Risk Monitoring

Application fraud is a new-gen threat that is a great issue for both businesses and users. It can lead to revenue losses as well as damaged reputation. Generally, application fraud is associated with false or stolen credentials used by hackers to gain access to specific online services or products.

Secure benefits like loans, insurance, or online goods are the main target for criminals. Recognizing and combating this deception is crucial as a part of the application fraud prevention strategy.

Understanding the various forms of application fraud is key to effectively addressing the issue. In this article, we will discuss approaches used by hackers alongside effective ways to detect and prevent credit card application fraud and other types of fraudulent techniques.

Industries and Areas Commonly Targeted

Scammers engage in fraudulent activities for various reasons, with financial gain being a primary motivation. However, in cases like data breaches, they can be driven by other motives. By considering the industry and type of application fraud, one can speculate on the scammers' objectives.

Today, cyber-attacks extend to mobile devices, which often lack robust data protection measures. Without adequate safeguards such as anti-spyware or antivirus software, mobile devices become prime targets for scammers. Shockingly, a staggering 70% of scams now cater to mobile platforms, witnessing a 68% surge in scams that originally came from mobile applications.

Different industries are affected greatly by the issue. The most targeted niches include:

  • Fintech – application fraud isn't limited to banks and loan providers; insurance companies are also affected. In India, a staggering 96% of attacks result in financial losses. The demand for application fraud detection systems in Fintech has surged significantly. Hackers find account takeovers, identity thefts, and synthetic IDs particularly lucrative because financial data can be exploited or sold. Consequently, many scams target these vulnerabilities.
  • SaaS - over the past two years, there has been a significant increase in phishing attackers exploiting legitimate Software as a Service (SaaS) platforms to bypass SPAM filters. This trend spans various services like website builders, note-taking apps, and file-sharing platforms. Besides, hackers are employing multi-stage cloud phishing tactics to steal login credentials and establish fake accounts.
  • Online Vendors - from loan providers and traveling platforms to to iGaming, betting and dating service providers - user accounts appear to be another prime target for application fraud because of the increasing volume of card transactions. Scammers often exploit the trust associated with these platforms. Some common tactics employed against this industry include sending fraudulent messages posing online payment services, falsely claiming that the merchant's account has been suspended, redirecting clients to counterfeit checkout pages, and acquiring client purchase details to enhance the credibility of banking scams.

It may all lead to disastrous consequences. On the one hand, businesses lose revenues and clients while their reputation is damaged. On the other hand, users find their personal and account data compromised, which takes time to restore. Application fraud prevention systems may help companies enhance their safety means to safeguard clients and internal infrastructure.

Methods and Tools Used by Fraudsters to Commit Application Fraud

Scammers employ various methods to commit application fraud, with one prevalent tactic being the use of synthetic identities. Detecting this type of fraud poses a challenge, especially for businesses that accept online application submissions and require the submission of ID documents.

But how exactly do scammers gather personal information and execute application fraud?

Data Breach

Data breaches occur across businesses of various sizes, stemming from both intentional and accidental causes. Accidents may arise from insecure passwords set by employees or the negligent exposure of passwords. Deliberate breaches occur when hackers directly target an entity to access their database.

However, intentional breaches often involve malicious hackers targeting companies to infiltrate their databases. These fraudsters employ various technologies, including bots, to execute brute force attacks, attempting millions of password variations to gain unauthorized access.

When a data breach occurs, millions of data records can be compromised. Commonly stolen data involves an individual’s name, date of birth, account details, and contact info.

It is not only about data breaches. Sometimes it is about lost credentials that can later be found on illicit marketplaces on the dark net. Besides, scammers may also use brute force attacks or exploit weak passwords to gain unauthorized access and then sell, and trade these credentials.

Call Center Scams

Identity theft isn't solely confined to the realm of the internet; call centers are also becoming a popular avenue for criminals. Unfortunately, relying solely on voice identification isn't sufficient to verify someone's identity. This is why call centers can be extremely vulnerable to fraudsters.

Moreover, the absence of effective application fraud detection measures makes it easier for criminals to exploit this vulnerability to their advantage.

Email Interception

Scammers employ sophisticated tactics, such as leveraging USPS Informed Delivery when applying for credit cards. This service, provided by USPS, enables users to track mail and packages before they reach their destination.

By utilizing USPS Informed Delivery, scammers receive notifications about the delivery status of credit cards. This allows them to intercept the card before a legitimate user retrieves it.

Exploiting Cloud Infrastructure

Fraudsters exploit virtual platforms for identity theft and application fraud. Usually, they utilize the same cloud services that businesses rely on daily.

Within the cloud, fraudsters deploy automated scripts and bots to orchestrate large-scale fraudulent schemes. These bots systematically attempt various combinations of PINs and passwords to gain unauthorized access to accounts.

Impacts of Application Fraud on Businesses

Application fraud inflicts significant financial repercussions on businesses, with estimates suggesting that up to 10% of banks' unrecoverable bad debts stem from this form of fraud.

Fraud victims suffer from various monetary losses, including chargebacks where businesses bear the cost of fraudulent purchases along with associated fees like late payment charges. The inability to process legitimate applications due to fraud can also result in revenue loss. For instance, businesses fail to approve credit card applications, leading to missed sales opportunities.

Moreover, application fraud leads to hidden costs, notably damaging a business's reputation. The erosion of trust among customers can decrease sales and reduce customer loyalty.

Methods to Detect and Prevent Application Fraud

Detecting and preventing application fraud should come as a blend of innovative approaches. One method involves implementing robust authentication processes to analyze the information provided in applications, thereby minimizing the risk of deceitful submissions.

Additionally, leveraging advanced data analytics enables the detection of suspicious patterns indicative of fraudulent behavior, empowering businesses and individuals to intervene promptly. Educating employees and customers about common fraud tactics can also bolster prevention efforts. By combining these strategies, businesses can effectively safeguard themselves against the costly consequences of application fraud.

Manual Red Flag Checks

While automation proves invaluable in detecting application fraud, there are occasions where human intervention becomes essential for accurate assessment. This typically applies to edge cases or outliers—instances that automated models may overlook due to a lack of training data.

The manual review serves as a crucial component in any anti-fraud strategy, yet it is still quite time-consuming and resourceful. Ideally, manual intervention should be reserved for cases where automation falls short, ensuring efficient allocation of resources.

Automated Solutions

Automation is the key to effective application fraud prevention with advanced innovative technologies in mind:

  1. Harnessing ML and AI can be game-changing when combating application fraud. These technologies sift through vast datasets, pinpointing patterns and anomalies associated with fraudulent behavior. Notably, machine learning algorithms and AI models evolve with new data, enhancing their accuracy over time.
  2. Anchoring fraud prevention efforts with robust authentication techniques is essential. Organizations must implement reliable procedures to authenticate applicants' identities, ensuring the legitimacy of submitted docs and confirming the user’s true credentials.
  3. Furthermore, integrating biometric authentication methods like fingerprint scanning, facial recognition, and voice analysis enhances onboarding security measures.
  4. Device ID provides a unique identifier for each device, allowing businesses to track and analyze user behavior across multiple sessions and detect suspicious patterns. By leveraging device ID, companies can identify and block fraudulent activities more effectively, reducing the risk of fraud and enhancing security measures.

Ongoing Behavior Analytics

Behavioral analytics involves analyzing behavior patterns scientifically. In modern fraud detection and prevention solutions, behavioral analytics tools are integrated to monitor customer behaviors and identify sudden deviations, indicating potential fraud. By swiftly detecting unusual behavior, businesses can proactively mitigate security risks and prevent cyber threats before fraudulent transactions occur.

The primary objective of behavioral analytics is to detect and flag suspicious activities by detecting anomalies that differ from typical user behavior patterns. This process relies on technologies that analyze extensive data through continuous monitoring of user activities.

Monitored data points to various factors such as IP addresses, location details, device information, VPNs, proxies, system configurations, browser settings, payment methods, login times, transaction values, and typical purchasing behaviors. Subsequently, the following steps are typically involved in the behavioral analytics process.

Baseline behavior analysis:

  • The system analyzes historical data to establish usage behavior and habits for each customer.
  • This helps in understanding the typical behavior of individual users.

Real-time monitoring:

  • Behavioral analytics tools monitor user activity in real-time.
  • Suspicious activity is flagged once it has occurred.
  • Alerts are generated for behaviors like unfamiliar purchases or unusually large transactions.

ML-powered algorithms incorporation:

  • Machine learning algorithms enhance fraud detection analytics.
  • They adapt to changing usage patterns and analyze large datasets to identify suspicious activity accurately.

Risk scoring models:

  • Behavior monitoring systems create user profiles based on patterns.
  • Risk scores are assigned to each user, reflecting the risk of involvement in fraudulent activity.
  • Historical data, machine learning algorithms, and past anomalies help to generate risk scores.
  • Users with higher-than-average risk scores may require additional authentication measures.

Application fraud detection and prevention:

  • Real-time monitoring enables the detection and flagging of potentially fraudulent activity.
  • Modern behavioral analytics tools respond to threats by notifying users, blocking suspicious activity, and alerting law enforcement if necessary.

Traditional methods alone are insufficient to combat increasingly sophisticated fraudulent tactics. Advanced technologies, such as machine learning algorithms and behavioral analytics, offer the agility and precision necessary to detect fraud in real time, identifying risky anomalies and patterns.

JuicyScore’s Layered Approach to Reducing Application Fraud Risk

JuicyScore harnesses cutting-edge machine learning and artificial intelligence technologies to offer top-tier loyalty fraud prevention services. Our solution was designed to meet the unique needs of businesses, providing a comprehensive suite of advanced tools aimed at launching secure and revenue-focused strategies.

AI-Driven Fraud Detection:

  1. Our anti-fraud software leverages state-of-the-art advancements in machine learning and artificial intelligence.
  2. Sophisticated AI-driven algorithms ensure efficiency and reliability by considering various criteria for device authentication.
  3. Comprehensive end-user profiles are constructed from diverse technological data and behavioral attributes, ensuring robust fraud detection capabilities.
  4. ML-powered techniques enable real-time detection and mitigation of application fraud risks by analyzing technical data and user device settings.

Multi-parameter Approach to Fraud Prevention:

  1. The system integrates a consolidated data vector for streamlined automated analysis. It uses 65 000 data points to identify and prevent either emerging or already existing fraud patterns. With our API model, it is possible to create customized anti-fraud scoring models based on data vector containing 220 attributes.
  2. It efficiently manages large datasets and examines critical parameters of operating system structures, client accounts, and connectivity nuances of Android and iOS devices.
  3. Its primary objective is to enhance organizations' decision-making processes by providing comprehensive insights into fraudulent activities and network infrastructure.

Key Features

At JuicyScore, safeguarding user privacy is our top priority. Our anti-fraud solution focuses on processing and analyzing behavioral and device-related parameters. This streamlined approach empowers businesses to effortlessly identify potential risks and maintain the integrity of their business-oriented approaches.

Device Fingerprinting

JuicyScore examines primary and secondary attributes associated with potential fraudulent behavior. Our software monitors essential device-related data to achieve unparalleled precision in device fingerprinting.

A robust data vector analyzes vital elements such as RAM capacity, screen size, display quality, device type (tablet, desktop, laptop, or mobile), and other pertinent parameters to ensure accurate fingerprinting.

Behavior Patterns Tracking

We've developed an automated alert system that promptly notifies in-house teams of any abnormal or suspicious actions indicative of potential application fraud.

Simultaneously, teams are equipped with the necessary tools to identify abnormal actions like device cloning, randomization, remote access, and other routing strategies. Our software aids in swiftly detecting and preventing various potentially harmful or fraudulent behaviors by leveraging behavioral patterns.


What are some common early warning signs of application fraud to watch out for?

The main signs of application fraud generally include unusual behavioral patterns, inconsistent information, or applications that originate from unnatural and unique locations. Additionally, applicants may insist on faster approval, which can also be a red flag.

What tools and techniques can help automate fraud detection?

Basically, advanced fraud prevention tools rely on ML and AI technologies. They are the fundamental technologies for fraud prevention automation at all safety layers. With these technologies in the stack, businesses can detect and eliminate fraud risks on autopilot.

Can fraud prevention measures integrate with existing KYC processes?

Yes, they can. Companies can seamlessly integrate anti-fraud suites with their established KYC processes for automated screening, real-time monitoring, data enrichment, and overall risk-prevention model scalability.

Should businesses try to prevent all application fraud or just detect the most serious cases?

In a perfect world, we would like to see all types of fraud promptly detected and blocked. However, fraudulent techniques are evolving. This is why companies must keep in mind regulatory compliance and technology revision to keep them up-to-date.

Are there regulatory measures in place to address application fraud?

Yes, there are. These regulatory measures are delivered by AML and FCA regulations, consumer protection laws, PCI DSS (the golden standard for companies that handle transactions via credit cards), and more.