Exploring the role of artificial intelligence and machine learning for financial intelligence in Nigeria

Abstract

Developing countries, especially in Africa, lag behind developed countries in utilizing artificial intelligence to detect and identify financial fraud and corruption incidents. However, with the fight against corruption and the increasing demand for accountability, artificial intelligence can address this gap. Artificial has already gained popularity and acceptance in nations such as the United States and China and is being used in the banking sector to reduce financial fraud. Nigeria, the most populous African country, can build upon the success of implementing and artificial intelligence in the nation's financial system. This exploratory study seeks to explore how the Economic and Financial Crimes Commission (EFCC) could leverage some of the artificial intelligence techniques to identify and detect instances of financial fraud and corruption. This exploratory study identifies the possibilities for integrating machine learning and artificial intelligence techniques to detect and identify fraud. The reviewed literature findings reveal that Radial Basis Functions (RBF) networks and Active Learning Through Sequential Design (ALSD) methods were more effective than other models in detecting money laundering.