Ever had an idea which you thought could be turned into an enterprise; then figured out that it would be too difficult to find sufficient funding in order to realize it? In developed economies, the existence of endless complex venture ideas must compete through structured and innovative business plans to gain the investors’ capital investment. This is also the case for developing economies, however, due to the nature of a developing market many ideas generated in such conditions are simpler and require much less finance. Despite simple business models with huge potential upside, entrepreneurs in developing countries struggle to find sources of finance. This is simply because there are multiple non-business related risk factors involved in providing credit in such less controllable conditions (Barrowclough, 2018). So here we are faced with an opportunity to solve poverty while also generating return on investment but the influence of uncontrollable, yet reliable, data has prohibited its success (Saini, 2019). The obvious solution? Big data analytics and AI.
AI can help financiers not only to provide more informed loans but also to comply with dynamic local laws, regulations which govern local financial markets (Saini, 2019). Moreover, blockchain technologies improves the availability of reliable and qualitative data from which AI could be fed to generate intelligent insight to help microfinanciers accurately distribute their funds. On the other side of the market, AI is also helping entrepreneurs discover possibilities of financing sources, through targeted ads and AI driven search engines.
Historically, microfinancing in developing countries has proven to generate increased business activity while also giving a return on capital for the investors, however, there is not substantial evidence proving that microfinancing has led to reductions in income and gender inequalities (Saini, 2019). Microfinancing has also not reached out to the poorest of the poor, due to their lack of knowledge and resources to ever discover such opportunities. Reaching out to such groups has become a possibility today, given that a portion of such groups have gained access to mobile phones and can transfer funds and access social media.
Usage of mobile phones in the poorest of the poor areas, has enabled the collection of data from previously unexamined areas. Individuals’ activities on social media are also fed to AI algorithms to provide a basis to the generation of creditworthiness and general character of a user (Saini, 2019). In order to not only achieve economical growth but also economic development such as gender equality; the AI algorithms must be developed from a socially responsible perspective, not with the purpose of maximizing monetary returns but achieving improved social development (Su, 2019). It is doubtful that private financial institutions will use AI in socially responsible ways; which is why local regulations need to adapt in order to respond to the endless advancements in AI and data analytics.
References:
Barrowclough, D. 2018, THE INS AND OUTS OF INCLUSIVE FINANCE: SOME LESSONS FROM MICROFINANCE AND BASIC INCOME. Viewed on 06 October, 2020 from: https://unctad.org/en/PublicationsLibrary/gdsmdp2017d3_en.pdf
Saini, M. 2019, AI’s role in the world of Microfinance. Viewed on 06 October, 2020 from: https://medium.com/@mridulasaini/ais-role-in-the-world-of-microfinance-12a1e2bcf5f0
Su, D. 2019, How social media marketing applied on MFIs. Viewed on 06 October 2020 from: https://www.academia.edu/40020730/How_social_media_marketing_applied_on_MFIs
