AI in Fintech Dealmaking: Why Patience Pays Off

The past two years have been dominated by bold claims that AI would revolutionize virtually every business function. Yet despite widespread adoption, many organizations are still searching for tangible ROI. MIT researchers found that 95% of organizations have generated no return on their AI investments. And according to a NTT DATA survey, nine in ten senior decision-makers report “AI fatigue,” shifting budgets toward clearer short-term payoffs. 

This skepticism is nothing new for PE, especially for firms making deals in the fintech sector. Operating partners, digital transformation, and ESG took time to gain traction in PE, but are now standard practice across the industry. AI appears to be following the same path. The question isn’t whether AI is capable of creating value; it’s whether organizations will have the patience to wait long enough to realize its full impact. Those who stay the course are better positioned to capture its true payoff in sourcing, diligence, and execution. 

AI’s Role in PE Dealmaking  

The debate about AI’s value often overlooks a noteworthy point: companies use it for very different purposes. A lending platform may apply it to risk assessment, an insurtech to underwriting, and a digital bank to fraud detection. Not surprisingly, the results vary. Some applications produce immediate benefits, while others take years to materialize. 

PE firms are finding particularly strong applications for AI in the deal process, where marginal improvements can create major advantages over time. One of the clearest areas is deal sourcing. For firms focused on fintech deals, AI tools can scan massive datasets to uncover lesser-known companies that fly under the radar. They can also flag emerging businesses, such as payment processors, lending platforms, or wealth management technologies, that could become attractive long-term targets. 

Due diligence is another critical area. In fintech deals, navigating multi-jurisdictional licensing requirements, assessing AML/KYC compliance programs, and evaluating exposure to shifting regulations can overwhelm deal teams. AI tools can map regulatory requirements across markets, flag compliance gaps, and benchmark a target’s regulatory posture against industry standards in a fraction of the time. 

AI is also proving useful for execution and portfolio management, where real-time monitoring matters most. For financial services portfolio companies, this might mean tracking transaction volumes, flagging regulatory changes, or identifying emerging competitive threats. AI models can alert investors to shifts that may affect valuation or timing. This early intelligence helps firms anticipate issues and capture upside more effectively. 

The Case for Patience in AI Adoption 

It’s tempting to scale back AI spending when results aren’t showing up fast enough. But Fintech PE leaders we’ve spoken with emphasize that pulling back now risks losing ground. Firms that stay committed are already building meaningful advantages in sourcing, diligence, and portfolio management. 

Based on our conversations with PE operating partners, here are a few practical ways firms are accelerating progress and capturing early wins: 

Fine-tune the scope: AI initiatives often stall when teams try to do too much at once. In fintech, start narrow: focus on specific dealmaking challenges where you already have strong data. Those that target high-impact use cases will see results faster than firms trying to transform everything at once. 

Invest in deal data quality: AI is only as strong as the data behind it. Clean, structured datasets are essential if firms want reliable insights for origination, evaluation, and portfolio oversight. Make sure the data that matters for dealmaking – industry benchmarks, transaction comparables, customer and supplier concentration, contract metadata – is organized and regularly refreshed. 

Designate internal champions: AI’s success in dealmaking ultimately depends on having the right leadership in place. Assign one deal lead and one data lead. Give them authority to tune screening and diligence, and measure three things: stronger pipeline, faster Investment Committee decisions, fewer screening false positives. 

Track leading indicators: ROI won’t be fully visible early on. Monitor pipeline strength, diligence speed, and decision quality to demonstrate impact before financial returns appear.

AI is a strategic investment that takes time to reach its full impact. In private equity, impatience kills returns. With AI, patience creates them.

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