5 ways Artificial Intelligence is transforming fintech’s fight against fraud

Fraud is a growing problem in the fintech ecosystem, with many firms suggesting that activity is on the rise. Is artificial intelligence poised to become a leading technology in combating nefarious activity in financial services? 

According to recent data, 7 in 10 fintechs claim that fraud volumes are higher than they were one year ago, and nearly 2 in 5 firms lost at least £1 million to fraud during the 12 months running up to October 2024. 

These concerning statistics call for more fintech firms to prioritise security to support the safety of their customers. 

While this has proven to be a challenging task, artificial intelligence (AI) is rapidly emerging as a key resource in the fight against fraud, paving the way for real-time analysis, advanced pattern recognition, and adaptive learning to match up to the sophistication of modern criminal tactics. 

In a landscape where traditional rule-based systems are increasingly struggling to protect fintechs from fraudulent activity, let’s explore five ways that AI is transforming the ability of firms in their battle for enhanced security: 

1. Advanced pattern recognition

Machine learning (ML) is a subset of AI and is an excellent tool for identifying complex patterns and anomalies that may be invisible to the human eye. This means that any evidence of unusual purchase amounts, locations, or transaction frequencies that stand out against a customer’s typical behaviour can be immediately flagged. 

The depth of pattern recognition can extend to geospatial analytics, which determines the most frequent locations that a person shops in and the cities where they often make purchases. These metrics can be combined with typical purchase amounts to monitor for deviations that could point to money laundering. 

This means that if a criminal uses PayPal for a transaction in an expected storefront, but it’s a far higher cost than usual, it can be flagged by artificial intelligence. 

AI analytical tools can also determine whether cards can automatically be frozen to stop fintech transactions if activity exists in multiple different locations. Although there may be natural reasons for this, such as a holiday overseas, customers can be directed to authentication measures as an added layer of security. 

2. Adaptive learning

Machine learning can also use existing data from fraud cases to adapt dynamically to new fraud tactics. This allows fintechs to form a defence alongside evolving criminal methods. 

Already, there are many emerging use cases of adaptive learning in action throughout the fintech landscape, and Stripe Radar uses insights from billions of global data points to cut card-testing attacks by as much as 80% for users. 

These adaptive AI methods can also help to scrutinise better Authorised Push Payment (APP) fraud, which affects hundreds of thousands of users in the UK each year. 

3. Biometrics checks

Artificial intelligence can be used to monitor user behaviour like keystroke dynamics, mouse movements, and login patterns to create a holistic overview of a user’s identity. 

The use of biometric verification, such as facial recognition and liveness checks during the customer onboarding process, represents a key step in the prevention of identity theft and synthetic identity fraud. 

AI developments are moving fast to prevent different forms of fraud, and Mastercard’s deployment of a RAG-enabled voice scam detection system in 2024 helped the payments giant boost its fraud detection rates by 300%. 

4. Automation tools

The technology can also automate key routine tasks like data collection, document verification, and alert generation, helping to assist human analysts in a way that frees them up to focus on higher-value, more complex tasks. 

In addition, AI will support compliance by using automation to continually run regulatory checks and reporting. Because of this, firms can rely on AI to improve their efficiency in the fight against fraudulent activity. 

5. Dynamic risk scoring

Transactions are happening all the time, so it’s essential that AI has the capabilities to sift through anomalies to flag for review. 

Banks like JPMorgan and many other fintech firms, such as Stripe, are already using AI systems to analyse thousands of transactions every minute to assign dynamic risk scores. 

These systems will continually refine the accuracy of their scores and learn from the results of flagged risks to pave the way for earlier fraud detection and suspicious behaviour. 

Using AI to fight fraud

Fraudulent activity poses an existential threat to many fintech firms today, and can result in millions of pounds worth of damage if financial technology players fail to have an effective system in place. 

Artificial intelligence is emerging as a key player in the fight against fraud by deploying predictive analytics and real-time monitoring when it comes to looking for signs of nefarious activity. At a time when the threat of fraud is growing, AI can form an efficient safety net for fintech companies.

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