How AI Improves Wallet Security in iGaming
Jun 19, 2025
Fluid
Explore how AI enhances wallet security in iGaming by preventing fraud, improving compliance, and creating safer environments for players.
AI is revolutionising wallet security in iGaming by reducing fraud, enhancing compliance with Malta Gaming Authority (MGA) standards, and creating safer environments for players. Here's how AI is making a difference:
Real-time fraud detection: AI analyses transactions in milliseconds to flag suspicious activity, reducing account takeovers by up to 68%.
Behavioural analytics: AI monitors user interaction patterns like typing speed and navigation to verify identities, cutting fraudulent transactions by 27%.
Dynamic risk scoring: Each transaction is evaluated in real-time using multiple data points, adapting to new threats without manual updates.
Reduced false positives: AI-powered systems decrease false alarms by over 40%, ensuring smooth experiences for legitimate users.
Proactive threat response: AI learns from past incidents, evolving to counter emerging fraud tactics.
For Malta's iGaming operators, AI isn't just about staying secure - it's about meeting strict MGA regulations while improving player trust and satisfaction.
How digital wallets and biometric security are shaping the next-gen casino experience
AI Fraud Detection Systems
AI-driven fraud detection systems are transforming how businesses identify and prevent fraudulent activities. By analysing vast amounts of data, these systems uncover patterns that might escape human reviewers. They simultaneously assess user behaviour and device attributes, providing a comprehensive defence. Today, over 60% of digital payment platforms rely on machine learning algorithms to detect and address suspicious activity.
The iGaming industry, in particular, faces a growing fraud problem. Between 2022 and 2024, online gaming fraud surged by 64%, leading to losses of €2.8 billion from first-party fraud in 2024 alone. As fraud schemes grow more sophisticated, traditional rule-based systems fall short, making advanced AI solutions a necessity. Let’s delve into how these systems tackle fraud.
How AI Spots Fraud Patterns
Machine learning models are exceptional at identifying subtle fraud patterns. They process hundreds of variables at once, such as login times, device details, betting habits, and withdrawal requests. These models can achieve detection accuracy rates of 90–95%, a significant improvement over the 70–80% accuracy typically seen with older rule-based systems.
One of the most prevalent threats is account takeovers. In 2023, 4% of logins on gambling platforms were linked to account takeover attempts, and these incidents accounted for 52% of all iGaming fraud cases. AI systems combat this by analysing behavioural biometrics - such as typing habits, mouse movements, and navigation patterns. Subtle deviations in these behaviours can trigger alerts when someone other than the account owner attempts to log in.
A great example of AI in action is PayPal's success story. By implementing machine learning to monitor transaction patterns in real time, PayPal cut fraudulent transactions by over 50% in just two years.
Another area where AI shines is in tackling bonus abuse. Fraudsters often exploit promotions by creating multiple accounts to claim welcome bonuses. AI systems detect such schemes by identifying links between accounts - whether through shared devices, similar betting behaviours, or connected payment methods - revealing coordinated fraud attempts.
Instant Alerts for Suspicious Activity
Real-time monitoring is a game-changer, flagging suspicious activities within milliseconds. This swift response has been shown to reduce account takeover incidents by as much as 68%. Speed is critical, as fraudsters often act quickly to maximise their gains before being detected.
AI systems employ dynamic risk scoring to evaluate each transaction as it occurs. High-risk behaviours, such as large withdrawals from new accounts or unusual betting patterns, trigger instant alerts. For instance, American Express improved its fraud detection rates by 20% using predictive analytics that analyse historical transaction data to anticipate potential threats. Additionally, machine learning has reduced false positives in fraud detection by over 40% compared to older methods.
"AI means from stringent to pre-emptive, enabling casinos to forecast and prevent fraud before its occurrence rather than just kick it out afterward on each alert the system gives."
Craig Davies, SBC News
Fluid's Fraud Prevention Features
Fluid is a prime example of how AI can be seamlessly integrated into payment systems to enhance security without disrupting the user experience. Its fraud prevention solution is built directly into the digital cashier, ensuring that payments are both secure and smooth for legitimate players. Fluid’s machine learning algorithms analyse transaction patterns, user behaviour, and device characteristics in real time.
What sets Fluid apart is its ability to process multiple data streams simultaneously. When a player makes a deposit or withdrawal, Fluid’s AI evaluates interaction patterns, device fingerprints, transaction histories, and more. This ensures legitimate transactions proceed without delays while blocking suspicious activity.
Fluid’s system is designed to adapt to new threats. Its AI models continuously learn from each transaction, refining their understanding of normal and suspicious behaviours. This adaptability keeps the system effective against emerging fraud tactics without the need for constant manual updates.
Payment providers using AI-powered real-time monitoring, like Fluid, have reported a 50% reduction in fraud incidents by flagging unusual spending behaviours. Fluid’s approach combines fraud detection with other security measures, such as device fingerprinting and behavioural analytics, creating multiple layers of defence. This comprehensive strategy not only reduces fraud but also maintains a personalised and seamless experience for players.
Real-Time Transaction Monitoring
For MGA-licensed iGaming operators, real-time monitoring isn't just a nice-to-have - it's a regulatory must. The Malta Gaming Authority (MGA) enforces strict requirements around financial transparency and detailed technical reporting to ensure system integrity and safeguard players. This means analysing transactions instantly is not just about security; it’s about staying compliant.
In an industry where high stakes and risky transactions are the norm, quick action is critical. Modern AI systems can assess transactions in milliseconds, leaving outdated batch processing methods - often delayed by hours or even days - in the dust. This speed doesn’t just improve efficiency; it enhances the ability to detect anomalies and assign risk scores dynamically.
"Our fraud algorithms monitor, in real time, every American Express transaction around the world for more than $1.2 trillion spent annually, and we generate fraud decisions in mere milliseconds." - VP of Machine Learning and Data Science, American Express
Detecting Transaction Anomalies
AI technology shines when it comes to identifying unusual patterns in deposits, withdrawals, or betting activity. By creating individual behavioural baselines for each player, these systems can analyse thousands of transactions to determine what’s considered normal for a specific account. When something seems off - like an unusually large deposit, withdrawals at odd hours, or irregular betting patterns - the system flags it immediately for further review.
In fact, a 2021 study revealed that AI-driven money laundering detection systems were able to cut down suspicious transaction volumes by as much as 40%. These anomaly detection methods work hand-in-hand with other fraud prevention tools, bolstering the security of player wallets. On top of this, dynamic risk scoring takes these insights further by refining risk evaluations as they happen.
Dynamic Risk Scoring
Dynamic risk scoring is a game-changer compared to the older, static rule-based systems. Instead of sticking to rigid rules that can’t keep up with evolving threats, dynamic scoring evaluates each transaction in real time. It pulls data from multiple sources - like transaction histories, device fingerprints, location patterns, betting habits, and timing anomalies - to calculate a risk score. This approach allows the system to learn and adapt on its own, eliminating the need for constant manual updates.
Behavioural Analytics for User Verification
Dynamic risk scoring focuses on evaluating individual transactions, but behavioural analytics takes security a step further by creating detailed profiles of how players naturally interact with platforms. Every click, swipe, and keystroke becomes a checkpoint, making it incredibly challenging for fraudsters to mimic genuine behaviour. This method works hand-in-hand with real-time transaction monitoring, ensuring continuous user verification.
Platforms that incorporate behavioural analytics have seen a 27% drop in fraudulent transactions. This reduction is achieved through AI's ability to define what "normal" behaviour looks like for each user and flag anything that deviates from that baseline as potentially fraudulent.
Tracking User Interaction Patterns
AI systems go beyond simply monitoring login credentials. They track a wide range of behaviours, including betting habits, login times and locations, deposit and withdrawal patterns, and even how users navigate through the app. On a more advanced level, the technology can assess biometrics like mouse movements and typing speeds to confirm a user's identity.
By analysing thousands of interactions over time, machine learning algorithms establish a baseline for each user. These dynamic baselines make it easier to spot unusual activity, such as sudden spikes in betting amounts, rapid changes in betting frequency, or logins from locations that are geographically far apart within a short timeframe. AI can also identify patterns like irregular betting speeds or repetitive actions, which often signal bot activity rather than genuine human behaviour.
"AI goes beyond basic transaction tracking - it analyzes behavioural patterns to pinpoint early signs of problematic gambling. Sudden increases in deposits, prolonged gaming sessions, and erratic betting habits can be red flags." - Optimove
Ongoing Authentication During Transactions
Behavioural analytics powered by AI enables passive, ongoing user verification by monitoring typing speeds, device movements, and touch patterns throughout a session.
This continuous authentication ensures that even after a transaction starts, the system keeps an eye on user behaviour. If someone else takes control of the account mid-transaction, the AI quickly detects the shift in behavioural patterns. Any deviations from the user's typical behaviour can trigger additional security checks, aligning with zero trust principles.
This approach allows players to make deposits, place bets, or withdraw funds without constant interruptions, while maintaining strong security throughout the session.
Fluid's Behavioural Analytics Capabilities
Fluid takes behavioural analytics and integrates it into a multi-layered security framework that builds on dynamic risk scoring and real-time monitoring. By analysing user interaction patterns, Fluid ensures secure payment journeys while enhancing the overall user experience. The platform's AI continuously monitors behaviour, flagging deviations from an established baseline and activating additional security measures when needed - without disrupting genuine users.
Fluid also uses this behavioural data to optimise payment interfaces, tailoring them to individual preferences. This not only strengthens fraud detection but also improves conversion rates by aligning with how players naturally interact with the system. It's a dual benefit: robust security operating in the background and a smoother user experience in the foreground.
AI Learning and Threat Response
AI's strength in wallet security isn't just about what it can do today - it's about its ability to evolve alongside emerging threats. As fraudsters refine their tactics, AI systems keep pace by learning from new data, offering a proactive defence against the latest risks. This constant evolution hinges on continuous model training, a concept we'll dive into next.
Training AI Models with New Data
AI models grow smarter over time by learning from every blocked transaction, false positive, or even successful attack. This ongoing feedback loop creates a dynamic and ever-improving security system. Training these models involves analysing data from historical transactions, user behaviour trends, and documented fraud cases. Machine learning algorithms process this information to pick up on subtle fraud indicators, continuously refining their understanding of what’s normal and what’s suspicious.
For example, major financial institutions have boosted fraud detection accuracy by employing real-time monitoring systems. However, high-quality data is the backbone of these systems. Robust data collection, thorough cleansing, and frequent auditing are essential to ensure that the AI models operate with fairness and transparency. These practices prepare the system to tackle emerging fraud techniques head-on.
Responding to New Fraud Techniques
AI's ability to adapt in real time makes it a game-changer in combating evolving fraud tactics. Unlike traditional rule-based systems, AI continuously analyses vast datasets to uncover complex patterns that older algorithms might overlook. For instance, between 2022 and 2024, online gaming fraud surged by 64%, with iGaming operators losing €2.6 billion to first-party fraud in 2024 alone.
AI excels at spotting new fraud patterns by identifying anomalies in established baselines. A European betting site, for example, used an AI model to detect a coordinated betting scheme on a specific match, leading to the suspension of multiple fraudulent accounts.
"AI is reshaping the landscape of online gambling fraud prevention. By using machine learning, pattern recognition, and real-time monitoring, AI is helping operators detect and prevent fraud faster and more accurately than ever before." – SDLC Corp
This ability to adapt on the fly offers a level of protection that static security measures simply can't match. It allows for the swift detection of new attack vectors, unusual transaction patterns, and emerging bot behaviours.
Fluid's Real-Time Optimisation Features
Fluid embodies this evolving approach by embedding continuous learning into its real-time transaction monitoring system. Its machine learning models assess every transaction and user interaction, refining their understanding of legitimate behaviour and potential fraud indicators. As threats evolve, Fluid’s system updates its algorithms automatically, providing robust protection without requiring manual intervention or system downtime.
Conclusion: AI's Impact on iGaming Wallet Security
AI is reshaping how iGaming platforms safeguard wallet security, offering real-time responses to ever-changing threats. With its ability to analyse massive amounts of data, spot complex fraud patterns, and deliver immediate risk evaluations, AI has become a critical tool for operators aiming to protect their players and their business.
For example, behavioural analytics powered by AI have been shown to reduce fraud by 27%, while AI-enhanced KYC systems achieve an impressive 84.2% accuracy in identifying suspicious activities. This level of precision not only strengthens transaction security but also creates a smoother, more personalised experience for users.
Beyond tackling fraud, AI also improves the overall user journey. Take Fluid’s AI-powered system as an example - it seamlessly combines real-time risk assessments with customised user experiences. By integrating advanced fraud detection with tailored payment flows, operators can maintain high security standards while boosting conversion rates and player satisfaction.
With the online gambling market expected to grow to €144.5 billion by 2028, investing in AI-driven wallet security is not just about protection - it’s about ensuring long-term growth. As threats evolve, AI’s ability to learn and adapt keeps security measures ahead of the curve, fostering the trust that modern iGaming businesses need to succeed in a highly competitive space.
FAQs
How does AI identify and prevent fraudulent activity in iGaming transactions?
AI strengthens wallet security in the iGaming world by keeping a close watch on user behaviour, transaction histories, and betting habits in real time. Through behavioural analytics and anomaly detection, it can identify anything out of the ordinary - like sudden spikes in betting amounts, logins from unexpected locations, or unusual transaction patterns.
When something suspicious pops up, the system acts fast. It might flag the activity for further review or even temporarily block the transaction. This quick response creates a safer gaming experience, shielding both players and operators from potential fraud.
How does AI help iGaming operators meet Malta Gaming Authority (MGA) compliance standards?
AI plays a crucial role in helping iGaming operators meet Malta Gaming Authority (MGA) regulations by boosting security, streamlining processes, and ensuring accountability. One of its key applications is in fraud detection, where it identifies suspicious activities to keep transactions secure and aligned with regulatory requirements.
Another major benefit is automation. AI handles essential compliance tasks like preparing and submitting detailed reports mandated by the MGA. This not only reduces the workload for operators but also lowers the risk of human error. By analysing player behaviour, AI further supports adherence to KYC (Know Your Customer) protocols and encourages responsible gaming, protecting both players and operators.
These tools allow iGaming businesses in Malta to stay compliant while delivering a safe and smooth experience for players.
How does AI help detect and prevent new types of fraud in the iGaming industry?
How AI Helps Combat Fraud in the iGaming Industry
AI systems have become a key tool in tackling fraud within the iGaming sector. By analysing massive amounts of transaction and behavioural data in real time, these systems can spot unusual patterns or anomalies that may signal fraudulent activity.
What makes AI particularly effective is its ability to learn and improve over time. With self-updating algorithms, these systems refine their accuracy as they process fresh data, which helps minimise false positives. This constant improvement is crucial because fraudsters are always finding new ways to exploit weaknesses.
With AI on their side, iGaming platforms can create a safer environment for transactions, safeguarding both players and operators from potential financial losses.