PresentationGFFC_CornellUniversityAI.pptx

Ai research paper based presentation

1. AI in Financial Decision-Making: Revolutionizing Investment Strategies and Risk Management Presented By: Mayank Keshan Authors: Aaryan Gupta , Mayank Puri Goswami, Mayank Keshan , Varun Tiwari 2024 Global Forum for Financial Consumers (GFFC) August 9, 2024

2. Table of Contents Abstract Introduction AI in Investment Strategies AI in Risk Management Challenges and Limitations Future Trends and Ethical Practices Conclusion References

3. Introduction Importance of AI in Finance: AI is transforming financial decision-making processes by enabling more precise and efficient strategies. Enhances the ability to analyze large volumes of data quickly and accurately. Overview of AI Applications: Focus on AI in investment strategies and risk management. AI technologies such as machine learning, neural networks, and natural language processing. Objectives of the Presentation: Explore AI's role in financial decision-making. Provide case studies demonstrating practical applications. Discuss challenges and future trends in AI-driven finance.

4. AI in Investment Strategies Algorithmic Trading: Uses AI to execute trades based on complex algorithms and large data sets. Reduces human error, increases trading speed, and improves market efficiency. Examples: High-frequency trading and quantitative trading strategies. Robo-Advisors: Automated platforms providing investment advice and portfolio management. Utilize AI to create personalized investment strategies based on individual risk profiles and goals. Examples: Betterment, Wealthfront . Sentiment Analysis: AI analyzes news articles, social media, and other sources to gauge market sentiment. Helps predict market movements and investor behavior. Example: Analyzing Twitter sentiment to forecast stock price movements.

5. Case Studies in Investment Strategies Hedge Funds: Use AI for predictive analytics, leading to enhanced returns and risk management. Example: Renaissance Technologies' Medallion Fund, which leverages AI for high-frequency trading. Percentage Use of AI in Hedge Funds : According to a 2020 Preqin report, approximately 40% of hedge funds are now using AI and machine learning to inform investment decisions and manage risks.

6. Case Studies in Investment Strategies Cont. Retail Investment Platforms: Offer AI-driven recommendations for individual investors. Examples: Robinhood, which uses AI to provide investment insights and personalized advice. AI tools that enable novice investors to make informed decisions. Growth of AI in Retail Platforms: The adoption of AI in financial advising and retail investment platforms is expected to manage approximately 10-15% of total US investable assets by 2025, according to a report from Deloitte .

7. AI in Risk Management Credit Risk Assessment : AI models predict default risks by analyzing historical data and credit behavior. Reduces exposure to bad loans and improves lending decisions. Example: ZestFinance , which uses machine learning to assess credit risk. Fraud Detection: AI monitors transactions in real-time to detect and prevent fraudulent activities. Uses pattern recognition and anomaly detection. Examples: Fraud Detection Savings: According to a report by Juniper Research, AI-driven fraud detection and prevention measures are projected to save businesses approximately $12 billion annually by 2023.

8. AI in Risk Management Cont. Market Risk Analysis: AI models analyze market data to identify potential risks and opportunities and h elps in creating robust risk mitigation strategies. Example: BlackRock's Aladdin platform, which uses AI to manage investment risks. A survey by Deloitte noted that 50% of financial services institutions plan to increase their investment in risk management capabilities, with a significant focus on AI and analytics over the next two years . Efficiency in Compliance: McKinsey estimates that AI technologies can reduce compliance costs by 30% to 40% through improved accuracy and automation . Credit Decision Speed: Wells Fargo reported that by using AI and machine learning for credit decisions, the processing time has been reduced from several days to just minutes.

9. Case Studies in Risk Management Banks and Financial Institutions: Use AI for credit scoring and loan approvals. Example: JP Morgan Chase's COiN , which processes legal documents and identifies risks. which reduced the amount of time lawyers and loan officers need to spend on these documents by 360,000 hours annually . Goldman Sachs : Employs AI and ML in trading, equity research, and risk assessment. Citigroup : Utilizes AI to enhance real-time transaction monitoring to identify unusual patterns that may suggest fraudulent activity Insurance Companies: Employ AI for claims processing and fraud detection. Example: Lemonade, which uses AI to process claims in seconds and detect fraudulent claims. Result: Enhanced operational efficiency and reduced losses.

10. Adoption Rates of AI/ML in the Financial Sector Growth in Adoption : According to a report by the Bank of England , the use of machine learning in UK financial services has been growing rapidly, with two-thirds of the surveyed firms already implementing machine learning in some form. Percentage of Adoption : Research by the World Economic Forum highlighted that over 60% of financial services firms have embedded at least one AI capability , which indicates a significant uptake in these technologies compared to previous years. Global Trend : A Deloitte survey suggests that around 70% of all financial services firms are using machine learning to predict cash flow events, adjust credit scores, and detect fraud.

11. Comparison of Traditional vs AI ML Approaches

12. Challenges and Limitations Data Privacy Concerns : Handling sensitive financial data securely is crucial. Compliance with data protection regulations like GDPR and CCPA. Algorithmic Bias: Ensuring AI models are fair and do not perpetuate biases present in training data. Impact: Biased models can lead to unfair lending practices and discrimination. Regulatory Compliance: Adhering to financial regulations and standards is essential. Examples: Ensuring AI models comply with Basel III regulations for banking.

13. Future Trends Integration with Blockchain: Combining AI with blockchain enhances transparency and security in financial transactions. Examples: Smart contracts and decentralized finance (DeFi) platforms. Advancements in AI Models: Development of more sophisticated AI models for better prediction and decision-making. Example: Explainable AI (XAI) for transparent and understandable AI decisions. AI and Quantum Computing: Potential for exponential improvements in financial modeling and decision-making processes. Example: Quantum AI for solving complex optimization problems in finance.

14. Conclusion Summary of AI's Impact: AI significantly enhances investment strategies and risk management. Provides precise, efficient, and data-driven decision-making capabilities. Transformative Potential: AI is revolutionizing the financial sector with continuous advancements. Encourages innovation and improved financial practices. Future Directions: Ongoing research and development in AI applications. Potential for further integration with emerging technologies like blockchain and quantum computing .

15. Conclusion . Efficiency Improvements: Financial firms using AI report a 22% reduction in operational costs on average due to improved efficiency and automation. Customer Experience: According to a survey by Accenture, banks that scale AI report a 10% to 15% increase in revenue due to enhanced personalized service offerings and customer engagement strategies. Increased Adoption of AI-Driven Decision Making: It is predicted that by 2030, AI technologies will be integral to the transaction process in nearly all top global financial institutions , streamlining processes like underwriting, loan origination, and customer onboarding. The trends used in whole research are discussed in publications by major consulting firms like McKinsey & Company, Boston Consulting Group, and others who regularly publish insights on technology advancements and their impacts on various industries, including finance.

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