The emperor’s new algorithm in fintech: Why your AI is not finding alpha, it’s just faster at being wrong
True alpha in the AI era will not come from having better algorithms. It will come from having better questions, stronger conviction in contrarian positions, and the operational capability to transform businesses that machines would overlook.
For decades, private equity has prided itself on generating alpha through information asymmetry, operational expertise, and the conviction to see value where others do not.
Now, every pitch deck landing on limited partners' desks promises AI-driven deal sourcing, predictive analytics for portfolio performance, and machine learning models that eliminate bias from investment decisions.
The numbers tell a seductive story.
According to a recent report from Dealroom.co, global VC investment in AI companies exceeded $100 billion in 2024, with a third of all venture funding directed toward AI-related opportunities. In fintech specifically, AI investment reached $17 billion in 2024 and is projected to surge to $70.1 billion by 2033.
Almost every major PE firm now touts its proprietary AI capabilities. But here is the uncomfortable truth, in my opinion: most firms are not using AI to find alpha. They are using it to industrialise beta.
The real alpha in private equity has never come from processing more data faster. It has come from asking different questions, seeing around corners, and having the operational muscle to transform businesses others would walk away from.
AI excels at pattern recognition in existing datasets. True alpha, by definition, exists where patterns have not yet formed or where conventional wisdom is demonstrably wrong.
So, what does it mean to use AI to find "true alpha" in private equity?
It starts with acknowledging three uncomfortable realities about how the industry is currently deploying these tools.
Reality one: You are optimising for the last cycle, not the next one
Most AI implementations in PE are fundamentally backward-looking.
Train a model on successful exits from 2015-2023, and it will reliably identify companies that would have been great investments in 2015-2023. It will spot the SaaS metrics that mattered during zero interest rate policy, the margin profiles that worked when capital was cheap, and the growth trajectories that impressed when multiples were expanding.
The problem?
We are not investing in 2015-2023 anymore.
True alpha emerges from identifying inflection points before they are visible in historical data.
But the firms that continued to generate alpha were not the ones that got better at identifying these patterns, they were the ones that recognised when the pattern was becoming crowded and moved capital elsewhere.
Reality two: Your model thinks correlation is causation (and so do you)
AI is exceptionally good at finding correlations in complex datasets. Feed it enough deal data and it will confidently tell you that companies with certain characteristics are more likely to generate strong returns. Some of those correlations may even be statistically significant.
None of them are causal.
The dangerous seduction of AI in private equity is that it makes sophisticated pattern-matching feel like insight.
But correlation is not strategy.
True alpha requires causal understanding, knowing not just what worked, but why it worked, and whether those causal mechanisms will persist.
Reality three: The crowd is using the same tools
Here is the most uncomfortable reality: if you are using commercially available AI tools to source deals, analyse opportunities, or benchmark performance, so is everyone else.
When every firm in the market is running similar models on similar datasets, you do not get alpha, you get convergence.
This is already happening in fintech at an alarming pace.
When everyone is using similar AI tools to identify similar opportunities in similar ways, the result is not differentiation, it is a race to the middle, with everyone arriving at consensus faster and more confidently than ever before.
What was once a competitive advantage becomes table stakes, and the only thing that separates you from the pack is execution, precisely the thing AI cannot do for you.
Where is the real opportunity?
If AI is not finding alpha through better pattern recognition, faster processing, or more comprehensive analysis, where should PE firms be deploying these tools?
The answer lies in using AI not to replace judgment, but to create the conditions where human judgment can focus on what matters:
Firstly, use AI to eliminate false negatives, not to pick winners. The highest value application of AI in deal sourcing is not identifying the "best" opportunities, it is ensuring you never miss the truly exceptional ones.
Configure your models to cast a wide net, to flag outliers and anomalies, to surface the companies that do not fit existing patterns. Then apply human judgment to determine which anomalies represent genuine opportunities versus noise.
The fintech sector is littered with companies that every conventional model would have screened out.
Secondly, deploy AI to stress-test your conviction, not to confirm it. The most dangerous bias in private equity is confirmation bias, the tendency to seek information that supports an investment thesis while ignoring contradictory signals.
Use AI to actively argue against your position.
Build models that identify every reason a deal might fail, every assumption that might be wrong, every comparable that went badly.
When buy now, pay later (BNPL) was the hottest sector in fintech, how many firms used AI to identify the regulatory risks, the margin compression from competition, and the dependency on low interest rates?
If your conviction survives aggressive machine-learning-powered scrutiny, it is probably worth backing. If it does not, you have saved yourself from a costly mistake, which is alpha preservation, if not alpha generation.
Lastly, focus AI on operational value creation, not deal selection. The uncomfortable truth is that most alpha in private equity comes from what you do after the deal closes, not from picking the right companies to begin with.
Use AI to identify operational inefficiencies across your portfolio, to benchmark performance against genuinely comparable peers, to predict which investments will need additional capital or management support before problems become crises.
An uncomfortable conclusion
True alpha in the AI era will not come from having better algorithms. It will come from having better questions, stronger conviction in contrarian positions, and the operational capability to transform businesses that machines would overlook.
The firms that generate exceptional returns over the next decade will not be the ones with the most sophisticated AI infrastructure.
They will be the ones that understand AI's limitations as clearly as its capabilities, which use these tools to enhance human judgment rather than replace it, and that recognise the uncomfortable truth that machines cannot see around corners; only humans can.
If your AI strategy makes you feel more confident about following the crowd, you are not finding alpha. You are just getting to the middle faster.
And in private equity, the middle is where returns go to die.
The views expressed in this article are those of the author and do not necessarily reflect the views of any affiliated organisations.
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