Increasing Financial Inclusion through AI

22/06/2021

Big data is growing at a rapid pace. Worldwide, an increase of 530% is expected — from 33 zettabytes in 2018 to an excess of 175 zettabytes by 2025 (Souce: EC Europe )

AI is regarded as key for unlocking the potential of this big data excess by transforming it into a financial inclusion enabler. In a recent WEF blog article, PayU CEO Laurent Le Moal and Amelia Ng, Olea, SC Ventures share their recommendations for both the public and private sectors on three main elements needed to sustainably and safely mobilize the potential of AI.

To take a deeper dive into this multifaceted topic Laurent and Amelia hosted a panel discussion inviting industry experts to share their professional and personal views. Watch the full discussion below.

Impactful financial inclusion via AI & Data Portability

Over the past decade, financial inclusion has been mainly driven by innovations expanding the accessibility to digital technologies. AI has proven to be the leading innovation extending access to financing for those who need it most: the 1.7 billion people globally not covered by traditional banking.

The authors identify three main elements paramount for fully utilizing AI’s potential to enable financial inclusion.

Digital identification systems for financial inclusion

Building a new digital identification infrastructure to reduce the cost of reaching the last-mile user is key to enabling an open and free market. Leading examples include Aadhar, a 12-digit unique ID issued by the Indian government, and UPI (United Payments Interface), an interoperable, mobile-first payment system regulated by the Indian central bank.

Data portability and its application to the lending & financing sector

An effortless transfer of personal data from one organization to another should be considered a user right. Instead of being kept in an organization’s silos, it should be in possession of the users who can freely share it with other service providers. India exhibits this principle through the Account Aggregator Framework (AA), enabling consent-based sharing of user data and lowering transaction costs.

Guidelines to develop ethical AI

An adequate representation of under-served audiences and eliminating possible biases of the underlying algorithm are essential to mitigate the risk of AI replicating social biases. The absence of a law regulating AI algorithms puts this topic on the private sector’s agenda— an act well-advised to facilitate independence.