PresentationGFFC_CornellUniversityAI.pptx

PresentationGFFC_CornellUniversityAI.pptx

• 1.
  AI in FinancialDecision-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 ofAI 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 InvestmentStrategies •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 inInvestment 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 inInvestment 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 RiskManagement •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 RiskManagement Cont. •Market Risk Analysis: •AI models analyze market data to identify potential risks and opportunities and helps 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 inRisk 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 ofAI/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 Traditionalvs AI MLApproaches
• 12.
  Challenges and Limitations •DataPrivacy 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 withBlockchain: •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'sImpact: •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: Financialfirms 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.
• 16.
  References . [1] S. Ranjan,D. R. Gupta, and D. A. Gupta, “Artificial intelligence in financial acumen: Challenges and opportunities,” Cosmos Journal of Engineering & Technology, 10, 1, 1 (2020). [2] Y. Cui, “Sophia Sophia tell me more, which is the most risk-free plan of all? AI anthropomorphism and risk aversion in financial decision- making,” International Journal of Bank Marketing, 40, 6, 1133 (2022). [3] J. Ren, “Research on financial investment decision based on artificial intelligence algorithm,” IEEE Sensors Journal, 21, 22, 25190 (2021). [4] J. Cernevi ˇ cien ˇ e˙ and A. Kabaˇsinskas, “Review of multi-criteria decision-making methods in finance using explainable artificial intelligence,” Frontiers in artificial intelligence, 5, 827584 (2022). [5] M. Stone, E. Aravopoulou, Y. Ekinci, G. Evans, M. Hobbs, A. Labib, P. Laughlin, J. Machtynger, and L. Machtynger, “Artificial intelligence (AI) in strategic marketing decision-making: a research agenda,” The Bottom Line, 33, 2, 183 (2020). [6] L. Cao, “Ai in finance: challenges, techniques, and opportunities,” ACM Computing Surveys (CSUR), 55, 3, 1 (2022). [7] S. Sachan, J.-B. Yang, D.-L. Xu, D. E. Benavides, and Y. Li, “An explainable AI decision-support-system to automate loan underwriting,” Expert Systems with Applications, 144, 113100 (2020). [8] F. Strich, A.-S. Mayer, and M. Fiedler, “What do I do in a world of artificial intelligence? Investigating the impact of substitutive decision- making AI systems on employees’ professional role identity,” Journal of the Association for Information Systems, 22, 2, 9 (2021).
• 17.
  References . [9] N. Nandal,A. Singh, M. Kumar, and R. Tanwar, “Healthcare based financial decision making system using artificial intelligence,” International Journal of Health Sciences, 11,255– 11,267 (2022). [10] M. Shanmuganathan, “Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions,” Journal of Behavioral and Experimental Finance, 27, 100297 (2020). [11] N. Rane, “Role and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Finance and Accounting,” Available at SSRN 4603206 (2023). [12] V. Lai, C. Chen, Q. V. Liao, A. Smith-Renner, and C. Tan, “Towards a science of human-ai decision making: a survey of empirical studies,” arXiv preprint arXiv:2112.11471 (2021). [13] B. Mart´ınez, R. Allmendinger, H. A. Khorshidi, T. Papamarkou, A. Feitas, J. Trippas, M. Zachariadis, N. Lord, and K. Benson, “Mapping the State of the Art: Artificial Intelligence for Decision Making in Financial Crime,” Cybersecurity for Decision Makers, 199–213 (2023). [14] V. Singh, S.-S. Chen, M. Singhania, B. Nanavati, A. Gupta et al., “How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda,” International Journal of Information Management Data Insights, 2, 2, 100094 (2022). [15] D. Mhlanga, “Industry 4.0 in finance: the impact of artificial intelligence (ai) on digital financial inclusion,” International Journal of Financial Studies, 8, 3, 45 (2020).
• 18.
  References . [16] O. Melnychenko,“Is artificial intelligence ready to assess an enterprise’s financial security?” Journal of Risk and Financial Management, 13, 9, 191 (2020). [17] C. Hildebrand and A. Bergner, “Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making,” Journal of the Academy of Marketing Science, 49, 4, 659 (2021). [18] A. Ashta and H. Herrmann, “Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance,” Strategic Change, 30, 3, 211 (2021). [19] P. Polak, C. Nelischer, H. Guo, and D. C. Robertson, ““Intelligent” finance and treasury management: what we can expect,” Ai & Society, 35, 3, 715 (2020). [20] J. W. Goodell, S. Kumar, W. M. Lim, and D. Pattnaik, “Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis,” Journal of Behavioral and Experimental Finance, 32, 100577 (2021). [21] A. M. Musleh Al-Sartawi, K. Hussainey, and A. Razzaque, “The role of artificial intelligence in sustainable finance,” (2022). [22] G. Singh, V. Garg, and P. Tiwari, “Application of artificial intelligence on behavioral finance,” Recent Advances in Intelligent Information Systems and Applied Mathematics, 342– 353, Springer (2020). [23] J. K. Hentzen, A. Hoffmann, R. Dolan, and E. Pala, “Artificial intelligence in customerfacing financial services: a systematic literature review and agenda for future research,” International Journal of Bank Marketing, 40, 6, 1299 (2022).
• 19.
  References . [24] E. Mogajiand N. P. Nguyen, “Managers’ understanding of artificial intelligence in relation to marketing financial services: insights from a cross-country study,” International Journal of Bank Marketing, 40, 6, 1272 (2022). [25] G. Cao, Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decisionmaking,” Technovation, 106, 102312 (2021) [26] M. James, “The Role of Artificial Intelligence in Financial Decision Making,” Available at SSRN 4628237 (2023). [27] X. Wang and M. Yin, “Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making,” 26th international conference on intelligent user interfaces, 318–328 (2021). [28] S. Gupta, S. Modgil, S. Bhattacharyya, and I. Bose, “Artificial intelligence for decision support systems in the field of operations research: review and future scope of research,” Annals of Operations Research, 308, 1, 215 (2022). [29] F. Konigstorfer ¨ and S. Thalmann, “Applications of Artificial Intelligence in commercial banks–A research agenda for behavioral finance,” Journal of behavioral and experimental finance, 27, 100352 (2020). [30] R. P. Buckley, D. A. Zetzsche, D. W. Arner, and B. W. Tang, “Regulating artificial intelligence in finance: Putting the human in the loop,” Sydney Law Review, The, 43, 1, 43 (2021). [31] A. A. A. Ahmed, A. Asadullah, and M. ShakawatHossain, “Impact of artificial intelligence and automation technologies on financial management,” PalArch’s Journal of Archaeology of Egypt/Egyptology, 17, 6, 10311 (2020).
• 20.
  References . [32] E. Kurshan,J. Chen, V. Storchan, and H. Shen, “On the current and emerging challenges of developing fair and ethical AI solutions in financial services,” Proceedings of the second ACM international conference on AI in finance, 1–8 (2021). [33] E. A. COS¸KUN, “The Role of Artificial Intelligence in Investment Decisions and Applications in The Turkish Finance Industry,” Gaziantep University Journal of Social Sciences, 21, 4, 2208 (2022). [34] B. S. Campello, L. T. Duarte, and J. M. Romano, “Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach,” ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5435– 5439, IEEE (2020) [35] E. Mogaji, T. O. Soetan, and T. A. Kieu, “The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers,” Australasian Marketing Journal, j–ausmj (2020). [36] R. Petkov, “Artificial intelligence (AI) and the accounting function—A revisit and a new perspective for developing framework,” Journal of emerging technologies in accounting, 17, 1, 99 (2020). [37] E. Nica, O.-M. Sabie, S. Mascu, and A. G. Lut¸an, “Artificial intelligence decision-making in shopping patterns: consumer values, cognition, and attitudes,” Economics, management and financial markets, 17, 1, 31 (2022). [38] F. Hashem and R. Alqatamin, “Role of artificial intelligence in enhancing efficiency of accounting information system and non-financial performance of the manufacturing companies,” International Business Research, 14, 12, 1 (2021).
• 21.
  References . [39] M. Godboleand H. P. Josyula, “NAVIGATING THE FUTURE: A COMPREHENSIVE ANALYSIS OF AI, ML, ERP, AND ORACLE INTEGRATION IN FINANCIAL DIGITAL TRANSFORMATION,” International Journal of Computer Engineering and Technology, 15 (2024). [40] M. Nagy, G. Laz˘ aroiu ˘ , and K. Valaskova, “Machine Intelligence and Autonomous Robotic Technologies in the Corporate Context of SMEs: Deep Learning and Virtual Simulation Algorithms, Cyber-Physical Production Networks, and Industry 4.0-Based Manufacturing Systems,” Applied Sciences, 13, 3, 1681 (2023). [41] A. F. Borges, F. J. Laurindo, M. M. Sp´ınola, R. F. Gonc¸alves, and C. A. Mattos, “The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions,” International Journal of Information Management, 57, 102225 (2021). [42] M. Ghobakhloo and N. T. Ching, “Adoption of digital technologies of smart manufacturing in SMEs,” Journal of Industrial Information Integration, 16, 100107 (2019). [43] K. Omoteso, “The application of artificial intelligence in auditing: Looking back to the future,” Expert Systems with Applications, 39, 9, 8490 (2012). [44] M. H. D. SHARKAWY, “Potential applications of collaborative intelligence technologies in manufacturing: study of applicability of collaborative intelligence technologies in manufacturing small-and-medium enterprises, collaborative intelligence frameworks, application benefits and adoption barriers,” (2020).