If central banks were already temples of monetary dogma, now they want to become AI laboratories as well. Monetary orthodoxy is merging with algorithms, and institutions like the Federal Reserve, the European Central Bank, the Bank of England, and even Banxico are giving artificial intelligence a seat at the table where the value of money is decided.
Today, the new oracle isn’t gold, nor the dollar — it’s machine learning.
According to a report by Reforma, more than 50 central banks are using AI tools for fraud detection, financial supervision, risk forecasting, text analysis, and economic modeling. The International Monetary Fund confirms it: AI is no longer a technological luxury — it is now integrated into the daily operations of global financial governance.
But what exactly are these automated systems doing?
Do we really want algorithms nudging interest rates, influencing inflation forecasts, or shaping credit conditions?
The image seems grotesque — but it’s real.
The Bank of England’s Monetary Policy Committee already uses large language models like GPT to draft speeches, analyze meeting transcripts, and synthesize economic data. How long until an official policy statement is signed not by a governor… but by a prompt?
AI doesn’t just predict. It observes, tracks, monitors, and compares.
The ECB uses it to automatically assess risk in bank portfolios.
Hong Kong’s monetary authority uses it to detect unusual financial activity.
Canada uses it to model inflation and inflation expectations.
The data explosion has created a world in which no human team can process everything in real time. But data is not wisdom.
A system trained on historical bias, incomplete data, or political pressure can amplify injustice, distort markets, or justify harmful policies. What happens when an algorithm decides that rate hikes won’t hurt “the average citizen,” but will conveniently “correct” the markets? Or when it concludes that an emerging economy should endure harsher austerity simply because the historical data says so?
The real issue isn’t whether AI can help.
It’s: Who programs it? With what data? And for whose interests?
Banxico Joins Late — As Always
Mexico’s central bank has begun experimenting with AI for analytical tasks. And, as usual, it has done so with “institutional caution” — the diplomatic phrase that means “we’re late, but don’t embarrass us.”
In collaboration with Columbia University, Banxico has tested AI systems for classifying economic news and analyzing sentiment in financial texts. It sounds promising, but raises a concern: will algorithms replace critical reading of Mexico’s economic reality? Will they rely on biased media, political rhetoric, or flawed economic commentary?
Who watches the watcher?
And who regulates the algorithm that reads what the watcher reads?
A Silent Revolution — With No Regulation
More than half of the world’s central banks use AI.
But only a few have regulatory frameworks to ensure ethical use, transparency, or oversight.
In other words: the guardians of global finance are playing with algorithmic fire — with no extinguisher in sight.
The Bank for International Settlements (BIS) has already warned that the lack of clear rules could create dangerous dependencies, amplified errors, or even systemic risks. AI could become a financial hazard comparable to toxic assets — only more opaque, faster, and harder to detect.
Artificial intelligence is here to stay.
But not all intelligence is wisdom.
And not all predictions are truth.
Central banks are diving into an ocean of data — but swimming in numbers does not guarantee they will reach the shore of sound judgment.
As one anonymous banker whispered at a Davos forum:
“The one who controls the algorithms controls perception; and the one who controls perception controls the economy.”
But today, even that seems outdated: all it takes is for a model to say “adjust,” and the entire system responds.
Are we entering a new financial technocracy?
Or worse: an enlightened despotism powered by artificial intelligence?
The Era of Monetary Policy Written by Algorithms
There was a time when central banks were seen as the last bastions of human judgment. Decisions were nuanced, intuitive, and rooted in decades of experience.
That era is gone.
Now, speeches are drafted by neural networks, data is processed by machine models, and financial forecasts are shaped by algorithms that do not sleep, doubt, or question themselves.
Banks in England, Canada, Japan, and Mexico no longer rely solely on Keynes or Friedman. They rely on prompts.
Over 50 central banks now institutionalize AI for:
- Text analysis of policy speeches
- Meeting transcript interpretation
- Fraud detection
- Economic forecasting
- Financial sentiment analysis
The twist?
AI is no longer a support tool.
It is becoming another voice—silent but influential—at the policy table.
The Unregulated Future
The seductive argument is that AI processes millions of data points no human could ever analyze. But that doesn’t make it correct, just, or safe.
A biased system can produce biased monetary policy.
A poorly trained model can misread inflation.
A manipulated dataset can shift economic forecasts.
The BIS warns:
“Indiscriminate use of AI can amplify errors, generate systemic risk, and blur lines of accountability.”
Who decides whether a forecast was wrong?
Who takes responsibility?
Who audits the data?
No one.
Because technological dogma has replaced self-criticism.
And Mexico, as usual, is not leading — it is following.
In short, we are delegating power without delegating accountability.
We are giving control to systems that cannot be held morally or politically responsible.
The global financial system has opened the vault — and AI has walked in.
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