AI in Financial Modeling: Advanced Predictive Techniques
AI, financial modeling, predictive analysis, machine learning, neural networks, natural language processing, risk assessment, investment strategies, data management, feature engineering, time-series forecasting, ethics, regulatory compliance
Skills you will gain:
This 12-week course is designed for M.Tech and M.Sc students in computer science and IT, as well as professionals in the BFSI, analytics, and fintech sectors. It covers machine learning algorithms, neural networks, and natural language processing to interpret and predict financial outcomes. Through video lectures, hands-on labs, and live expert sessions, participants will learn to develop and implement AI models to drive financial decisions and strategies.
Aim: The course aims to provide an in-depth understanding of how artificial intelligence can revolutionize financial modeling through enhanced decision-making and predictive analysis. The course aims to equip participants with the skills to apply AI in analyzing stock markets, assessing risks, and optimizing investment strategies.
Program Objectives:
- Understand AI in Financial Contexts: Grasp the role of AI in financial modeling and decision-making.
- Develop Predictive Models: Learn to create and use various AI-driven predictive models for financial analysis.
- Practical AI Application: Gain hands-on experience in applying AI tools like Python, R, and TensorFlow to financial datasets.
- Ethical and Regulatory Insights: Navigate the ethical and regulatory challenges of implementing AI in financial environments.
What you will learn?
- Introduction to AI in Financial Modeling:
- Learn the foundations of financial modeling and the significant role AI plays in enhancing financial decision-making.
- Gain insights into AI technologies like machine learning and deep learning, and their applications in finance.
- Data Management and Preprocessing:
- Understand the sources and management of financial data crucial for AI modeling.
- Explore data quality, preprocessing, and feature engineering techniques to prepare data for effective AI analysis.
- Advanced Predictive Modeling Techniques:
- Dive into time-series forecasting using models like ARIMA and LSTM networks to predict market trends.
- Study risk management models, including credit risk modeling and market risk prediction, using AI.
- Implementing AI Models:
- Learn about the integration of AI models with financial systems, including APIs and real-time data feeds.
- Address the challenges and solutions in deploying AI models within financial environments.
- Ethics and Regulation:
- Discuss the ethical considerations and regulatory compliance issues relevant to AI in finance.
Intended For :
- Students: M.Tech, M.Sc in Computer Science, IT, and related fields.
- Professionals: E0 & E1 level professionals in BFSI, analytics services, and fintech IT services, looking to enhance their skills in AI-driven financial strategies.
Career Supporting Skills
