Enhancing Financial Forecasting Accuracy Through AI-Driven Predictive Analytics Models

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@article{1702765,
      author = {Muhammad Ashraf Faheem, Muhammad Aslam, Sridevi Kakolu},
      title = {Enhancing Financial Forecasting Accuracy Through AI-Driven Predictive Analytics Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {12},
      pages = {322-328},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702765.pdf},
      abstract = {Artificial Intelligence (AI) has completely transformed the way financial forecasting is done to improve the way risk assessment and decision making is done. To understand the use of AI in the financial industry, we evaluate the capability of AI-powered predictive analytics, which shows benefits in improving risk evaluation and backing up decision-making. Utilizing machine learning algorithms, neural networks, and big data analytics, AI models can analyze vast quantities of financial data in real-time, uncovering insights that might go unnoticed with conventional techniques. These capabilities allow financial institutions to foretell market fluctuations, estimate credit risks, and optimize investment strategies. Furthermore, AI models can learn by adapting and improving over time, bringing them more in line with a dynamic market state. Yet, the paper also confronts the obstacles of AI-enabled financial prediction: data security concerns, model interpretability, and ethical factors related to automated choice-making. In essence, this research presents the far-reaching use of AI in financial forecasting and its essential part in creating a more resilient and knowledgeable financial world.},
      keywords = {Financial forecasting, Predictive analytics, Artificial intelligence, Machine learning, Deep learning},
      month = {June},
  }