Abstract
This study employs Artificial Intelligence (AI) and Machine Learning (ML) methodologies to develop advanced predictive finance strategies aimed at enhancing the accuracy of financial forecasting, improving risk assessment and supporting data-driven investment decision-making. In order to provide companies with the ability to forecast market swings, improve resource allocation and build financial resilience, the study aims to integrate traditional financial modelling with data-driven predictive analytics. The research employs quantitative and experimental methodology, including some of the most advanced machine learning techniques, such as Random Forest, Gradient Boosting and Long Short-Term Memory (LSTM) networks, in order to forecast time-series data pertaining to the financial sector. Python-based frameworks are utilized in order to investigate historical datasets derived from stock indexes, macroeconomic indicators and business performance reports. Both Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are employed to evaluate the models performance subsequent to k-fold cross-validation – based training and validation.In addition, the predictive power of the models is improved through the utilization of sentiment analysis of financial news and trends in social media. When compared to more conventional methods, it is predicted that the study will demonstrate a significant improvement in terms of both the accuracy of predictions and the management of risks. The predictive framework that has been built will make it easier for investors, politicians and financial institutions to make decisions based on data. The paper makes the proposition that the combination of artificial intelligence and financial analytics will result in increased transparency, efficiency and sustainability within the financial systems. This will ultimately lead to the development of more intelligent investment ecosystems.
Keywords
- Predictive Analytics
- Financial Forecasting
- Machine Learning
- Risk Management
- Artificial Intelligence
How to cite
[23] V. Bogojević Arsić, “Challenges of Financial Risk Management: AI Applications,” Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, vol. 26, no. 3, pp. 27–34, 2021, doi: 10.7595/management.fon.2021.0015
References (2)
- 1.Dr. Madhav SaraswatAssistant Professor, Institute of Business Studies, ChaudharyCharan Singh University, Meerut, Uttar Pradesh, IndiaEmail ID: saraswat.madhav@gmail.com
- 2.Ms. ShalluResearch Scholar, Institute of Business Studies, ChaudharyCharan Singh University, Meerut, Uttar Pradesh, IndiaEmail ID: shallutaneja94@gmail.com