<![CDATA[Time series analysis is used to identify patterns in sequential data over time, utilizing previous observations. Forecasting is the process of predicting or estimating future events based on available data. This study focuses on forecasting life insurance reserves for catastrophic risks using the ARIMA (AutoRegressive Integrated Moving Average) method. The data used comes from the Financial Services Authority Banking Statistics, covering the period from August 2017 to December 2023. Catastrophic risk includes losses caused by natural events or accidents that can result in significant harm to a company. The ARIMA method is used to predict future outcomes based on historical data, aiming to provide accurate estimates of the reserves needed. Accurate predictions are crucial in helping companies allocate funds more efficiently and reduce unforeseen financial risks. The steps taken in this study include data collection, model identification, parameter estimation, and model diagnosis to ensure the accuracy of the generated forecasts.]]>
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