The contribution of AI-Based analysis and rating models to financial inclusion: the Lenddo case for women-led SMEs in developing countries

The world of work is at the dawn of major transformations and is still poorly prepared for it. Thanks to the analysis of the latest publications and in particular of the articles and reports published by academics, consulting firms and think tanks, this contribution aims to build an exploratory representation of the expected effects of the development of AI in the process of bank credit analysis. This procedural development, in comparison with the statistical approaches traditionally used, can serve as an original technical-financial support for a better inclusion of the excluded. The use of new sources of information by AI makes it possible to reveal weak signals, whether in the form of interactions or non-linearities between the explanatory variables for the assessment of customer creditworthiness. Thus, the predictive improvement of this new process of credit analysis sometimes translate, at the macroeconomic level, into positive estimates for economic growth and, at the microeconomic level, by individual gains, in particular by improving financial inclusion and access to credit for the most vulnerable borrowers. Based on this, the synthesized case study of Fintech Linddo's experience in setting up a credit assessment for women-led SMEs in Ethiopia presented very encouraging results. However, these new bank credit analysis processes can raise many biases and especially ethical, legal and regulatory questions. These limits undoubtedly call for the establishment of a new form of financial regulation based on the certification of AI algorithms and data mobilized by banks.