Category: Technology

Olawunmi Gbolade is a seasoned programme manager and consultant based in Rockville, Maryland with focus on using advanced analytics to promote financial inclusion, particularly in marginalised communities across the United States. She speaks with OLUKOREDE YISHAU on this crucial topic. Excerpts:

Let’s start with the big picture. Financial inclusion remains a significant issue in the U.S., with many communities still lacking access to essential services. How widespread is this problem, and why does it persist?

Financial inclusion, or the lack thereof, is indeed a pressing challenge in the U.S. According to the Federal Deposit Insurance Corporation (FDIC), as of 2021, about 5.4% of U.S. households—around 7.1 million—were unbanked. This means they don’t have access to basic banking services. Additionally, 13% of households are underbanked, relying on alternative services like payday loans or check cashing. These statistics highlight significant barriers, especially for low-income households, racial minorities, and rural populations. The issue persists due to a combination of factors, including systemic inequalities, a lack of trust in financial institutions, and inadequate access to affordable financial services.

With these challenges in mind, how can advanced analytics, particularly predictive models, make a difference?

Advanced analytics, especially predictive models, offer powerful tools to bridge the gap in financial inclusion. These models use historical data, machine learning, and statistical algorithms to predict future behaviors and outcomes. In the financial sector, this can be transformative. For example, traditional credit scoring models, like FICO, often exclude marginalized populations because they rely on formal credit histories. However, predictive analytics can incorporate alternative data sources—like rent payments, utility bills, and even mobile phone usage—to assess creditworthiness. This more inclusive approach allows financial institutions to extend credit to those previously overlooked.

A good example is Petal, a U.S.-based fintech company that uses machine learning to analyze customers’ cash flow and spending habits, rather than just their credit scores. This approach has opened up credit to people with limited or no credit history, such as younger adults and immigrants.

That’s fascinating. Could you elaborate on how predictive analytics can be used beyond credit scoring, particularly in designing other financial products?

Absolutely. Predictive analytics can play a significant role in designing a variety of financial products that cater to the unique needs of different communities. For instance, savings products can be tailored based on behavioral data. Financial institutions can analyze how various demographic groups approach saving and create products that encourage sustainable savings habits.

An example could be a savings account that automatically rounds up transactions and deposits the difference into a savings account. This type of product, informed by predictive data, can help low-income households save incrementally without much effort.

It sounds like predictive analytics is not just about inclusion, but also about empowerment. How is this approach being applied by government agencies?

That’s correct. Empowerment is key. Government agencies are increasingly using advanced analytics to evaluate and improve their financial inclusion programs. One example is the U.S. Department of the Treasury’s Community Development Financial Institutions (CDFI) Fund. They use data to identify regions where financial inclusion efforts are most needed and ensure that federal resources are allocated effectively. By analyzing data on income levels, credit access, and financial behaviors, the Treasury can better target communities that would benefit most from support.

Another interesting initiative is the U.S. Postal Service’s exploration of offering financial services. Using demographic data, the USPS can pinpoint neighborhoods where traditional banking services are scarce and consider piloting postal banking programs in those areas. This data-driven approach ensures that resources are deployed where they can have the greatest impact.

It’s impressive to see how data can guide these decisions. What about evaluating the success of existing programs? How do analytics come into play there?

Advanced analytics are crucial for evaluating program effectiveness. Agencies like the Consumer Financial Protection Bureau (CFPB) use data to track the success of financial literacy and inclusion programs. By analyzing participants’ financial behaviors over time, they can assess which aspects of the programs are working and where adjustments are needed.

For example, if a financial literacy program isn’t leading to improved financial decision-making in low-income households, data can reveal why. Maybe the content needs to be more relevant or accessible, or perhaps the delivery method isn’t resonating with the target audience. With real-time data, adjustments can be made quickly, making these programs more effective.

Real-time data seems to be a game changer. Can you share an example where this kind of dynamic adjustment was particularly impactful?

Certainly. A recent example is how the U.S. government distributed stimulus payments during the COVID-19 pandemic. Using advanced analytics, they were able to assess economic impacts in real-time and identify populations most in need of support. This allowed for targeted distribution of stimulus payments and unemployment benefits, ensuring that relief efforts reached the most vulnerable groups.

This ability to adjust strategies dynamically, based on real-time data, is one of the biggest advantages of advanced analytics, especially during crises when quick and targeted interventions are essential.

As promising as this all sounds, there are always challenges. What are some of the hurdles you see in implementing advanced analytics in financial inclusion, and how can they be addressed?

One of the main challenges is data privacy and security. With increased reliance on data, it’s essential to protect individuals’ personal information. Robust data governance frameworks and transparency in how data is collected and used are critical.

Another challenge is the potential for bias in predictive models. If these models aren’t carefully designed, they can inadvertently perpetuate existing inequalities. For example, over-reliance on certain data points could exclude specific groups. To mitigate this risk, it’s important to use diverse data sources and continuously monitor the models for bias.

Addressing these challenges requires a commitment to ethical use of data and ongoing scrutiny of how these models are applied.

That’s an important point. As we look to the future, what do you see as the next steps in leveraging advanced analytics for financial inclusion?

The future lies in continuing to innovate while ensuring ethical practices. We need to keep harnessing data to design more inclusive products and optimize government programs. But we must also prioritize transparency, fairness, and security in how these tools are used.

If we do this right, we can break down barriers to financial inclusion and create a system that works for everyone. This not only benefits individuals and communities but also contributes to broader economic growth and resilience.

Thank you, Olawunmi, for sharing your insights. It’s clear that advanced analytics has the potential to transform financial inclusion in meaningful ways. We appreciate your time and expertise.

Thank you. It was a pleasure to discuss this important topic.