Cybercriminals haven’t created the world’s biggest criminal growth industry by resting on their laurels – it’s estimated that global cybercrime cost will reach $10.5tr annually by 2025, up from $3tr in 2015. These persistent individuals understand that their nefarious online activities must evolve to achieve their goal: to deceive intended targets for financial gain while evading the clutches of the authorities. 

Their determination to develop innovative attacks to fit new trends has brought one of the fastest-growing areas of fintech into their line of sight: embedded finance. This seamless integration of banking-like services into a non-financial customer journey ticks all the boxes for cybercriminals: it’s a new financial service that lacks regulatory control; it’s growing at an exponential rate; and its inherent convenience makes it easy to move money around. 

Embedded finance platforms that treat cybersecurity as an afterthought leave gaps in their defences for cybercriminals to exploit – exposing businesses to reputational damage and financial loss. Rather than giving them a head start, fintech providers must embed robust fraud detection and prevention capabilities into the customer journey – not just convenience. 

The role of AI in detecting and preventing financial fraud

The cyber-attack surface is vast – and continuing to grow and evolve rapidly. Traditional fraud detection techniques barely dent the mindboggling amount of data that needs analysing to prevent cyber-attacks – allowing them to go undetected. These manual and time-intensive processes heighten vulnerability and delay remediation activities – making it almost impossible to manage cybercrime proactively. 

How do financial service providers overcome the mountains of data that stand between them and the perpetrators? Two words: artificial intelligence (AI). AI-powered fraud detection and prevention tools conduct real-time analysis of broad and diverse customer data sets and present their findings expeditiously. 

This ability to monitor vast swaths of transactional data allows providers to identify nuanced trends that can be used to detect fraud in real-time. When fraud is detected, AI models can automatically reject transactions or flag and rate them for further investigation – and the benefits are compelling: 

• Speed: AI continuously processes and analyses new data and can assess countless transactions in real-time.  
• Scale: The efficiency of fraud-based AI algorithms improves as data sets grow. Data helps AI to distinguish between different transactional behaviours and determine which are legitimate and which are fraudulent. 
• Efficiency: Unlike people, AI algorithms love repetitive tasks. They are built to handle the data analysis heavy lifting, and only involve people in decision-making when they have spotted subtle or counterintuitive patterns in transactions.  

The use of AI in real-world fraud detection and prevention cases

Danske Bank

Almost all Danske Bank’s customer interactions take place digitally, providing a large attack surface for cybercriminals to target. The bank had a low 40% fraud detection rate and was managing up to 1,200 false positives per day. Consequently, 99.5% of all cases it was investigating were not fraud related – false alarms that required a substantial investment of people, time, and money to investigate.  

The bank made the strategic decision to apply AI-powered analytics to improve its fraud detection process while reducing false positives – and it paid off: they experienced a 60% reduction in false positives, with an expectation to reach as high as 80%, and a 50% increase in true positives. 

Mastercard

Mastercard’s AI-powered Decision Intelligence technology uses patterns obtained from cardholders’ historical shopping and spending habits to establish a behavioural baseline against which it compares new transactions. Unlike traditional prevention technologies, which rely on a one-size-fits-all approach to transaction evaluation, the Decision Intelligence tool circumvents the common causes of false declines by analysing every transaction in context. 

The challenges of using AI for fraud detection and prevention

The integration of AI-powered fraud detection and prevention functionality into embedded finance platforms is not without its challenges – including:   

• Lack of data: AI algorithms work by learning from data. SMEs may not have the IT infrastructure needed to harvest enough data to create a baseline understanding of what fraud looks like. 
• Ethics: The developers who build AI decisioning models can’t always explain how it arrives at the outcomes or which factors had the biggest influence. Known as a “black box”, this opaque symptom of AI creates ethical challenges, such as the fairness and accountability of outputs. 
• Data privacy and security: AI’s reliance on vast datasets exposes it to privacy concerns around the handling of personal information and potential data breaches. 
• Regulatory compliance: The General Data Protection Regulation (GDPR) requires everyone responsible for using personal data to follow strict rules called ‘data protection principles’. They must ensure the information is: used fairly, lawfully, and transparently – or face penalties. 

Best practices for implementing AI for fraud detection and prevention in embedded finance

Successfully applying AI to fraud detection and prevention models involves training algorithms to identify trends and characteristics indicative of high-risk transactions within a dataset. There’s more to this than simply pouring data into the funnel; the foundations must be laid to build an informed solution that can automate risk management: 

• Input data: By feeding segregated data into the AI solution in different forms – such as good data and fraudulent data – it’s empowered to learn and perform better.     
• Extract features: Things like past transactions, identity details, locations, preferred choices during payment, and network details play a crucial role in determining the signals for identifying fraud. 
• Algorithm training: To ensure the solution can differentiate between legitimate and fraudulent transactions – and predict fraudulent activities accurately – algorithms are trained using learning datasets. 
• Model development: Once the training phase is complete the solution can be improved by adding new data or features. 

In the context of embedded finance, AI-powered fraud detection and prevention must be integrated into the entire customer journey – or fraudulent activity may go undetected. Cybercriminals test for vulnerabilities in systems to pinpoint any weak spots they can exploit. Take embedded lending for example: from the moment the customer is onboarded to the moment they receive their funds, they are exposed to cybercrime. When AI is leveraged to protect them at every stage of this journey, they benefit from a truly frictionless embedded experience.  

The future of AI in detecting and preventing financial fraud in embedded finance

According to Juniper Research’s AI in Financial Fraud Detection report, global business spend on AI-enabled financial fraud detection and prevention platforms will exceed $10bn globally in 2027 –  rising from just over $6.5bn in 2022. The report also forecasts that cost savings will reach $10.4bn globally in 2027, from $2.7bn in 2022. 

This rapid growth will be perpetuated by a new trend in AI-enabled fraud management: a focus on accessing fraud information from beyond a business’s transactions. To achieve this, fintech providers are forging partnerships with third parties, such as credit bureaus and payment networks, to improve data coverage – and enhance algorithm learning. 

AI: wave goodbye to manual fraud detection and prevention

We hear a lot of talk about “keeping up with cyber criminals”. To put these words into action, fintech providers of embedded finance platforms are leveraging AI-enabled fraud detection and prevention solutions to do what manual processes can’t: conduct real-time analysis of vast datasets, identify trends indicating suspicious behaviour without delay, and provide automated alerts for timely remediation.  

This proactive approach to fraud management augments embedded finance by providing a vital layer of protection from cybercrime throughout the entire customer journey – one that is scalable, expeditious, accurate, and always improving. 

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