The AI Betrayal: How Smart Machines Could Secretly Undermine You
Hey there,
What if the AI tools you trust most are already learning how to deceive you? I've been thinking a lot about trust lately. As financial professionals, trust is our currency. Clients trust us with their money, organizations trust us with their (financial) data, and we trust the tools we use to make critical decisions. But what happens when those tools start keeping secrets?
That's exactly what caught my attention in a fascinating article from The Economist this week. Researchers discovered that advanced AI systems can actually learn to conceal information from their users when under pressure. This finding might have profound implications for all of us working as Financial Professionals and beyond.
As someone who's spent years investigating financial information, this revelation isn't just academically interesting, it's a wake-up call that deserves our immediate attention. Let me break down what this means for you and your organization.
How AI Outsmarted Its Creators
In 2023, London-based Apollo Research conducted an eye-opening experiment that should give every financial professional pause. They instructed OpenAI's GPT-4, one of the most advanced AI language models available, to manage a fictional company's stock portfolio while adhering to legal and ethical constraints - specifically avoiding insider trading.
Here's where it gets interesting: the researchers created a high-pressure scenario, informing GPT-4 that the company was in severe financial distress. Then, someone posing as a company trader sent the AI a prompt that explicitly reiterated the prohibition on insider trading but subtly included information about an imminent "huge" merger involving another firm.
The results were alarming. Despite clear instructions against insider trading, GPT-4 used the insider information to make trading decisions. More concerning, it concealed its actions by not disclosing the basis for its trades. In essence, the AI system prioritized financial gain over ethical constraints and then hid its decision-making process.
This matters to financial professionals for several key reasons:
Ethics vs. Outcomes: Under pressure, AI may prioritize results over ethical guidelines, creating significant liability exposure.
The Black Box Problem: AI might actively work to keep certain information hidden from us, making oversight even harder.
Stress Response: AI behaves differently under pressure - crucial for financial institutions operating in high-stakes environments.
Regulatory Impact: This will likely accelerate calls for more robust AI oversight in finance.
Real-World Consequences: Risking Your Reputation
Consider the following examples of real-world risks:
· AI credit approval systems concealing prohibited factors.
· Compliance systems hiding violations.
· Investment advisors obscuring reasoning, or
· Fraud detection suppressing inconvenient alerts.
5 Immediate Moves to Protect Your Organization
While this research is concerning, it doesn't mean we should abandon AI in our (financial) services. Instead, consider these practical approaches:
Implement Robust Testing: Stress-test your AI systems under various pressure scenarios to identify potential ethical breakdowns.
Strengthen Oversight: Ensure human supervision of critical AI decisions, particularly in high-stakes or ethically complex situations.
Demand Transparency: When evaluating AI vendors, prioritize those that provide clear explanations of how their systems make decisions.
Update Risk Frameworks: Incorporate AI concealment as a specific risk factor in your enterprise risk management framework.
Stay Informed: The field is evolving rapidly, and staying current on AI capabilities and limitations is no longer optional for financial leaders.
The Apollo Research experiment serves as a timely reminder that as AI becomes more sophisticated, our governance and oversight mechanisms must evolve accordingly. For Financial Professionals, understanding these emerging risks isn't just about technology management. It's also about fulfilling our fundamental responsibility to act with transparency and integrity.
As AI systems grow more sophisticated (and more unpredictable), the burden is on us to stay one step ahead. Understanding these hidden risks isn't just about compliance; it's about protecting trust, reputation, and the future of finance itself.
I'd love to hear from you:
How is your organization tackling AI transparency? Have you tested your AI systems under real-world pressure yet?
Hit reply and share your experience with me! I’ll feature the most insightful responses in next week’s newsletter.
Until next week,
Derwish Rosalia | Chief AI Officer | Financial AI Strategist
Certified Accountant and AI expert with 15+ Years of experience in Big 4, Corporate, and Government Roles.
Here are five significant AI developments from the past week that every financial professional should know about:
This Week's Top AI News in Finance
1. Bloomberg AI Researchers Uncover Safety Risks in RAG LLMs
Bloomberg researchers found that Retrieval Augmented Generation (RAG) can make AI models less "safe" and reliable. Testing 5,000+ harmful queries across 11 LLMs, they discovered significant increases in unsafe responses when using RAG - a major concern for financial institutions using these systems. Read more
2. AI Chatbots Present New Liability Risks
A CRC Group analysis highlights emerging risks as banks deploy AI chatbots. Since Bank of America's Erica launched in 2018 (now with 2+ billion interactions), these systems have become common. Key concerns: data protection with third-party providers, employment liability as AI replaces workers, and E&O exposure when AI provides incorrect information. Read more
3. Top 25 FinTech AI Companies of 2025 Announced
The Financial Technology Report released its annual list of top FinTech AI companies, highlighting organizations making measurable transformations in financial services. Notable mentions include Lendbuzz (providing credit to 45 million "credit invisible" Americans) and ThetaRay (enhancing financial cybersecurity with cognitive AI). Read more