Fraud Malware Prevention
Fraud Malware Prevention blocks malware and threats targeting financial institutions. With its AI-based device analysis, passive biometric analysis, and behavioral malware detection technologies, it minimizes fraud losses and improves the customer experience.
Discover the Most Cost-Effective Fraud & Malware Prevention Solution.
Problem
Instead of bypassing systems, attackers now directly target users, deceiving them with social engineering methods to make them approve fraudulent transactions themselves. Furthermore, through malware such as Remote Access Trojans (RATs), attackers can remotely access a victim’s device, perform all transactions as the legitimate user, and bypass traditional security controls.
Mobile channels, in particular, pose a higher risk due to increasing trojan and malware activities and advanced fraud techniques. Existing fraud detection systems are often insufficient in detecting these advanced attack types, as they mostly focus only on the user’s identity and transaction details, without analyzing how the transactions are performed. Therefore, in the evolving threat landscape, there is a need for innovative and advanced technologies like behavioral analysis for effective protection.
What is Fraud Malware Prevention?
Fraud Malware Prevention offers a proactive and predictive stance against digital fraud, rather than a reactive defense. Our platform is a next-generation cybersecurity solution that simultaneously analyzes every user interaction across three key dimensions—from the device’s digital DNA to the user’s behavioral signature. Our Device Monitoring technology detects and neutralizes fraud attempts at their inception, ensuring a completely smooth, seamless, and secure digital experience for your customers.
Multi-Layered Defense
The cloud-based Fraud Malware Prevention combines critical technologies like Device Fingerprinting, Passive Biometric Analysis, and Malware Detection not in separate silos, but in a single, integrated system where they are in constant communication. Thanks to this synergy, complex and multi-stage attack patterns that each technology could not detect alone can be uncovered. A signal generated by one technology is instantly verified or refuted by the findings of the others, thus maximizing detection accuracy.
Continuous and Passive Authentication
Traditional security controls are typically activated only at critical moments like login or payment. This "security guard at the door" approach is blind to threats that develop after entry. Fraud Malware Prevention, however, operates on the principle of "continuous authentication." Our security analyses run continuously and completely invisibly in the background, from the moment a user session begins until it ends. This approach provides an uninterrupted shield of protection even against the most insidious attack types, such as Remote Access Trojans (RATs) or mid-session account takeovers (session hijacking).
Artificial Intelligence
Our platform features AI engines trained on global financial transaction data. These AI findings are continuously enhanced by our experienced analysts at our Digital Crime Fighting Centers. This hybrid model adopts an "Explainable AI" approach, enabling your teams to make faster decisions by explaining the concrete reasons behind a risk score.
Fraud Malware Prevention Product Features
VPN Detection
Determines if the user is attempting to mask their identity or location by hiding their real IP address behind a Virtual Private Network (VPN).
IP Geolocation
Identifies the device's real-world geographic location (e.g., country, city) using the IP address from which it is connecting to the internet.
Browser Tamper Detection
Detects if an attacker has manually altered browser properties, such as the user agent, version, or other settings, in an attempt to impersonate another user.
Velocity Signals
Analyzes the number of actions performed by a device or user within a specific time frame (e.g., the number of login attempts in one hour). An abnormally high velocity can be an indicator of fraud.
Suspect Score
A general metric that creates a risk score by combining all signals collected from the device (location, velocity, anomalies, etc.). The higher the score, the higher the risk of fraud.
Android Emulator Detection
Detects whether the application is running on a physical Android device or on an emulation software running on a computer, which is frequently used by fraudsters.
Rooted Device Detection
Determines if the security restrictions on an Android device have been removed. Rooted devices pose a higher security risk.
Cloned App Detection
Identifies whether an application has been duplicated on the same device, often for managing multiple accounts or committing fraud.
Factory Reset Detection
Detects if a device has recently been reset to factory settings. Fraudsters often use this method to cover their tracks.
Frida Detection
Detects the presence of "Frida," a tool used by developers and cyber-attackers to analyze or alter the runtime behavior of applications. Its presence can indicate malicious intervention.
Geolocation Spoofing Detection
Uncovers attempts to appear in a different physical location by faking the device's GPS or network-based location data.
MitM Attack Detection
Detects if the user's internet traffic is being secretly intercepted by a third party (a Man-in-the-Middle attack) to steal or alter data.
Bot Detection
Distinguishes whether the entity interacting with the application is a real human or a bot performing automated tasks.
Incognito Detection
Determines if the user is using the browser's "incognito" or "private" mode, which does not record activity history.
Virtual Machine Detection
Identifies if transactions are being performed on a Virtual Machine (VM) instead of a physical computer, a technique used by fraudsters to hide their identity.
IP Blocklist Matching
Checks if the device's IP address is on known blocklists (blacklists) associated with previous cybercrime, spam, or other malicious activities.
Remote Tools Detection
Identifies if the device is being controlled by remote access software like TeamViewer or VNC. This can indicate an account takeover scenario.
High-Activity Device
Detects when a single device exhibits an abnormally high number of activities, such as creating accounts, performing transactions, or making login attempts, far exceeding normal user behavior.
Jailbroken Device Detection
Detects if the operating system restrictions on an iPhone or iPad have been removed.
Privacy-Focused Browser
Detects if the user is using a special privacy-focused browser, such as Tor or Brave, which makes tracking and fingerprinting more difficult.
Behavior-based Malware Detection
A core technique that detects malware based on the malicious actions it performs, rather than its static signatures. This approach is effective against both known and never-before-seen (zero-day) threats.
Abuse of Accessibility Services
Detects when and how Android Accessibility Services are being misused by malware like GoldDigger and Gigabud to read screen content, log keystrokes, and perform actions on behalf of the user.
Active Screen Capturing/Recording
Detects if the device's screen is being actively recorded or broadcast elsewhere in real-time to steal sensitive information.
Unauthorized Remote Access
Detects behavioral biometrics and interaction anomalies that indicate the device is being controlled by a remote operator, not its owner.
Overlay Attacks
Identifies attempts by a malicious application to steal sensitive data, such as login credentials, by placing a fake window (an overlay) on top of a legitimate app.
Sideloaded App Detection
Detects applications installed from unknown or unauthorized sources outside of official app stores, a common distribution method for malware.
Suspicious Permissions Analysis
Analyzes the permissions requested by other apps on the device and identifies risks by flagging those with potentially dangerous combinations of permissions.
App Cloning and Tampering
Performs integrity checks to verify if the host application it runs within has been cloned or if its code integrity has been compromised.
Fraud Malware Prevention
Which Threat Scenarios Does It Prevent?
Fraud Malware Prevention provides a multi-layered defense mechanism specifically designed against the most common and sophisticated fraud methods faced by financial institutions. The platform deeply analyzes the tactics, techniques, and procedures used by fraudsters to develop targeted solutions against each threat vector.
Threat-Solution Matrix
| Threat Scenario | Description | "Fraud Malware Prevention" Solution | Technologies Used |
|---|---|---|---|
| Account Takeover - ATO | A fraudster gains access to a legitimate user's account using stolen credentials obtained through phishing or data breaches. | It instantly detects micro-anomalies between the user's normal behavioral profile (typing speed, mouse usage, navigation habits) and the behavior exhibited by the fraudster. It checks the global reputation database to see if the device has been used in fraud before. |
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| New Account Fraud | Opening fraudulent accounts in bulk using stolen or entirely fabricated (synthetic) identities to abuse welcome bonuses, launder money, or lay the groundwork for future fraud. | It analyzes the application form-filling behavior, capturing suspicious patterns like inhuman, bot-like speed, abnormal navigation between fields, or continuous copy-pasting of data. It detects if the device is an emulator, a virtual machine, or has been used to open hundreds of different accounts previously. |
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| Payment Fraud | Executing unauthorized financial transactions from compromised accounts, with stolen card information, or through social engineering. | It generates an instant risk score by analyzing the user's behavior at the time of the transaction (hesitation, haste, pauses as if following instructions), the device's geolocation (VPN/Proxy/Tor usage), and the transaction amount and recipient against the user's normal spending profile. |
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| Malware and RAT Attacks | A banking trojan or Remote Access Tool (RAT) that has infiltrated the user's device hijacks transactions, steals credentials, or manipulates the session. | It detects suspicious processes running on the device that interact with your banking app, screen overlay attempts that draw fake screens over the legitimate app, and abnormal sensor data (e.g., a transaction being performed while the phone is motionless on a table). |
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| Authorized Push Payment - APP Fraud | The victim is persuaded by a fraudster via phone or messaging to willingly send money from their own device to a fraudulent account. | The platform detects the user's abnormal behavior during the transaction (long pauses, constant switching between screens, segmented and slow data entry as if following instructions), generating a strong alert that the user may be under duress or being coached. |
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| Mule Account Detection | The detection of intermediary accounts and the networks managing them, which are used to obscure the trail of and launder illegally obtained funds. | It analyzes behaviors such as an abnormal number of different accounts being accessed from a single device, inconsistent logins from different geolocations, and abnormal patterns in fund flows to uncover hidden links between seemingly independent accounts and expose mule account networks. |
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Intelligent Security in 3D Secure (3DS) Processes
Cloud-based Fraud Malware Prevention works by adding an invisible and intelligent security layer to the 3D Secure (3DS) authentication process without disrupting the user. Traditional 3DS often relies on an additional step, such as a one-time password (OTP). However, our platform activates before and during this step, making the process both more secure and more seamless.
At the moment of the transaction, our platform performs a risk assessment within seconds using Advanced Device Fingerprinting and Passive Biometric Analysis technologies. Hundreds of data points are analyzed, including device trust, the user’s behavioral signature (such as mouse movements and typing rhythm), and geolocation. If the risk is low, the “frictionless flow” of 3DS 2.0 is supported, and the transaction is approved without requiring an OTP from the legitimate customer. This increases conversion rates and improves the customer experience.
If a 3DS challenge is required, our platform can detect even the most sophisticated attacks, such as Remote Access Trojans (RATs) or social engineering. Anomalies in the user’s behavior (e.g., hesitant data entry under a fraudster’s guidance) or the presence of malware on the device are instantly detected, and the transaction is blocked. In this way, fraudsters attempting to bypass 3DS even with stolen credentials are stopped, financial losses are prevented, and liability is secured for your institution.
Who Is It an Ideal Solution For?
Cloud-based Fraud Malware Prevention is a flexible, scalable, and powerful platform that answers the unique challenges and specific needs of institutions operating in every area of digital finance. Our solution creates concrete and measurable value for each sector by establishing the right balance between security and user experience required by different business models.
Banks
We enable you to grow your digital channels securely without compromising customer trust or increasingly strict regulatory compliance. This allows you to secure customer assets with advanced Account Takeover (ATO) and social engineering protection against the most sophisticated attacks, and improve the customer experience by increasing the adoption of digital banking channels through passive and frictionless authentication methods. Furthermore, by significantly reducing the false positive rate, we enable your fraud teams to focus their time and resources on truly suspicious cases, thereby increasing operational efficiency.
Payment Institutions
In today's highly competitive market, we enable you to securely onboard every transaction and new user to your platform without sacrificing your pace of innovation. With real-time transaction risk analysis, you can prevent fraudulent transactions to significantly reduce chargeback rates and their associated costs. You can increase conversion rates by offering fast and seamless customer acquisition processes, thereby gaining market share from your competitors. Additionally, thanks to our flexible and customizable rule engine, you can easily define and apply security policies aligned with your institution's risk appetite for different products, markets, or customer segments, effectively optimizing risk management.
Crypto-Asset Service Providers
In this high-risk and highly targeted sector, we enable you to protect your platform's and your users' digital assets against the most advanced cyberattacks. You can proactively detect and block malware designed for crypto-assets, such as threats like ClipBanker or CliptoShuffler that change wallet addresses when they are copied to the clipboard. In addition, by identifying mule accounts, illicit fund flows, and fraud networks at an early stage, you can effectively fulfill your Anti-Money Laundering (AML) obligations and protect your platform from illegal activities. All these security approaches strengthen the trust and reputation of your platform in the eyes of both your existing users and potential investors.
Fintechs
For all fintechs in the evolving financial technology ecosystem, from start-ups to industry leaders, Fraud Malware Prevention offers effective protection against increasing cyber threats and regulatory pressures, alongside meeting the requirements for speed, scalability, and innovation. Our platform provides consistent security across multiple digital channels and products, supporting the highest level of user experience without losing speed and flexibility during product launches. Through advanced identity and device verification mechanisms, it minimizes fraud in customer registration processes and ensures legal compliance and operational security when expanding into new markets. With risk profile management and reporting tailored to different customer segments, it allows institutions to increase both customer satisfaction and operational efficiency.
Global Risks in the Financial Sector
The rapid adoption of digital banking worldwide has created a vast and attractive attack surface for cybercriminals. According to the FBI’s 2023 Internet Crime Report, reported losses from cybercrime exceeded $12.5 billion, underscoring the massive financial impact of these threats. Mobile devices, in particular, have become a primary target, with a significant global increase in banking malware attacks.
The innovative yet complex API and cloud-based infrastructures of the finance and fintech sectors are introducing new security risks, such as API vulnerabilities and cloud misconfigurations. This situation strengthens the perception, even among industry employees, that security teams are struggling to keep pace with the evolving threat landscape.
During the 2024-2025 period, a significant rise in sophisticated mobile malware like Anubis and SOVA is being observed. Meanwhile, the proliferation of the “Malware-as-a-Service” (MaaS) model is making cybercrime more accessible to a wider range of attackers. Modern attacks increasingly employ advanced techniques designed specifically to bypass or disable traditional security software.
In the face of these new, multi-faceted threats, single-layered, conventional security methods are no longer sufficient.
Fraud Malware Prevention: Technical Specifications and Capabilities
The power of our platform comes from the deep synergy of three core, interconnected technology pillars that learn from and complement each other. These technologies analyze every user interaction at a microscopic level, leaving no blind spots for fraudsters to hide.
1. Advanced Device Fingerprinting
This technology assigns a unique digital identity (hash value) to every web browser and mobile device by combining hundreds of hardware, software, network, and behavioral parameters. This digital identity, which is nearly impossible to spoof or alter, constitutes the device's digital DNA. It ensures the device can be consistently recognized even if its IP address changes, cookies are deleted, or it is browsing in incognito mode.
Some Collected Data Points:
Device Monitoring Parameters include hundreds of hardware and software parameters, as well as advanced fingerprinting techniques and network information. These comprehensive data collection methods allow for the creation of a unique device profile.
1. Hardware Properties:
Processor: Processor type and number of cores.
Screen Details: Screen resolution and color depth.
Battery Status: Battery level and charging status.
Storage and Memory: The device's total memory and storage space.
Sensor Data: Data from sensors like the accelerometer and gyroscope.2. Software and Browser Information:
Operating System: The operating system and its version.
Browser Details: Browser type and version (user-agent string).
Fonts and Plugins: Installed system fonts, browser plugins, and extensions.
Supported File Types: MIME types supported by the browser.
Language and Time Settings: Language and time zone settings.3. Network Connection Parameters:
IP and Location: IP address and associated geolocation information.
Connection Type: Connection type, such as Wi-Fi or mobile.
Internet Service Provider: Information about the Internet Service Provider (ISP).
DNS and Proxy Detection: Detection of used DNS servers and proxy/VPN usage.4. Advanced Fingerprinting Methods:
Canvas Fingerprinting:The way the browser draws text or graphics onto a hidden canvas is analyzed to capture the unique rendering signature of its graphics card.
WebGL Fingerprinting: Another unique layer of identification specific to the hardware is created by testing its 3D graphics capabilities.
The Importance of these Data Points:
Fraud Malware Prevention instantly detects virtual machines (VMs), Android/iOS emulators, automation tools like Selenium and Puppeteer, and specially designed "anti-detect" browsers used by fraudsters to conceal their identity.
It uncovers fraud rings by detecting attempts to open an abnormal number of accounts or perform transactions from a single device. When a device used for fraudulent activity is identified at one of our clients, it is blocklisted across our entire client network. This proactively prevents fraudsters from using the same device to attack other platforms.
2. Passive Biometric Analysis
Passive Biometric Analysis goes beyond traditional authentication methods—what you know (password), what you have (phone), or what you are (fingerprint, face)—to verify user identity purely based on how they behave. Every physical user interaction on your digital platform is analyzed at the millisecond level to create a "digital body language" or behavioral signature profile that is unique and inimitable for each individual. These comprehensive analyses run seamlessly in the background without disrupting the user experience.
Analyzed Behavioral Data:
Keystroke Dynamics:A user's keyboard usage habits are as unique as a fingerprint. Dozens of metrics are analyzed, including typing speed, key press duration, delays between keystrokes (rhythm), frequently used key combinations, backspace key usage frequency, and transitions between upper and lower case.
Mouse / Cursor Dynamics: Mouse/Cursor Dynamics: Features such as the trajectory of mouse movements (curved or straight), speed, and acceleration are analyzed, along with pauses before and after clicks and navigation patterns on the page. This data provides valuable information about the user's current state, such as hesitation or haste, and their overall intent.
Touchscreen Interactions (Mobile):On mobile devices, data is collected on the most frequently used areas of the screen, the pressure of a finger's touch, the speed and length of swipes, and pinch-to-zoom gestures.
Device Handling and Motion (Mobile):Using data from the device's gyroscope and accelerometer sensors, the user's phone holding angle, which hand they use, and micro-movements while walking or standing still are analyzed and incorporated into the behavioral profile.
The Role of Passive Biometric Analysis in Fraud Detection:
Passive biometric analysis distinguishes between humans and fraudsters/bots with high accuracy. It detects anomalies by contrasting the natural behaviors of real users (thinking, hesitating, making mistakes, personal rhythm) with the inhuman speed and perfection of bots or the methods of fraudsters, such as entering stolen data via copy-paste. Through this micro-behavioral analysis, it is determined whether a real user or a threat is behind the session.
This analysis provides continuous authentication every second of the session without the user's knowledge. As long as the user's behavior is consistent with their established profile, no additional verification step or password prompt is required. However, a sudden change in behavior (e.g., calm mouse movements being replaced by tense and rapid ones) is perceived by the system as a strong indicator of an account takeover (ATO) or social engineering attack. In such cases, the system can take automated actions, such as stopping the transaction or activating additional security layers.
3. Proactive Malware Detection
Unlike traditional antivirus software, our platform does not employ a reactive approach based solely on known malware signatures. Instead, it uses a proactive method that analyzes the behaviors and intentions of applications running on the device. This provides the capability to detect even the most dangerous, never-before-seen financial malware, known as "zero-day" threats, before they can execute a malicious action.
Detected Threat Types:
Overlay Attacks:Malicious applications place fake login or transaction confirmation screens over your legitimate banking app to steal credentials or approvals. Our system prevents such attempts by detecting abnormal permissions and behaviors, such as drawing over other apps.
Keyloggers and Screen Recorders:It recognizes software attempting to record user-entered passwords, card numbers, or sensitive on-screen data by identifying abnormal keyboard listening and screen capture attempts.
SMS Grabbers:It detects applications that secretly read SMS messages containing one-time passwords (OTPs) or transaction notifications and forward them to fraudsters, identified by their abuse of SMS permissions.
Remote Access Trojans (RATs): It detects software that allows a fraudster to take full control of the device (viewing the screen, making clicks, accessing files) by identifying suspicious services running in the background.
Importance for Fraud Detection:
Malware hidden on seemingly legitimate devices poses a high potential for fraud. Methods like device fingerprinting or passive biometric analysis alone are insufficient in such cases. In this scenario, the malware detection layer provides 360-degree protection by identifying "hidden threats" on the device.
Fraudsters constantly change their malware code to evade signature-based virus detection systems. Nevertheless, our behavioral analysis engine provides effective protection against such advanced evasion tactics by recognizing malicious actions (like screen drawing or SMS reading) even if the code changes.
1. Detection Scope
Provides comprehensive protection by combining device, behavioral, and malware analysis. It leaves no blind spots for complex attacks such as malware, social engineering, and zero-day threats.
Are limited to their area of expertise. These solutions can overlook Trojan attacks or malware originating from a legitimate device.
3. Artificial Intelligence
Uses Dynamic and Hybrid Intelligence, continuously fed by self-learning AI models, global threat intelligence, and human analyst insights.
Typically use isolated machine learning models that run on their own datasets; they lack global threat context and human intuition.
Why Fraud Malware Prevention?
Unlike point solutions on the market that focus on a single problem, Fraud Malware Prevention provides your institution with a holistic, integrated, and future-oriented defense strategy against fraud. This not only stops immediate threats but also improves the customer experience and increases operational efficiency.
2. Customer Experience
All analyses run seamlessly in the background without interrupting the user experience.
Often operate passively but can lead to a negative customer experience through false alarms or additional verification steps.
4. Ease of Integration
Offers fast and flexible integration within days via a lightweight SDK for mobile platforms and a single JavaScript code for websites.
Integration is often easy, but integrating multiple solutions can be complex and costly.
5. Future-Proofing
Is effective against zero-day threats and new fraud tactics thanks to its behavioral analysis and anomaly detection.
Their detection capabilities weaken as fraudsters discover and exploit the existing solution's blind spots.
