GVG shares insight on the Use of Big Data for inclusive fintech at the ITU’s webinar
Fintech has improved access to formal financial services in Africa. In Sub-Saharan Africa, for example, the percentage of account ownership at a financial institution or with a mobile money service provider was 55.07 % in 2021, according to data from the World Bank. This is an improvement compared to the previous years. However, this means that approximately 45 % of the people living in the region still do not have access to financial services. How can the potential of fintech be more efficiently tapped to ensure that it allows more and more Africans to become part of the formal economy? On 7 March, the ITU’s webinar on the use of Big Data and AI for inclusive fintech provided some answers to this question.
Indeed, the ITU’s webinar explored the benefits of Big Data and AI as means to enhance fintech-led financial inclusion. Our CEO, James Claude, was invited to take part in the event, due to his experience as the leader of a company that specializes in Big Data analysis as a way of supporting governments’ decision-making processes.
Alongside him were four other experts on the topic of Big Data and AI for inclusive fintech: Arisha Salman, Financial Sector Specialist at CGAP; Isaac Gachugu, Financial Services Technical Lead at Safaricom; Alexandra Rizzi, Research Director at the Centre for Financial Inclusion and Kwame Oppong, Head of Fintech and Innovation at the Bank of Ghana.
The discussion, moderated by Rory Macmillan, partner at Macmillan and Keck, generated interesting insights into Big Data and AI’s capacity to support the creation of relevant financial products as well as effective domestic revenue mobilization (DRM). It also included important interventions around the challenges related to the use of Big Data and AI.
Big Data & AI for more inclusive financial products
According to the World Bank, financial inclusion is about ensuring that all citizens have access to formal financial services that meet their needs. It is therefore “a key enabler to reducing poverty and boosting prosperity”. In their respective intervention, Arisha Salman, Isaac Gachugu, and Alexandra Rizzi pointed out the potential of Big Data and AI when it comes to identifying and filling gaps in financial products and services, with a view to better-serving consumers.
For example, Isaac Gachugu explained that leveraging non-traditional banking data trails, in partnership with the Ghanaian government and Commercial bank, enabled his company to “transform lives” by offering micro-loans, mobile overdrafts, and credit for small businesses. He added that Safaricom’s plan was to keep building on AI and data.
Arisha Salman, for her part, highlighted the fact that the data generated by low-income individuals, although currently lagging, represented a “huge market opportunity” for FSPs to offer these individuals better access to financial services. She also conceded that the use of AI was still limited in the context of inclusive financial services and that Open Finance may be considered as an alternative.
Big Data to boost DRM
An inclusive financial ecosystem is also an ecosystem that supports effective domestic revenue mobilization (DRM). In his intervention, James Claude discussed the way in which Big Data technologies contribute towards an inclusive financial ecosystem by boosting revenue mobilization efforts. According to him, increasing DRM involves regularizing digital payment systems. Indeed, these systems are largely untaxed, he said, which leads to a decline in the governments’ sources of tax revenue, even though the industry is growing. James explained that the main roadblocks to effective DRM in Africa are the sheer size of the informal economy and tax evasion.
The purpose of Big Data technologies is to streamline tax collection and management systems, with a view to positively impacting local tax revenue generation and management. As a result, many African governments have adopted Big Data technologies to address tax collection and management issues over the past decade.
The growth in mobile money transactions, which is in itself positive for financial inclusion and economic growth, also presents an opportunity for the government to increase tax income. Here again, Big Data technologies can make a difference, by providing the relevant authorities with reliable metrics about the mobile money ecosystem, to support effective taxation of the transactions carried out through these services, if such a form of taxation were envisaged.
Big Data and AI for financial inclusion: the challenges
The discussion showed that Big Data and AI can support financial inclusion by enabling FSPs to provide inclusive financial services to all populations and governments to increase their revenue. However, for this to fully materialize, the risks involved in the use of both tools need to be considered and mitigated.
Therefore, it is essential that institutions build their capacity in relation to the use of AI and Big Data, said Kwame Oppong. And just as AI and Big Data can support effective regulation, as Kwame explained in his presentation, so too can regulation support the effective and compliant use of these tools.
Two main issues arose from the exchange: AI bias, and data privacy and security. As Alexandra Rizzi pointed out, bias introduced in AI algorithms that decide who is a fraudster or who gets access to credit, for instance, can have negative repercussions on individuals. That is particularly the case of domestic migrants, Alexandra indicated, due mostly to the complexity of their data trails. As a result, nuanced data trails and AI credit scoring models should be used when assessing migrants’ credit requests. In this case, regulation may support risk mitigation, by clearly laying out AI use conditions and requirements.
Regarding the privacy and security of the data collected from the consumers, regulation also provides a safeguard in this respect. Indeed, it sets the standards for the safe and responsible use of the data and prescribing which data is necessary for the purpose. This is particularly relevant when talking about Open Finance, a topic that Arisha Salman raised and was further discussed in the Q&A session. In this context, Isaac Gachugu stated that the responsible and “sensitive” use of the data involved only sharing information relevant to the considered product.
As Kwame Oppong put it, “[t]he market is already evolving. AI is here. Big Data has been here for a while”. It thus appears that the only way is forward, especially considering the benefits already experienced in terms of financial inclusion. However, applications are limited, especially in the case of AI, and some challenges, like data protection and AI bias, are yet to be fully addressed. This means that optimistic caution, further study and the exploration of alternatives, such as Open Finance, may be the order of the day.
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