For several years, artificial intelligence (AI) has been altering the banking industry, and one area where its impact has been particularly substantial is in financial modelling. Here are some of the ways AI is changing financial modelling:

· AI algorithms can examine massive amounts of financial data in real time, including historical and real-time market data. This assists financial analysts in identifying patterns and trends that would be difficult to notice manually, as well as improving the accuracy of their models.

· Many manual processes involved in financial modelling, such as data entry, data cleaning, and report generation, can be automated by AI. This allows analysts to concentrate on more difficult activities and decision-making.

· Based on historical data, AI models can be trained to anticipate future financial events such as stock prices. These forecasts can assist investors in making informed decisions and optimising their investments.

· AI models can assist financial organisations in more efficiently managing risk by evaluating market trends and spotting possible concerns before they become actual issues. This can lead to better risk management techniques and more informed decision-making.

· Large volumes of financial data can be analysed by AI algorithms to find trends and anomalies that may suggest fraudulent conduct. This can assist financial institutions in detecting and preventing fraud before it causes major damage.

Researchers and experts in finance and AI have an excellent opportunity to contribute their knowledge and ideas. The application of AI in financial modelling is still in its early stages, and there is plenty of room for further research and development.

Article topics that could be covered include:

· Case studies illustrate how artificial intelligence (AI) has been applied to improve financial models in certain industries or sectors such as banking, insurance, or investment management.

· Investigating the ethical aspects of utilising artificial intelligence in financial modelling, including bias, transparency, and accountability.

· Investigating the impact of artificial intelligence on financial modelling in emerging economies where data may be poor or untrustworthy.

· Evaluating and comparing the performance of AI models in financial forecasting and risk management to classical statistical models.

· Creating innovative AI algorithms or methodologies for financial modelling and verifying their performance via simulation or real-world applications.

Here are some references that provide an overview of the ways AI is being used in financial modelling, as well as the challenges and opportunities associated with this emerging field.

1. Zhang, Y., Du, X., & Hu, Y. (2020). Artificial intelligence in finance: A review. Intelligent Automation & Soft Computing, 26(2), 251-263.

2. Li, Q., & Xu, C. (2021). Artificial intelligence and financial modelling: A literature review. International Journal of Financial Engineering, 8(1), 2150001.

3. Lee, K., Kim, K. J., & Kim, M. K. (2020). Financial modeling using machine learning techniques: A survey and future research directions. Sustainability, 12(7), 2802.

4. Kshetri, N., Voas, J., & Lee, I. (2020). AI in finance: A review. Journal of Business Research, 109, 549-557.

5. Jiang, X., & Wang, S. (2019). Artificial intelligence in finance: Past, present, and future. Frontiers of Computer Science, 13(5), 793-810.