AI adoption in financial services and fintech in 2025

A few weeks ago, I visited several events in London over UK Fintech Week. I listened to a lot of speakers and panels and spoke to a lot of people in the sector—at established financial services companies, scaleups, those still early in their product journeys and other ecosystem participants.  

By far the most common use cases of predictive AI in financial services are KYC (Know Your Customer) and AML (anti-money laundering) compliance.  

KYC and AML flows contain a lot of rules-based repeat processes that require flawless accuracy in their execution. Rare mistakes and oversights led to fines totaling almost $24 billion in 2024. American companies alone were slapped with over $3 billion in fines. 

Automation of these processes reduces errors and oversights drastically and requires a fraction of the resources as legacy KYC and AML processes. 

In April of 2024, Visa announced that its AI-powered fraud detection system helped prevent $41 billion in fraudulent transactions in a single year by analyzing customer behaviour patterns, geolocation and transaction velocity.  
 
Another compelling example of an automated AML feature is SEON’s transaction monitoring model and screening system used to reduce their clients’ manual fraud reviews by 37%, or by over 1/3. 

Similarly, Revolut has released an AI-powered feature to protect customers against APP (Authorised Push Payment) scams that often act as a precursor to money laundering. The feature uses a ML (machine learning) model to flag potential scams in real time by comparing normal user transaction patterns to irregularities, identifying suspicious user behaviour automatically and simplifying a task that is otherwise quite tedious. A few other common use cases are 

• Extracting data from 10-ks. 
• Automating loan application processing and customer data cross-referencing. 
• Identifying customer relationships to decrease time spent on and overall costs of KYC reviews.

And financial services companies can apply predictive AI to other low-complexity, high-impact processes that are currently resource intensive. There are also opportunities to add extra value for customers with more adventurous features and a competitive edge. It’s not easy standing out in an often-crowded sector dominated by financial giants, especially as a newcomer, and to do so requires a culture of innovation.  

Predictive AI offers fintech innovators the insights to better forecast how new features will be received, adopted, or rejected by different segments of their audiences before spending money and time building them. It can also be applied to critique the ideation process and prototypes.  

Predictive AI is useful at most steps in the feature ideation to development process. Use it for instance  

• To validate ideas by projecting historical user behaviour patterns onto feature prototypes. 

Monzo, for example, uses ML models to identify patterns in user behaviour like login activity, onboarding flow interactions and when customers use certain features like making payments. 
 
When building a new feature, Monzo can now use this data to predict how users might interact with the proposed feature. 

The model could show whether a certain user profile would use or ignore the feature, if it may increase engagement with core or prioritised services, fail at inspiring an uptick in meaningful engagement or perhaps offer value to users in some unexpected way. 

• To assess compliance implications of new products or features before they are fully developed and launched. 

Upstart, a U.S.-based AI-powered consumer lending fintech, uses predictive AI tools to run pre-deployment simulations to gauge if their loan platform is compliant with the ECOA (Equal Credit Opportunity Act) by simulating variables like demographic groups to measure if any are disproportionately declined.  

To make the model’s findings transparent to regulators, Upstart uses an XAI (Explainable Artificial Intelligence) model to unpack its logic and show if decisions meet regulatory standards. 

Many financial services providers, including Upstart, use proxy models to simulate potential biases. However, the CFPB (Consumer Financial Protection Bureau) has not set clear legal rules for proxy models and that lack of clarity does mean using proxy models does entail a degree of compliance risk. 

Traditionally U.K. regulators have focused more on general decision-making transparency without any particular focus on demographics. As a result, financial institutions usually put an emphasis on making outcomes explainable to the FCA (Financial Conduct Authority). 

• To predict churn and to prioritize the development of certain features over others. 

ML models trained on enough user data can identify potential markers for disengagement before human analysts are able to connect the dots. For instance, normally when David receives his paycheck he checks his account that same day and splits his salary up between accounts.  

But over the last 4 months, he has waited two to three days to take any action. ML models could keep track of which accounts are receiving a delayed reaction to financial events, like David’s, and take steps to reengage him and others like him before churn happens, building a new feature to meet their needs or targeting them with a new campaign. 

Blockers for adopting predictive AI  

Predictive AI models estimate outcomes before companies allocate resources, helping to optimise R&D.  

But the quality of predicted outcomes is reliant on the fullness and quality of the data the AI models making them are trained on. If data is obsolete or incomplete, predictions will be less accurate. 

This is the Achilles' heel for many older and larger financial institutions: bringing together siloed data in multiple formats.  

Two solutions in this case are data integration techniques like data fabric architecture and AI-driven document understanding that can be used to bridge gaps between legacy systems and unify documentation with minimal manual intervention.  

Younger financial services providers built as digitally native have an advantage when it comes to data accessibility but also have less data than older, more established rivals. If larger, mature financial institutions can use their deep pockets of historical data, they can give themselves an edge when developing AI models and tools whose quality of performance is correlated to data volume. 

Some of the digitally native new providers are now getting large enough to be taken seriously by the big beasts of the financial services sector—Revolut as the most obvious example. 

The financial sector and predictive AI going forward 

Digitalization has allowed companies in the financial sector to ramp up operations exponentially, but most have still been somewhat constrained by “human factor” limitations like time zones and working hours. 

That is beginning to change. At the start of the year, Goldman Sachs introduced an internal AI assistant for its employees and BBVA released a customer-facing AI assistant. Revolut announced it will release its AI-powered assistant later this year.  

The autonomous nature of AI agents and assistants will let companies increase productivity and go beyond what a financially viable legacy human workforce makes it possible to achieve. Market research by McKinsey estimates that AI adoption will represent $1 trillion in value to the global banking sector annually through a combination of efficiency gains and new commercial opportunities. 

As financial services companies build and refine their AI adoption processes and regulation is defined, use cases for AI will continue to expand past KYC and AML compliance automations and become commonplace in areas like new feature idea validation, prototype testing and churn prediction.  

With success stories like Visa avoiding $41B worth of fraudulent transactions, and more companies prioritizing AI assistants and agents, AI adoption and integration are higher stakes. 

Wins like Visa’s come down to access to and quality of data, and enterprises fall into one category and young fintechs another - one with decades’ worth of data points split between various databases and systems, the other with sparser data but unified access.  

It will be interesting to see which turns out to be the more advantageous starting point for AI adoption: deep reservoirs of data and long-term experience, or digitally native systems and agile teams.