Home » Lendtech » How Big Data Can Drive Financial Inclusion in Africa – Okoye
Lendtech

How Big Data Can Drive Financial Inclusion in Africa – Okoye

DIGITAL
Emeka Okoye, Senior Semantic Web Architect at Cymantiks Nigeria

Big data, machine learning and analytics can be used to create opportunities to improve financial inclusion and reduce the population of the unbanked in Africa by helping financial service providers to offer affordable financial services, Emeka Okoye, a semantic web architect, has said.

Okoye, founder of CYMANTIKS Nigeria Limited, who spoke at the just concluded Committee of e-Business Heads (CeBIH) 2017 annual retreat, explained that having a deeper understanding of what financial products the under-banked consumers and small businesses need and want “is all about the data”.

According to him, exploitation of the big data is opening doors for financial institutions to enter the microfinance sector. “The new competition can have a positive impact on microfinance institutions [MFIs] as it pushes them to innovate around “alternative” credit scoring models and to develop mobile-based savings and loan products that can be easily accessed by clients.”

Okoye argued that big data can offer big benefits to consumers with more convenient and affordable financial services that better meet their needs.

“But there are also several risks that arise from the increased use of big data. Consumers should also be protected from harm that result from data practices,” he warned.

He explained that financial institutions and fintechs can objectively use big data to extend credit to consumers who previously relied on expensive and sometimes exploitative informal credit, if any, because they had no formal credit history.

He added that MFIs can design new products for identified customers to fit the actual needs and realities of consumers based on their behaviour and demographic information.

He therefore listed some benefits of big data to financial institutions, which include:

  1. Enter new markets, increase competition on price, quality and innovation
  2. Rapid customer segmentation
  3. Data mining of social signals from customers and prospects
  4. Individualized reach campaigning
  5. Tailor customer service and increase efforts to cross sell
  6. Speed client acquisition through automation
  7. Reducing risk through predictive analytics
  8. Identify behavioural patterns among customers
  9. Use the information to stimulate better use of financial services and identify potential new users
  10. Data analytics has become imperative to increase revenue, enhance customer experience, optimize cost structures and manage enterprise risks
  11. Enable automation
  12. Improve product design
  13. Creating transparency
  14. Enabling experimentation to discover needs
  15. Can spur the entry of many millions of aspiring middle-class consumers into the formal credit system
  16. Segment populations to customize actions (such as by creating more targeted product design or marketing)
  17. Replace or supporting human decision-making with automated algorithms
  18. Innovate new business models, products, and services