In an age of unprecedented economic and financial change, predictive analytics has become an essential tool for financial institutions seeking to stay ahead of the curve and avoid potential risks. With the rapid advancement of AI technology, unprecedented capabilities have emerged in the field of financial predictive analytics, redefining how economic trends are forecasted and financial risks are managed.

Financial predictive analytics, using AI, enables organizations to anticipate and react to changes in the markets more effectively than ever before. Instead of relying solely on traditional historical data and static statistical models, AI provides advanced tools such as machine learning and big data analytics to analyze massive amounts of information and provide accurate insights into future trends.

AI-powered predictive algorithms are more flexible and powerful, as they can process unstructured data such as economic news and social data, and recognize complex patterns that may be invisible to traditional methods. By analyzing these patterns, AI can provide informed predictions about financial performance, asset prices, and economic trends, helping companies make strategic decisions based on accurate information.

Who Should Attend?

• Financial analysts.
• Fund managers and institutional investors.
• Financial risk managers.
• Financial advisors.
• Entrepreneurs and startup owners.
• Data managers and analysts.
• Financial experts in large corporations.
• People interested in AI technology in finance.

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• The concept of predictive analytics and its importance in the financial field.
• Using historical data and statistical models to predict future financial trends.
• The foundations of artificial intelligence, machine learning and deep learning techniques.
• Using artificial intelligence in financial analysis and trend forecasting.
• Software tools and techniques used in predictive analysis.
• Collecting and cleaning big data related to financial affairs.
• Using artificial intelligence to analyze big data and extract accurate insights.
• Using predictive analytics to improve investment strategies and make informed investment decisions.

• Introduction to Financial Predictive Analytics
  • Definition of predictive analytics and its importance in the financial context.
  • Difference between predictive and prescriptive analytics.
  • How predictive analytics contribute to improving investment strategies, risk management, and financial planning.
• Introduction to Artificial Intelligence (AI)
  • Foundations of AI, machine learning, and deep learning.
  • Key technologies: machine learning, neural networks, optimization algorithms.
  • Uses of AI in the financial field, such as stock price prediction and fraud detection.
• Collecting and processing big data
  • Sources of financial data (historical data, live data, unstructured data).
  • Data cleaning and preparation techniques for analysis.
  • Big data analysis tools and techniques, such as Hadoop and Spark.
• Building predictive models
  • Designing and building predictive models using AI.
  • Common algorithms in predictive analytics such as linear regression, random forests, and neural networks.
  • Evaluating model performance using indicators such as prediction accuracy, F1-score, and ROC curve.
• Applying predictive analytics in risk management and investment strategies
  • Using predictive analytics to identify investment opportunities and manage portfolios.
  • Using predictive models to assess risk and improve risk management strategies.
• Dealing with ethical and regulatory challenges
  • Ethical challenges associated with the use of AI in financial analytics.
  • Laws and regulations related to AI and financial data analytics.
• Developing strategic decision-making skills
  • Using the results of predictive analytics in strategic decision-making.
  • Analyzing and interpreting the results of predictive models to develop effective financial strategies.