Application of XGBoost Algorithm to Develop Mutual Fund Marketing Prediction Model for Banks’ Wealth Management - Jen-Ying Shih (Engineering Proceedings, 2025)
Competition in Taiwan’s banking industry is becoming fierce. Banks’ traditional income based on interest rates is insufficient to support their growth. Therefore, banks are eager to expand their wealth management business to increase profits. The fee income from the sale of mutual funds is one of the major sources of banks’ wealth management business. The problem is how to look for the right customers and contact them effectively. Therefore, it is necessary to develop classification prediction models for these banks to evaluate their customers’ potential to buy mutual fund products sold by commercial banks and then deploy marketing resources on these customers to increase banks’ profits. Recently, the XGBoost algorithm has been widely used in conducting classification tasks. Therefore, using the eXtreme Gradient Boosting algorithm, a mutual fund marketing prediction model is developed based on a commercial bank’s data in this study. The results show that whether a customer has an unsecured loan, a customer’s amount of assets in the bank, the number of months for transactions, a place of residence, and whether the bank is the main bank for the total amount of credit card bills in the past six months are the top five factors for the models, providing valuable information for effective wealth management and marketing.