China, Brazil, India, Mexico, Indonesia, and Turkey — big data has the potential to help between 325 million and 580 million people gain access to formal credit for the first time.
Data can be a powerful force for financial inclusion, and the promise of big data is especially alluring. However, a gap remains between the vision and the reality of FSPs’ (Financial Service Provider’s) ability to leverage big data for financial inclusion. This gap reveals the need to provide more proof points to advance the industry’s understanding and underscore the social and economic benefits of using data to promote inclusive finance.
Among the early adopters of data-driven innovation for financial inclusion are firms that specialize in credit analytics using new digital or alternative data sources, such as mobile call data records, utility payments, social media activity, and others to assess risk and extend credit to “thin-file” or “credit-invisible” customers.
Here are a few insights we learned during our research:
- Aire integrates a 3-minute virtual interview into lenders’ online loan application forms that assesses the applicant’s character and ability to pay, leveraging insights from research in behavioral economics. FSPs use the scores from these virtual interviews to extend credit to individuals with little or no formal credit history. On average, Aire has increased loan disbursement by 14%.
- Tala (previously InVenture) asks customers to download its mobile application, which scans the customer’s phone and uses that data to provide a credit score within 20 seconds.
- MiMoni, an online lender in Mexico that provides instant credit decisions, worked with Kenshoo, a social media analytics company, to identify its most responsive audience and optimize its social media strategy around that segment’s activity. Leveraging Kenshoo’s big data analytics for MiMoni’s Facebook advertising campaigns led to a 60 percent decrease in its customer acquisition costs.
Alternative credit scoring is just the beginning
The smart analysis and application of data can improve every aspect of a business’s operations. There are several ways that businesses can harness the value of (big) data, including:
- Creating transparency
- e.g. a comprehensive view along the supply chain or operating process;
- Enabling experimentation to discover needs, expose variability, and improve performance
- e.g. through A/B testing;
- Segmenting populations to customize actions
- e.g. creating more targeted product design or marketing;
- Replacing or supporting human decision-making with automated algorithms
- e.g. through the use of chatbots; and
- Innovating new business models, products, and services.
This figure illustrates a few examples of business processes that an FSP can improve by applying more automated and data-driven decision-making. Data analytics is a foundation upon which to build stronger risk management, enable automation or standardization of processes and innovation of business models, simplify acquisition, inform product design, and improve service, all of which ultimately lead to a better customer experience. Each of these pieces is interrelated and mutually-reinforcing, addressing various inefficiencies or pain points in traditional business processes and customer interaction.
Common to these examples is the organizations’ ability to derive value from data, regardless of its source, scale, or complexity. So how do you do that? Read the paper, and stay tuned for our next post!