A person generally needs a financial history to be approved for credit. Access to credit is often taken for granted in developed countries. Traditionally to provide someone with credit, a credit score must first be established.
Artificial intelligence allows financial service providers to make use of alternative data sources, such as information on a person’s phone that might be shared for the purpose of obtaining credit. Satellite images can also provide an estimate of past and future agriculture income as well as the timing and sources of this income, and thus, they can provide key inputs to credit assessments for small farmers. A significant number of features that describe a person’s behaviour can be extracted from the phone, and AI is capable of relating these seemingly unrelated features to a single credit score based on examples of past loan behaviour.
By using AI and data analytics, financial products such as loans become available to a significant part of the world population that has no formal bank account, payslip or digital financial track record. Disbursing small loan amounts suddenly becomes financially feasible, since the entire process is automated and scalable. Fintech companies have found a way to monetize the regulatory hurdles that have kept traditional banks from lending to the poor. The idea of lending purely based on the data available on a consumer’s mobile device, ie creating a mobile digital credit store was an unproven model before but now it is a reality. We are witnessing the emergence of new business models with traditional banks partnering with fintechs for provision of digital credit services and the emergence of non banks fintechs in the digital lending space.
The objective of this session is to discuss some of the innovations that AI and big data analytics and how this can enhance financial inclusion in emerging economies.