ML-Based Scenario Modeling and Forecasting for Oracle Cloud Financials
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I1P105Keywords:
Oracle Cloud Financials, Machine Learning, Scenario Modeling, Adaptive Forecasting, External Factor Integration, Geopolitical RiskAbstract
Quality financial planning and rapid scenario planning are needed in the current turbulent economic environment. The proposed Machine Learning (ML) based scenario modeling and adaptive forecasting are embedded in the Oracle Cloud Financials. The suggested system is an improvement over conventional budgeting and planning, where the system will consume internal ERP data and external macroeconomic indicators, including inflation rates, geopolitical risk indexes, exchange rates, and commodity prices, to produce data-driven, real-time forecasts. An important innovation is the adaptive what-if simulation engine, which provides dynamic adjustments to the forecasting models according to changing input conditions and external shocks. By combining a time-series model with regressions and deep learning (e.g., LSTM networks), the system will facilitate responsive financial planning in the face of uncertainty. External sources of data will be included with real-time APIs by various institutions like the World Bank and the IMF, which will make the data dynamic and allow recalibration of the same in response to different scenarios. Experiment findings reveal that an adaptive ML model outsmarts the use of static forecasting, displaying more accuracy and reactivity to macroeconomic developments. Sensitivity and shock analysis, visual scenario dashboards and integration with the budgeting and planning modules in Oracle were also introduced in the paper. Future applications will involve the integration of ESG and climate risk data, the extension to multi-cloud ERP infrastructures, and the use of explainable AI (XAI) to provide model clarity. All in all, this piece offers a scalable and intelligent forecasting framework to support more resilient and informed financial decision-making
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