Artificial Intelligence in Modern Finance: Pathways to Smarter, Safer, and Faster Systems

Abstract

This paper examines the applications of artificial intelligence (AI) in various areas of finance, including corporate performance, real estate investment, banking, fraud detection, creditscoring, and investor sentiment analysis. AI techniques, such as machine learning and neural networks, have been shown to outperform traditional models in predicting financial outcomes and assessing risks. These advancements lead to more accurate decision-making, reducedbiases, and improved efficiency. AI also enhances fraud detection systems, credit scoring accuracy, and derivative pricing. Despite these benefits, challenges remain in terms of model interpretability, privacy concerns, and regulatory compliance. This research underscores AI\'s transformative impact on financial sectors, offering precise insights and strategic advantages. The continued evolution ofAI technologiesis expected to further reshape financial practices.

Introduction

A. Needs

AI is transforming the financial industry by improving:

• Efficiency and accuracy

• Risk management through fraud detection and compliance

• Trading strategies via algorithmic decision-making

• Customer service using chatbots and virtual assistants

• Credit scoring, loan approvals, and personalized planning
  Overall, AI helps streamline operations, reduce costs, and enhance decision-making for financial institutions and customers alike.

B. Definition

AI in finance refers to the use of technologies like:

• Machine Learning (ML)

• Natural Language Processing (NLP)

• Predictive Analytics

Applications include:

• Fraud detection

• Algorithmic trading

• Credit scoring

• Customer support

• Compliance and risk management

AI helps analyze large datasets in real-time, enhancing speed, accuracy, security, and personalization in financial services.

C. Importance

AI’s impact on finance is multifaceted:

• Fraud detection: Real-time pattern analysis helps prevent cybercrime.

• Trading: AI processes large market datasets to execute trades faster and more profitably.

• Risk management: Better credit and market risk analysis reduces financial losses.

• Customer experience: Chatbots and virtual assistants offer 24/7 personalized support.

• Compliance: Ensures regulatory adherence while optimizing decision-making processes.

II. Literature Review – Key Research Themes

A wide body of literature highlights AI’s application across finance:

1. Fraud Detection

• West & Bhattacharyya (2016): AI improves real-time fraud detection using supervised learning.

• Limitation: Requires continuous updates and suffers from ethical/data concerns.

2. Algorithmic and High-Frequency Trading

• Kirilenko & Lo (2013), Brummer & Yadav (2019): AI reduces human bias and optimizes trade execution; 40-70% of trading is now algorithmic.

3. Market Efficiency & Sentiment Analysis

• Feng et al. (2021), Hasan et al. (2020): AI enables faster price discovery and improved liquidity, but can lead to overreactions.

• Tools like FinBERT used for ESG analysis and green investment.

4. Predictive Modeling & Forecasting

• Zhang et al. (2020), Jiang et al. (2017): Deep learning enhances forecasting accuracy for stock trends, forex, volatility, and bankruptcy.

• AI models like LSTM, SVM, and Neural Networks outperform traditional systems.

5. Portfolio Management

• Parisi & Manaog (2023), Soleymani & Vasighi (2020): AI techniques like DRL and genetic algorithms improve asset allocation and risk-return dynamics.

6. Real Estate & Banking

• AI forecasts real estate investments, evaluates banking performance, and identifies key risks, including regulatory and economic factors.

7. Robo-Advisors & Personalized Finance

• AI-powered advisors (e.g., by Thompson & Liu, 2019) automate investment management with customized strategies.

8. Credit Scoring & Default Prediction

• Jones et al. (2015), Martinez & Roberts (2020): AI integrates alternate data to enhance credit scoring and reduce loan bias.

• Tools like Random Forest and Adaboost improve accuracy and reduce human bias.

III. Comparative Review of Top 5 Papers

Key Trends and Challenges Identified

• Benefits: Higher accuracy, better risk control, efficiency in financial operations

• Challenges:

  • Data privacy and model transparency

  • Integration with legacy systems

  • Need for regulatory frameworks and ethical guidelines

• Future Directions:

  • Improved explainability of AI models

  • Cross-sector collaboration

  • Addressing systemic risks and algorithmic fairness

Conclusion

AI is revolutionizing finance, banking, and real estate by enhancing fraud detection, risk management, credit scoring, sentiment analysis, and market forecasting. This topic is crucial as AI-driven models outperform traditional methods, addressing growing data complexity, evolving fraud tactics, and the need for unbiased financial decision-making. The objective is to explore AI’s role in optimizing trading, investment strategies, and risk assessment while improving market efficiency. The goal is to enhance accuracy, reduce biases, and refine financialprocesseswhile addressingethical and regulatorychallenges.Lookingahead, advancements in deep learning, reinforcement learning, and quantum computing will further refine AI-driven trading, robo- advisors, and sentiment analysis, though challenges in data privacy, compliance, and model interpretability must be addressed for responsible AI adoption in finance.

References

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Copyright © 2025 Dr. Goldi Soni, Sakshi Singh, Deepanjali Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.