Enterprise AI Analysis
Cybersecure XAI Algorithm for Generating Recommendations Based on Financial Fundamentals Using DeepSeek
This paper introduces a novel methodology to automate investment recommendations, leveraging generative AI (DeepSeek) guided by prompt engineering and neural networks for price prediction. The system incorporates an Artificial Intelligence Trust, Risk, and Security Management (AI TRISM) model to provide robust security. It focuses on long-term investments based on financial fundamentals like PER and NPM. The explainable AI (XAI) system uses DeepSeek for descriptions and SHAP/LIME charts for feature importance. The system achieved 56.62% average profitability across 120 portfolios, significantly outperforming NASDAQ-100 and S&P-500 indices. A user study showed high user trust and usefulness of explanations.
Executive Impact
The methodology achieved significant improvements in investment recommendations, backed by strong statistical validation and enhanced user trust through transparent AI explanations.
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Average Portfolio Profitability
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Portfolios Analyzed
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User Trust Score
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Statistical Significance
Deep Analysis & Enterprise Applications
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Generative AI in Financial Markets
This section reviews how Generative AI (GAI) is used in financial markets, from direct portfolio management to analytical tools. Studies like Pelster et al. and Kim explore ChatGPT's role in stock selection and asset management, often outperforming traditional methods. Other research leverages GAI for synthetic financial data, sentiment analysis, and improving advisory services. While existing works focus on GAI for portfolio building, our research integrates XAI to enhance transparency and understanding, a key differentiator.
Explainable AI for Investment Recommendations
Explainable AI (XAI) is critical for improving trust and interpretability in stock recommendations. Ferrara et al. combined ML and XAI (SHAP, LIME) to predict stock prices, identifying key variable impacts. Wang et al. used deep learning to predict returns, highlighting predictors like momentum and fundamentals. Our work extends this by integrating both XAI and GAI, providing transparent and comprehensive explanations alongside portfolio recommendations. We use fundamental financial data, unlike previous studies that focused primarily on market behavior, offering deeper insights into investment rationale.
AI Trust, Risk, and Security Management (AI TRISM)
Managing trust, risk, and security (TRiSM) is paramount for AI systems in finance. This section outlines our AI TRiSM model, which ensures robust security for the DeepSeek-powered recommendation system. It addresses vulnerabilities through adversarial perturbation tests, black and white box testing, data failure simulations, and sandbox environments to maintain prediction reliability and system stability. Security measures include MFA, encryption, adversarial training, and GDPR-compliant audits. Trust is built via interactive dashboards, detailed decision logs, and real-time data visualization, fostering transparency and accountability. Continuous monitoring with platforms like Datadog and Splunk, KRIs, and predictive analysis ensures proactive anomaly detection and ethical compliance, positioning DeepSeek as a dependable solution for long-term investment.
56.62%
Average Portfolio Profitability
Enterprise Process Flow
Data Collection
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Prediction of Portfolio
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Explanations with GAI
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Security Protocols (AI TRISM)
| Feature | Proposed System | Benchmark Indices |
|---|---|---|
| Average Profitability (4 years) | 56.62% | 47.46% |
| Statistical Significance (p-value) | < 0.001 | N/A |
| Transparency & Explainability (XAI) | High (SHAP, LIME, DeepSeek) | Low/None |
| Risk & Security Management | AI TRiSM Integrated | Standard |
Case Study: User Study: Trust in XAI Explanations
A user study with 7 participants (balanced gender, diverse financial background) evaluated the system's XAI-based portfolio recommender. The highest score (6.57/7) was for understanding company descriptions generated by DeepSeek. The usefulness of explanations for trusting recommendations scored 6.14/7, with most participants (57.1%) agreeing and 28.6% strongly agreeing. This highlights the critical role of transparency and detailed explanations in fostering user trust in AI-driven financial advice. The study confirmed that integrating DeepSeek for descriptions and SHAP/LIME for feature importance significantly enhances user confidence.
Key Findings:
- High understandability of DeepSeek-generated company descriptions (6.57/7)
- Explanations crucial for trust (6.14/7)
- 57.1% agreed, 28.6% strongly agreed on usefulness for trust
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Estimated Annual Savings
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Employee Hours Reclaimed Annually
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