“AI in Finance: Speed Without Scrutiny?”“Joseph Plazo on Why Algorithms Still Need Human Judgment”
“AI in Finance: Speed Without Scrutiny?”“Joseph Plazo on Why Algorithms Still Need Human Judgment”
Blog Article
At a recent event hosted by the Asian Institute of Management in Manila, Joseph Plazo, voiced concerns that many in his field tend to ignore.
His argument was not anti-technology, but pro-governance.
“If you allow a machine to manage your portfolio,” he said, “ensure it reflects your priorities—not just your profit targets.”
???? **The Model Is Perfect. The Context Is Missing.**
Mr. Plazo is not a critic from the fringe. His firm’s systems have achieved a 99% success rate across various assets and timeframes.
But that success, he suggests, carries risk.
“A well-trained model can still make perfectly logical errors.”
He cited a case during the COVID-19 pandemic when a bot under his supervision flagged a short on gold—just before the US Federal Reserve announced an intervention.
“We cancelled the trade. It failed to anticipate a shift that any seasoned investor would have questioned.”
???? **Why Delay Still Matters**
Plazo referred to what he terms **“strategic friction”**—the time it takes to think before a trade.
“Speed without governance is simply exposure.”
He presented a framework his firm uses, called **Conviction Calculus**. It includes three questions:
- Are we trading in line with our long-term thesis—or merely responding to signals?
- Has the recommendation been challenged by human insight—policy awareness, historical precedent, market tone?
- Is the leadership team prepared to justify the trade beyond performance metrics?
???? **The Ethical Gap in Asia’s Fintech Race**
Plazo’s comments come at a time of accelerating fintech growth across Asia. From Singapore to Seoul, AI-led investing is seen as both policy strategy and capital advantage.
But as Mr. Plazo points out:
“Governance is lagging behind growth.”
In 2024, two hedge funds in Hong Kong lost billions after AI models failed to factor in geopolitical risk—a result of logic executed too quickly, and too narrowly.
“The models did what they were told. But no one asked whether they should.”
???? **The Case for Narrative-Aware AI**
Plazo remains bullish on AI’s potential—but not its current limitations.
His firm is building what he describes as **“narrative-integrated AI”**—systems that account for macro context, cultural tone, and regulatory environment, not just price and volume.
“We need models that don’t just predict—but interpret.”
Investors from Tokyo and Jakarta reportedly expressed interest in these models after the speech. One regional fund manager noted:
“We need systems that understand politics, not just price movement.”
???? **Silent Errors in a World That Doesn’t Pause**
Plazo ended with a line that encapsulated his thesis:
“Flawless code may be the most dangerous tool in the wrong context.”
It website was less a warning than a call to apply the same rigour to ethics as we do to execution.