FinTechs are attractive to fraudsters. Why?
FinTechs rely on digital transactions and handle vast amounts of data. FinTechs face significant vulnerability, with an average fraud rate of 0.30%—twice the credit card fraud rate (0.15 – 0.20%) and triple the debit card fraud rate (0.10%). Fraud also causes 34% of Big Tech and FinTech companies to lose customers.
I discuss the paradigm shift arising due to the use of Artificial Intelligence in FinTech and how it powers fraud detection and risk management with Samuel Jaja, a software engineer with a keen interest in Artificial intelligence. He even holds a master’s degree in Artificial Intelligence and Data Science from the University of Hull.
Samuel Jaja agrees that FinTechs are attractive targets, especially in emerging markets that handle millions in transaction volume with security teams that are a fraction of the size typically seen in established banks.
They specifically target newer platforms where security protocols might evolve and detection systems haven’t yet learned all the malicious behavior patterns. Because of this, more than ever, FinTech founders are seeking foolproof and advanced software solutions to tackle fraud.
Artificial Intelligence has evolved over time from merely being a concept to a theory, then a tested theory (The Turing Test). Since then, implementations have transformed lives and disrupted technology over the past decades, from social media feeds to online shopping to self-driving cars and chatbots.
The FinTech industry is at a unique intersection. The industry has moved beyond simple rule-based systems, but it is not yet at full maturity. Most mid-to-large FinTechs use machine learning for transaction monitoring and anomaly detection, but the sophistication varies dramatically.
Large or leading dedicated companies often have dedicated data science teams that implement multilayered systems that combine supervised and unsupervised learning models. These teams analyze hundreds of data points per transaction in milliseconds and continuously retrain their models.
But the reality for many companies, especially in emerging markets, is starkly different. They typically use off-the-shelf solutions with limited customization, or they’re building basic models internally that aren’t fully optimized for their specific risk profiles.
From Samuel Jaja’s experience, he has observed that the biggest gap is in real-time decision engines. Many platforms flag suspicious transactions, but fewer can accurately make instant approval/decline decisions. This is where advances in machine learning come in to refine fraud detection and risk management efforts.
What Exactly is Fraud Detection and Risk Management?
Fraud detection and risk management are important processes for companies and financial institutions alike. Of course, FinTechs are not exempted, as most FinTechs float on digital platforms that render them more vulnerable, especially when no robust cybersecurity defenses exist.
Businesses must be foolproof against potential threats, whether general or unique to their operation framework. For example, if a FinTech platform collects sensitive data like phone numbers, identification numbers, and social security numbers to complete processes. In that case, extra measures need to be put in place to guard such data.
Fraud risk management covers all aspects of fraud prevention and detection. Scaling and operating a business entails ensuring that certain measures are set to identify and mitigate fraud risks and prevent and detect them. Fraud detection serves as a core component of Fraud risk management.
Fraud risk management involves identifying, analyzing, and mitigating a company’s potential for fraud. It encompasses systems and policies or guidelines to prevent, detect, and respond to fraudulent activities to protect and preserve an organization’s financial assets and reputation. Fraud detection, however, simply involves different measures or techniques that are harnessed to spot fraudulent activities.
As an AI software engineer specializing in artificial intelligence for fraud detection, Samuel Jaja agrees that both measures are complementary but distinct. Fraud detection is about identifying malicious activities that have already begun, such as someone using a stolen card, account takeover attempts, synthetic identity fraud, etc. It’s reactive by nature, focused on finding the needle in the haystack of legitimate transactions.
However, risk management is broader and more proactive. Fraud risk management is about understanding the overall vulnerability landscape and implementing systems to mitigate potential threats before they materialize.
Samuel Jaja explains practically that it includes everything from KYC/AML procedures and transaction limits to capital adequacy and operational resilience. Fraud risk management and fraud detection intersect constantly as they are complementary. Fraud detection systems feed data to risk models and help give insights about which customer segments, transaction types, or geographic regions present higher risk.
Then, risk management frameworks determine how to implement actions based on those insights.
One thing is clear: processes are crucial to protecting financial assets and public reputation. The Federal Trade Commission revealed that US consumers lost over $12.5 billion to fraud in 2024, a 25% rise from $10 billion in 2023. Most of these figures stemmed from FinTech and cryptocurrency scams.
The Synapse Crisis is a good example for FinTech founders to learn from. The banking-as-a-service (BaaS) provider facilitated financial transactions for FinTech startups; unfortunately, in early 2024, Synapse had to file for bankruptcy and cease operations due to operational failures and fraud-related issues.
The Synapse crisis exposed vulnerabilities in FinTech infrastructure, particularly for companies relying on third-party providers without strong fraud prevention mechanisms. For FinTech founders, the message is clear: customer trust is fragile, and security cannot be an afterthought. Building a great product in FinTech goes beyond the most seamless product use or creating the best user interface.
It’s vital to ensure that customers’ funds are safe from the grasp of fraudulent cybercriminals as traditional systems suffer from three major limitations, according to Samuel Jaja.
- Rule-based systems used in traditional systems are too rigid. Rule-based systems can only detect fraud patterns that have been previously identified and explicitly programmed.
Like machine learning systems, Fraudsters constantly learn from mistakes, whether on their part or others and constantly move on to new methods, especially if the newly devised method is much easier and more covert. Taking time to update rules means compliance teams will never stay on top of detecting or preventing fraud. - Traditional systems generate many false positives. This is bad for the customer experience and overwhelms fraud teams who must manually review these cases.
- Traditional systems operate in silos. Traditional systems often analyze single transaction points without considering the broader context of user behavior. They might flag a large transaction without considering if it’s consistent with the customer’s usual end-of-month pattern.
- Traditional systems are largely unable to detect new, emerging fraud patterns. Traditional systems can’t identify what they haven’t seen before, which is dangerous because fraudsters now employ more sophisticated techniques like synthetic identities or coordinated attack networks.
How Machine Learning is Powering Fraud Detection and Risk Management in FinTech
Previously, the traditional fraud detection model in banking typically entailed rule-based systems or methods. The system used predefined parameters to identify suspicious transactions.
However, as technology evolved, fraudsters adopted techniques that evolved with available technologies, such as deep fakes, synthetic identity fraud, etc., and traditional systems can not effectively monitor the advanced methods fraudsters may use to bypass them.
This is why machine learning has become increasingly relied upon. Machine learning can process and learn from large datasets and recognize complex fraudulent behaviors that rule-based systems may miss.
It can also constantly adapt and improve detection accuracy over time based on the new data it receives. Machine learning offers numerous advantages for fraud detection and risk management, including:
1. Real-time Time Detection
Unlike traditional methods, machine learning enables real-time transaction monitoring and can help identify fraudulent activities before or as they happen. Samuel Jaja explains the process: “ML models look for deviations from established patterns simultaneously at multiple levels. They’re analyzing hundreds of data points per transaction in milliseconds, far more than any rule-based system could process.”
At the transaction level, models examine apparent factors like amount, merchant category, and location. Then, they examine even more robust data.
They examine the transaction velocity (the number of transactions made in a timeframe), the sequence of transactions, the device being used, typing patterns, and even how someone navigates through the app.
“Machine Learning systems for fraud detection and prevention are powerful because they can establish baselines at different levels, take multiple signals, weigh them appropriately, and make an accurate decision in milliseconds while the transaction continues.”
2. Adaptive Learning / Continuous Improvement
Machine learning models continuously evolve and learn from data. This way, they share features with a “living compliance officer.” However, they can detect fraud in real-time, which makes them incredibly powerful and reliable. Capital and expertise can always be a constraint, but it doesn’t hurt to implement systems one step at a time to enable seamless scaling and less workload on the team.
“I always recommend starting with an anomaly detection system focused on transaction monitoring. It provides the most immediate return on investment and establishes the foundation for more sophisticated fraud prevention.
The key is to begin simple but build with expansion in mind. Start by implementing a system that establishes individual customer baselines and flags significant deviations, unusual transaction amounts, unexpected merchant categories, abnormal timing or frequency, or geographical anomalies.”
Machine learning models can learn new fraud patterns and improve accuracy over time, making it harder for fraudulent activities to slip through the cracks.
3. Pattern Recognition/ Anomaly Detection
Machine learning models can analyze large datasets to identify unusual spending behaviors, login patterns, and device fingerprints.
By spotting discrepancies in normal user behavior, FinTech companies can flag potentially fraudulent transactions early, allowing for an improved fraud risk management plan for your FinTech company.
4. Minimized False Positives
One challenge of traditional systems is the high rate of false positives detected, where legitimate transactions are mistakenly flagged as fraudulent. For Samuel Jaja, this is where sophistication comes in.
Modern systems use a multi-layered fraud detection approach, combining different models (ensemble models) for better accuracy, analyzing contextual data like user behavior and device information, and applying flexible, dynamic risk thresholds tailored to each situation.
“A new customer making a large international transfer might trigger additional verification, while for an established business customer with that regular pattern, it flows through.”
Machine learning improves accuracy by distinguishing between genuine and suspicious activities. This reduces friction for legitimate customers and saves time spent investigating and analyzing false positives.
Read: Why Traditional Banks Should Stop Trying to Innovate (and Focus on Partnerships Instead)
5. Automated Risk Scoring
AI-powered systems assign risk scores to transactions and user activities based on their past behaviors, device locations, and spending habits. Higher-risk transactions may trigger additional authentication steps, such as multi-factor authentication (MFA) or a full-on restriction.
Enabling this process may be tricky; it is a double-edged sword. It may leave customers feeling frustrated, or it may prevent fraudulent activity. This is why it is important to implement seamless communication and user experience as fraud detection and prevention measures are implemented.
6. Detecting Synthetic Identity Fraud
Fraudsters now use AI-generated fake identities to open fraudulent accounts, and it is being used more than ever, especially with the rise of access to generative AI tools.
Fraudsters increasingly use AI-generated fake identities to open fraudulent accounts. Machine learning models analyze inconsistencies in identity data, like mismatched addresses, biometric inconsistencies, or suspicious usage patterns, to detect and block synthetic fraud.
7. Behavioral Analysis & User Authentication
“Behavioral analysis has emerged as one of the most powerful tools in our arsenal, especially for combating sophisticated fraud. It’s effective because it focuses on how people interact with systems rather than just what they do.”
AI improves fraud detection through behavioral analysis. To verify users, it analyzes typing speed, mouse movements, and login behaviors. When an account suddenly exhibits a new typing pattern or device usage, the system flags it suspicious.
This approach analyzes hundreds of subtle signals, including how users navigate an app, how fast they type, their swipe patterns, transaction timing, and the angle at which they hold their phone. It establishes a behavioral baseline for each user and detects deviations that might indicate account takeover or manipulation.
This approach is effective because it is hard for fraudsters to mimic. Fraudsters can steal a person’s credentials, but they can’t easily replicate how that person interacts with their banking app.
“An associate of mine worked with a FinTech company in Kenya that implemented behavioral biometrics and reduced account takeover fraud by over 60% in the first six months. The system was detecting subtle changes when accounts were accessed by unauthorized users, even when all the login credentials were correct.”
However, the tricky part is balancing sensitivity with user experience. People do not always act the same. What if they’re holding a coffee in one hand or rushing to catch a bus, and their phone habits change? The smartest systems get that and can differentiate between normal quirks and something suspicious.
8. Enhanced Compliance & Regulatory Adherence
The regulatory environment for FinTechs and neobanks is constantly evolving as governments assess and improve regulatory guidelines.
The FinTech industry is relatively new. However, FinTechs must comply with dynamic and always-changing anti-money laundering (AML) and Know Your Customer (KYC) regulations. New requirements, like updates to Ultimate Beneficial Ownership reporting, are always emerging.
“The most valuable is AI’s ability to adapt to regulatory changes. When new requirements emerge, like the recent updates to Ultimate Beneficial Ownership reporting, AI systems can quickly adjust their monitoring parameters without complete redesigns.”
Machine learning helps automate compliance checks, monitor transactions for fraudulent activities, and generate reports to meet regulatory requirements. Samuel Jaja thinks that AI is no longer a tool for cutting compliance costs but for strategic advantage. Traditional compliance processes are manual, resource-intensive, and often reactive, reviewing transactions after they’ve occurred. AI enables a shift to proactive, real-time compliance monitoring.
“In AML (Anti Money Laundering), the machine learning model can analyze complex networks of transactions to identify potential money laundering patterns that rule-based systems would miss, like connecting seemingly unrelated accounts and identifying coordinated suspicious activities.
For KYC requirements, AI enhances the accuracy and efficiency of identity verification with computer vision algorithms that can verify ID documents and perform facial matching with greater precision than manual reviews, while also detecting sophisticated forgeries.”
Machine learning and artificial intelligence systems or solutions do not replace human compliance officers. They enable them to make smarter and faster decisions by providing clear, documented reasoning.
“Advanced AI systems maintain detailed, explainable records of why certain decisions were made, which satisfies regulators’ expectations for transparency and accountability. When a compliance officer needs to explain why a particular transaction was flagged or approved, the system provides clear, documented reasoning.”
9. Cost Efficiency & Scalability
AI-driven fraud detection automates processes that would otherwise have required large fraud investigation teams, which is not cost-effective. With machine learning, FinTech companies can scale their fraud prevention strategies without significantly increasing operational costs. The good news? Effective AI fraud detection is becoming increasingly accessible.
Jaja Samuels explains that AI-driven fraud detection and prevention does not have to be “capital-intensive.” Open-source tools can be leveraged. Frameworks like TensorFlow and PyTorch provide sophisticated capabilities without licensing costs. Some open-source projects specifically for fraud detection provide pre-built components you can adapt.
A focused approach can be more useful and cost-effective than detecting everything. Identify your highest-risk areas through simple analysis. Companies can examine chargeback data, customer complaints, or manual reviews to determine where fraud occurs. Companies can build targeted models for those specific vulnerabilities when this area is identified.
A hybrid approach should be considered. Companies don’t need to jump directly to advanced deep learning. They can start with simpler statistical methods and rule-based systems informed by machine learning insights. This creates a foundation that can be gradually enhanced as resources grow.
API-based services that offer fraud detection capabilities with usage-based pricing. These API services allow access to sophisticated models without building everything in-house. Fraud teams and executives should ensure that data privacy and lock-in are not created.
Companies should learn to collaborate when possible. A collective approach helps everyone build more robust defenses without each company having to independently detect every new fraud pattern.
“In some markets, smaller FinTechs pool anonymous fraud data and insights through industry associations or informal networks”
The Way Forward For Machine Learning In Compliance and Fraud Risk Management
As the FinTech industry evolves, risk management frameworks and fraud risk management software will advance in the coming years. FinTechs are now prioritizing payment fraud detection and constantly exploring ways to make products foolproof.
Revolut has implemented advanced machine learning models to analyze transaction patterns and user behaviors for real-time fraud detection. Their system processes numerous variables per transaction to assess risk levels, incorporating factors such as spending patterns, location data, device information, and behavioral biometrics to identify anomalies indicative of fraud. This approach has resulted in a 30% reduction in fraud losses related to card scams, particularly those involving investment opportunities.
One major shift on the horizon is the rise of explainable AI (XAI). As AI-powered systems become more sophisticated, the need for transparency will become more indispensable. FinTech companies must be able to justify their risk assessments, not just for regulatory compliance but also to maintain customer trust.
XAI will make AI-driven decisions clearer and more accountable, ensuring the “why” behind risk-related actions is understood. Operationally, black-box models make it difficult for fraud analysts to review alerts effectively. Analysts can’t make informed decisions or provide meaningful feedback to improve the system without understanding why something was flagged.
“From a regulatory perspective, authorities increasingly demand transparency. In many jurisdictions, you must be able to explain exactly why you blocked a transaction or flagged an account. “Because the algorithm said so” isn’t an acceptable answer.”
Jaja also explains that the best XAI systems in fraud detection do not just provide raw feature importance but are also able to translate the model’s decision into understandable reasons, like “This transaction was flagged because it occurred outside your normal geographic area, was for an amount significantly larger than your typical spending, and was to a merchant you’ve never transacted with before.”
Another game-changer will be the fusion of AI with blockchain technology. With this fusion, fraud detection systems can enhance the security and traceability of financial transactions, making fraud detection smarter and compliance monitoring stronger. AI-powered smart contracts and automated risk management tools also hold immense value for fraud risk management. They will help reduce manual oversight and make financial security more seamless and efficient.
Federated learning and privacy-preserving AI are also emerging developments that major key players in the FinTech industry are excited about. Imagine FinTech platforms collaborating and sharing risk intelligence without exposing sensitive user data.
“The concept is powerful—training AI models across multiple institutions’ data without that data ever leaving their secure environments. Each FinTech trains models on their local data, then only shares the model updates rather than the underlying customer information.”
However, federated learning and privacy-preserving AI present some challenges. Systems using this technology can be vulnerable to poisoning attacks, where a participant (or compromised system) introduces malicious updates that can undermine the model’s effectiveness. Computational efficiency challenges and questions about handling vastly different data distributions across participants also exist.
“Organizationally, it requires unprecedented cooperation between natural competitors. Questions about intellectual property, competitive advantage, and governance become extremely complex.”
When I asked Samuel Jaja about what excites him most about the future of artificial intelligence and machine learning in fraud prevention, he highlighted three interesting pathways:
- Multimodal AI systems can simultaneously analyze different data types, such as transaction data, user behavior, document images, and voice patterns during customer service calls, and create a unified risk assessment. Because the holistic approach will catch fraud schemes that exploit the gaps between different systems.
- Even more sophisticated synthetic data generation is being used for fraud detection training. Techniques like generative adversarial networks (GANs) allow companies to create realistic synthetic fraud examples, dramatically improving model performance for emerging fraud patterns.
This technique allows for development of even more effective fraud detection systems backed by AI. However, one of the biggest challenges in developing this pathway is having sufficient examples of rare fraud types to train models effectively.
- The development of edge AI for fraud detection. Processing certain risk signals directly on user devices enables faster responses and enhances privacy. This pathway is quite promising. Engineers are seeing promising results with systems that detect unusual device manipulation or suspicious navigation patterns locally before the transaction reaches the server.
Samuel Jaja also expressed his enthusiasm about the increased accessibility of machine learning fraud detection and prevention systems by companies in emerging markets or growing companies.
“What excites me most is seeing these technologies become more accessible to smaller FinTechs and those in emerging markets. The democratization of AI tools allows startups to implement sophisticated fraud detection that was previously only available to the largest players. This levels the playing field against fraudsters who have historically targeted smaller platforms with less robust protections.”
Artificial intelligence is the new disruptor that will cause immense improvements in compliance and fraud risk management. Companies that embrace artificial intelligence as an aid for fraud detection and risk management will be the ones to stay ahead because they have chosen to keep their customers safe and scale their business with confidence and low costs.
Final Thoughts
As fraudsters adopt sophisticated measures for equally fraudulent activities, AI-powered fraud prevention systems will constantly evolve to help FinTechs combat fraud with the agility and intelligence needed to stay ahead of cybercriminals.
By utilizing machine learning, FinTechs can help create safer digital ecosystems, reduce financial losses, and improve customers’ confidence in online transactions again.
However, understand that machine learning systems for fraud detection and prevention do not replace humans and are only as good as the data they are trained on. Lapses in training data may create blind spots for criminals to exploit. Therefore, they shouldn’t be over-relied on.
“AI models are only as good as the data they’re trained on. If your historical data doesn’t include certain fraud patterns, your model won’t recognize them. This creates blind spots that sophisticated fraudsters can identify and exploit.”
