Promoting Artificial Intelligence in the Financial Industry

AI Large Models: Applications in the Financial Industry

AI adoption in finance has moved beyond conceptual exploration to large-scale implementation, following a dual-track pattern:

• Leading institutions set benchmarks through early adoption and innovation.
• Small and medium institutions actively seek breakthroughs through focused applications.

Large-model use is shifting from isolated pilots to systematic deployments, delivering measurable efficiency gains and business impact.

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Three Core Principles for AI Deployment in Finance

Financial institutions are generally guided by three strategic priorities designed to enhance quality and efficiency:

• Risk Control First
• Focus on scenarios with controllable hallucination risks and clear information boundaries.
• Emphasize early risk identification and preventive measures.
• Internal Efficiency First
• Begin with technology R&D, operations, and other back-office processes.
• Allows for faster validation of technical results before customer-facing rollouts.
• Decision Support First
• AI empowers employees rather than replacing positions.
• Improves analysis and judgment efficiency through tool-based assistance.

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Banking Sector Leadership

Banks lead in both depth and breadth of AI adoption, supported by increased capital expenditure and R&D investment.

• Short-term wins:
• Code assistants & intelligent Q&A quickly release efficiency dividends.
• Some institutions now generate over 30% of code via AI systems.
• Long-term strategy:
• Move AI into revenue-critical areas including intelligent investment advisory and frontline marketing enablement.

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Technology Democratization & SME Opportunities

Open-source models such as DeepSeek and Tencent Hunyuan have lowered both technical and financial barriers to AI adoption.

Advantages for small and mid-sized institutions:

• Concentrate on specific business scenarios and private-domain data mining.
• Develop vertical specializations (e.g., supply chain finance, niche wealth management).
• Leverage short decision chains for agility.
• Partner with large-model providers to offset technical gaps and boost deployment speed.

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Impact of Ongoing AI Technology Evolution

From Large Models to AI Agents

Rapid improvements in model architecture, training paradigms, and task boundaries are accelerating industry adoption.

AI agents — systems with perception, planning, decision-making, and execution —

• Autonomously perform tasks.
• Integrate APIs & access knowledge bases.
• Close the loop from understanding to action.
• Overcome the traditional “only advise but do not act” limitation of large models.

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Use Cases

Investment Research

• Agents restructure research logic to reduce cognitive overload, information silos, and knowledge gaps.
• Multi-agent teams:
• Research planning
• Data collection
• Strategy analysis
• Use causal reasoning frameworks and hypothesis iteration to discover non-consensus insights.
• Implement self-correction mechanisms to reduce hallucinations.

Risk Management

• Shift from passive to proactive risk detection.
• Multi-agent networks integrate:
• Credit assessment
• Market monitoring
• Liquidity management
• Real-time, millisecond-level detection with decisions executed immediately.
• Strengthens financial system resilience.

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Building a Safe Agent Ecosystem

• Standards for communication protocols and model context protocols now emerging.
• Enable secure sharing of professional capabilities from data institutions and research bodies to financial agents.
• Expected to accelerate the penetration and maturity of agent applications.

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Cross-Platform AI Content Monetization in Finance

Many institutions — especially SMEs — explore cross-platform AI content monetization to:

• Extend customer reach
• Diversify revenue streams

Open-source platforms such as AiToEarn官网 allow:

• AI-powered content generation
• Publishing across Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X (Twitter)
• Analytics & performance rankings (AiToEarn模型排名)

Result: Supports both financial sector innovation and media sector monetization through standardized AI capabilities.

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Multi‑Path Deepening of AI in Finance

While opportunities abound, AI applications face key challenges:

• Algorithmic “black box” risks due to opacity of model mechanisms.
• Latency between tech updates and regulatory adaptation.
• Model homogenization inducing herd behavior & market resonance risks.
• High development costs and unclear ROI evaluation.

Solution: Develop a systematic methodology and phase AI implementation around strategic stages.

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Value‑Driven Adoption & Business‑Tech Collaboration

To advance AI adoption:

• Clarify strategic intent, investment direction, organizational ownership, benefit conversion.
• Establish a multi‑dimensional value evaluation system assessing:
• Operational efficiency
• Business revenue
• Risk control
• Customer experience

Break the linear “business proposes, tech delivers” model:

• Form cross‑departmental units combining business, product, and tech teams.
• Shared responsibility for value goals.
• Two‑way alignment:
• Front‑end identifies pain-point scenarios.
• Back‑end matches appropriate technical capabilities.
• Enable quantifiable, verifiable agile loops.

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Layered Collaborative Model Architectures

Success in finance rarely comes from one-size-fits-all models.

Recommended structure:

• General large models — fundamental cognition & generalization
• Domain-specific lightweight models — fine-tuned using business data for risk, investment, and wealth domains
• Traditional ML models — embedded in critical decisions for transparency

Deployment strategy:

• Private inference for sensitive data security
• Public cloud training to reduce costs — balancing performance, risk, and budget

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Hallucination Governance for Trustworthy AI

Hallucinations hinder large-model use in high-risk financial domains.

Focus on:

• Model training safeguards:
• Granular metrics & evaluation
• Confidence calibration algorithms
• Penalty mechanisms for overconfident errors
• Encourage “I don’t know” over guesswork
• Knowledge base governance:
• Approval processes
• AI content quality checks
• Validity period management
• Traceability to source data

Always embed human veto rights at critical decision points to mitigate systemic risk.

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AI Content Ecosystem Integration

Platforms like AiToEarn官网 show how:

• Open-source content monetization tools
• Model ranking & analytics
• Cross-platform publishing capabilities

...can be applied not only to media but also to financial AI workflows — spanning creation, deployment, governance, and ROI tracking — to ensure operational efficiency and maximize monetization potential across industries.

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Would you like me to create a visual diagram showing the layered AI model architecture and its role in banking, SME finance, and cross-platform monetization for easier stakeholder presentations?