Machine Learning Models for Financial Inclusion in Malaysia: Opportunity for Insurance or Takaful in Achieving Financial Inclusion

Financial inclusion is one of the means by which Malaysians may realize the Shared Prosperity Vision 2030. Conventional insurance or Islamic insurance (Takaful) could play a vital role in enhancing socioeconomic results in a country with a Muslim majority. However, the restricted adoption of insurance products poses a challenge to the expansion of financial inclusion in Malaysia. To surmount this, a robust model that accurately forecasts the determinants of insurance adoption is imperative. This research harnesses the power of machine learning models to predict insurance uptake in Malaysia, using the Global Findex 2021 Database as a foundation. The decision tree model with data training displayed the best precision and accuracy. The most important elements in forecasting insurance uptake as extracted from the random forest model were income, employment status and education. Furthermore, the main barrier to financial inclusion is the lack of documentation. The findings suggest there is an opportunity for Takaful players to engage with the government to develop Takaful products to cater to lower-income and unemployed populations. In addition, the government should play a critical role in promoting financial literacy and encouraging financial digitalization in ensuring everyone is more financially savvy moving Malaysia towards a financially inclusive society. The application of machine learning techniques within Malaysia's unique financial inclusion landscape constitutes a noteworthy contribution. This study yields insights that can inform strategic policy formulations and industry practices.