Predictive Analysis of the Impact of Exchange Rate Fluctuations on the Financial Performance of Multinational Fintech Companies in Indonesia

Abstract

Exchange rate fluctuations are one of the main risks affecting the financial stability of multinational companies, including the digital banking sector in Indonesia. This study aims to analyze the impact of exchange rate fluctuations on the company's financial performance by using a case study on Bank BNI Digital. The financial data used covers the period January 2015 to December 2024, including variables such as exchange rate (IDR/USD), revenue, operating expenses, net profit, equity, liabilities, and exchange rate volatility. A machine learning-based predictive approach was applied through the Random Forest and XGBoost algorithms to evaluate the relationship between exchange rate fluctuations and the company's financial performance. The results showed that exchange rate fluctuations have a weak linear relationship with financial performance, especially company revenue, with a correlation coefficient of 0.01. Nevertheless, the simulation of the impact of the exchange rate on net profit shows that the company is able to maintain financial stability in a scenario of moderate exchange rate changes (±15%). The feature importance analysis of the XGBoost model shows that revenue and operating expenses are the dominant factors affecting financial performance, while exchange rates contribute less. Based on these findings, the study recommends the implementation of forward contracts to manage exchange rate risk, natural hedging strategies to balance currency exposure, and optimization of operational efficiency as a risk mitigation measure. This research provides strategic insights for Bank BNI Digital and similar companies in designing a resilient risk management strategy against exchange rate fluctuations in the global market.