Bridging the Gap: Safe AI Implementation in Financial Services

By Mark Goodey | October 29, 2024

In the rush to adopt artificial intelligence, particularly Large Language Models (LLMs), financial institutions must understand both the capabilities and limitations of these technologies. One crucial limitation often overlooked is that LLMs cannot perform reliable mathematical calculations despite their impressive natural language capabilities.

The Mathematical Limitations of LLMs

LLMs are trained to predict the next most likely word or token in a sequence, making them excellent at understanding and generating human-like text. However, they don’t perform actual mathematical computations. When asked to calculate complex financial metrics or perform mathematical operations, LLMs essentially make educated guesses based on their training data, which can lead to significant errors.

Consider this simple example:

If you ask an LLM to calculate the compound annual growth rate (CAGR) of an investment that grew from $10,000 to $15,000 over three years, it might provide a plausible but not mathematically precise answer. However, the same LLM can excellently explain what CAGR means and why it’s crucial for investment analysis.

Combining Technologies for Accuracy and Insight

The solution lies in combining different technologies:

Deterministic Models: For precise mathematical calculations
Traditional Solutions: For data processing and validation
LLMs: For generating natural language explanations and insights
Sentiment & Market Analysis Tools: Deeper insights into market trends and customer perceptions

This hybrid approach ensures that each component handles what it does best. Deterministic models perform the calculations with mathematical precision, while LLMs translate complex financial data into clear, actionable narratives.

The Importance of Controlled Experimentation

Financial services can safely experiment with AI through proper governance and control mechanisms. Key components include:

Validation Frameworks: Ensuring AI outputs align with established financial models
Audit Trails: Tracking all AI-driven decisions and recommendations
Quality Controls: Implementing multiple checks for mathematical accuracy
Risk Management: Maintaining oversight of AI applications in different contexts

Building Safe AI Systems for Financial Services

Creating safe and effective AI systems requires a deep understanding of both financial services and technology. Essential elements include:

Technical Safeguards:

Input validation systems
Output verification protocols
Regular model performance monitoring
Data quality checks

Operational Controls:

Clear approval processes
Defined use case parameters
Regular system audits
Documentation requirements

Real-World Applications

When properly implemented, this controlled approach to AI can enhance various financial operations:

1. Investment Analysis

Accurate Calculation of financial metrics
Natural language summaries of market trends
Clear explanation of investment strategies

2. Risk Assessment

Precise risk calculations
Detailed narrative risk reports
Comprehensive market context

3. Compliance Reporting

Accurate regulatory calculations
Clear compliance narratives
Audit-ready documentation

4. Sentiment & Market Analysis

Clear understanding of market trends
Deep insights into customer perceptions

Best Practices for AI Implementation

Successful AI implementation in financial services requires:

Clear understanding of Goals

Begin with well-defined, limited-scope projects
Validate results against existing systems
Gradually expand based on proven success

Maintaining Control

Establish clear governance frameworks
Implement robust testing procedures
Ensure human oversight at critical points

Ensuring Transparency

Document all AI-driven processes
Maintain clear audit trails
Provide explainable outputs

Looking Forward

The future of AI in financial services isn’t about replacing traditional systems but enhancing them. By understanding each technology’s limitations and implementing proper controls, financial services can leverage AI’s capabilities while maintaining the accuracy and reliability their stakeholders expect.

Success in this area comes from combining deep financial sector experience with technological expertise. This enables organisations to develop AI solutions that are not just effective but also safe and controlled. The key is maintaining a balanced approach: embracing innovation while ensuring proper governance and risk management. To learn more about how AI infin8 can help you experiment with AI under your governance and control, check out our Innovation Lab, or contact us here.