 Integrating AI with Traditional Financial SystemsShikha Tuteja, Ravinder Tonk, Moushumi Das, Vishal Jagota and Rajan Vohra Chapter 2 in AI in Finance:Shaping the Future of Intelligent Automation and Financial Services, 2026, pp 27-45 from World Scientific Publishing Co. Pte. Ltd. Abstract: Financial sector, being a critical component of the global economy, is increasingly in need of speed, precision, and personalization, which legacy systems are not able to provide. Artificial intelligence (AI), which employs techniques such as machine learning, natural language processing, and robotic process automation, is replacing traditional IT infrastructures and manual processes. This change has helped overcome the issues of slow decision-making, fraud detection, risk management, and regulatory compliance. AI powers its tools with predictive market analysis, real-time fraud detection, and hyper-personalized financial services, enhancing customer satisfaction and reducing operational inefficiencies. Applications range from algorithmic trading to credit scoring and virtual assistants to compliance monitoring. However, despite such integration, it presents multiple problems, including system incompatibility, data quality issues, regulatory challenges, and ethical concerns. Financial institutions would need to invest in infrastructure capital, address bias in the AI models, and nurture organizational change for effective and successful adoption. In this chapter, the transformative aspect of AI and its successful use in firms such as JPMorgan Chase and Goldman Sachs is discussed. The aspect of ethical AI is emphasized and predicted to be critical when AI and quantum computing redefine financial landscapes, fostering broader financial inclusion and innovation in the future. Keywords: Artificial Intelligence; AI in Finance; Financial Technology; FinTech; Machine Learning; Deep Learning; Neural Networks; Automation; Robotics; Intelligent Automation; Algorithmic Trading; Robo-Advisors; Predictive Analytics; Data Science; Big Data; Risk Management; Credit Scoring; Fraud Detection; AI Ethics; Responsible AI; AI Governance; Regulatory Compliance; Financial Regulations; Cybersecurity; Blockchain; Cryptocurrencies; Bitcoin; Ethereum; InsurTech; Digital Banking; AI in Banking; AI in Investments; AI in Insurance; AI in Wealth Management; AI in Payments; Natural Language Processing; AI Chatbots; Virtual Assistants; Customer Experience; Personalization; Sentiment Analysis; Credit Risk Modeling; Financial Forecasting; AI-powered Decision Making; Quantitative Finance; Trading Algorithms; High-Frequency Trading; AI in Hedge Funds; AI-driven Market Analysis; Automated Financial Services; Smart Contracts; Digital Assets; AI-driven Portfolio Management; Financial Planning; AI in Asset Management; AI in Lending; AI in Mortgage Industry; Financial Inclusion; Alternative Data; Explainable AI; Model Interpretability; AI and Human Collaboration; AI-driven Credit Analysis; Robo-Trading; Supervised Learning; Unsupervised Learning; Reinforcement Learning; AI-driven Customer Insights; Data-driven Decision Making; Financial Market Predictions; Behavioral Finance; Smart Finance; AI-based Anomaly Detection; Computational Finance; Financial Data Analytics; Financial Fraud Prevention; Future of Work in Finance; AI Strategy in Financial Firms; Financial Risk Analytics; Digital Transformation in Finance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9781800618428_0002 Ordering information: This item can be ordered from Access Statistics for this chapter More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd. |
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