What Is Application Fraud? And How Do You Detect It?

Overview

Application fraud is a form of deception where individuals misrepresent information or illegitimately use someone else's personal information (PII) to obtain various benefits, typically financial, such as opening bank accounts or applying for credit cards. This malicious activity has become increasingly prevalent in today's digital age, as fraudsters exploit advances in technology and data availability to carry out their schemes. Application fraud can take multiple forms, including third-party fraud, where stolen or compromised credentials are used, first-party fraud, where an individual intentionally provides false information (such as inflating income) to obtain financial products, and mule fraud, where accounts are opened to move illicit funds.

Techniques such as falsifying information or using virtual machines to mask IP addresses have made it even harder for organizations to detect fraud. While traditional methods of identity theft often involved physical crimes, such as stealing a wallet or tampering with mail, modern application fraud is largely carried out online. However, application fraud isn’t limited to stolen or compromised credentials. First-party fraud occurs when an applicant deliberately provides misleading information, opens an account without intent to repay, or engages in financial crime. Fraudsters may also set up mule accounts to launder illicit funds. They even target specific industries, committing acts like insurance application fraud, where false or misleading information is submitted to obtain unauthorized insurance payouts or coverage.
 

Consequently, application fraud presents significant challenges for financial institutions and businesses that must safeguard both their operations and customer data. These challenges extend to maintaining compliance with AML and KYC regulations to prevent reputational damage and far-reaching consequences for their organizations. Effective management of this threat requires a robust combination of advanced data analytics, identity verification technologies, such as biometrics, and vigilant monitoring processes.

The Importance of Data to Detect and Tackle Application Fraud

The Data Proliferation Problem

The widespread availability of data and technology can help organizations identify fraud and verify identities, enhancing financial security. However, these same tools also enable fraudsters to conduct sophisticated scams like bust-out fraud, which involves building up credit before intentionally defaulting, or using stolen PII to bypass controls. Organizations face the ongoing challenge of balancing protective technology use against its potential misuse in combating application fraud. 

• Organizations now have access to more data than ever —unfortunately, the same can be said for fraudsters.
• Decisions can be better informed due to the availability of data, but these decisions can be more difficult to make for the same reasons.
• Data is an asset but managing it is a huge responsibility and a regulatory burden. The costs of data mismanagement are extremely high.
• Technology makes the lives of customers simpler, but it also makes it easier to steal data. The days of paper-based and in-person fraud are slowly coming to an end.

When preventing application fraud, a data strategy is vital. Not only do you need data to figure out that an application is legitimate, but it’s also the point where key data is collected about your customer. A robust data strategy involves meticulous gathering and organization of diverse data sources, enabling accurate assessments of applicant legitimacy. By harnessing data from various checkpoints, including behavioral analysis and metadata, businesses can gain comprehensive insights into an applicant's identity and intentions. This integrated approach helps identify anomalies or inconsistencies that might indicate fraudulent activity. Additionally, having a sophisticated data infrastructure supports seamless compliance with regulatory requirements, such as AML and KYC, while protecting sensitive customer information. Overall, a well-considered data strategy is indispensable not only for fraud prevention but also for fostering trust and credibility in customer relationships.

Criminals exploit a range of tactics to commit application fraud, from using stolen or fabricated identities—made easier by constant data breaches and readily available personal data on the dark web—to misrepresenting financial details or opening mule accounts to launder illicit funds.This pits providers in an ever-ascending arms race with fraudsters to ensure it’s prohibitively difficult to use a stolen or synthetic identity to commit fraud. Leveraging tools like artificial intelligence (AI) to detect anomalies in applicant behavior, such as mismatched IP addresses or application attributes that may indicate synthetic identities, is critical.

Expansion of Criminal Methods in Application Fraud 

Today’s fraudsters are more sophisticated and more organized than ever. They often mirror similar strategies of the organizations they target, using tools like virtual machines to evade detection, leading to significant financial losses for businesses and individuals alike.

As organizations tackle one area, fraudsters shift their attention to another, employing advanced methods like biometrics spoofing or exploiting weaknesses in onboarding processes. They can harness existing technology and even develop new technology to stay ahead.

Organizations now have to assume that fraudsters could be as capable as their anti-fraud team. They have the ability to commit loan application fraud en masse to overwhelm existing systems, hoping some fraudulent applications will bypass security protocols. To counteract these threats, organizations must adopt an agile approach that involves constant updates to their security measures, utilizing an array of sophisticated technologies. Employing machine learning and artificial intelligence can help identify patterns and behaviors indicative of fraud, enabling businesses to adjust their defenses in real time. Additionally, fostering a collaborative environment where financial institutions share insights into fraud trends can significantly enhance the overall resilience of the industry. Ultimately, this proactive stance not only deters fraudsters but also reassures customers of their safety, maintaining trust and satisfaction.

Changing Customer Expectations 

Today’s consumers expect to have ample choice when selecting the products and services they want to purchase. 
While consumers value speed and convenience in online applications, they are also willing to accept some friction—especially for high-value products like home loans—when it enhances security and protects them from fraud.Financial institutions must constantly balance their duty to protect customers, themselves, and society at large from fraud, while maintaining high customer satisfaction levels. This is a delicate balance and moving too far in either direction can have dire consequences. 

To provide a clearer understanding of consumer attitudes toward the impact that fraud and security checks have on them during the application process, FICO commissioned an independent survey across 14 countries. In every country, almost all respondents were prepared to open at least one type of financial account online. 

We asked respondents what they would do if during an online application process the provider asked them to carry out a task offline — for example, to take a telephone call, post some documents, or visit a branch to complete the application. 

The survey results show that when people start an application online, most expect to complete it online. If they can't, a significant number will abandon the application, and may even switch to a competitor.

The number of people who are willing to spend more than three hours completing an application is low — most expect to finish an application in less than 30 minutes. 

Using Data as a Defense Against Application Fraud

Data can now be used as a tool to combat application fraud. 

There are three main methods of data implementation that can drastically minimize the risk of application fraud. 

Data Ingestion

Data ingestion involves examining various data sources and understanding the ways in which they interlink, forming a comprehensive view of the information ecosystem. Organizations collect data from multiple channels, such as direct inputs from application forms, metadata from user interactions, or third-party sources like credit reference agencies. Each data piece acts as a puzzle part, contributing to a larger picture that helps in detecting and mitigating application fraud. The challenge lies in ensuring seamless integration of these diverse data forms, preventing informational silos that may hinder fraud detection efforts. Successfully managing data ingestion not only involves careful selection and integration of data but also requires a dynamic process that allows for the incorporation of new, reliable data sources while excluding redundant or questionable ones. This approach ensures that the data used in decision-making is accurate and up-to-date, enabling organizations to identify fraudulent activities with greater precision while maintaining operational efficiency.

Data can come from many sources, including:

• Data from the application itself (either supplied or metadata)
• Purchased data from third parties, for example, a credit reference agency
• Data from identity solutions such as FICO® Identity Resolution Engine
• Information from previous interactions with the applicant
• Information from records of other accountholders, e.g., common data points such as an address, telephone number, or email addresses

This abundance of data can be a blessing and a curse. It gives you plenty of information to base fraud decisions on, but any single data element can disproportionately skew the final results. This causes organizations to struggle to correctly analyze all the data they have access to. 

Informational siloes mean that data sharing across products, channels, or consumer lifecycle stages can trigger scenarios where, for example, they open an account for a customer whose identity has already been found to be fraudulent by another part of the business. 

These issues can be particularly challenging for organizations that don’t have an agile process of adding new sources of data or removing sources that are questionable or are of limited value.

To make your fraud detection accurate so that more fraud is spotted without increasing false positives, it is necessary to utilize data that not only indicates negative behavior but also signifies what good behavior looks like. 

This can be extremely difficult to get right. There is often a fine line between the two. Sources of data from both internal sources and shared consortia data, such as that from credit reference agencies, provide good references for known bad actors. 

At the same time, they are unlikely to provide examples of what good or normal behavior looks like.

Data Enrichment

Gathering and organizing data for fraud detection is crucial. Standardizing structured data simplifies integration and analysis, while natural language processing helps extract insights from unstructured data. Adding contextual information helps identify behavior patterns signaling fraud. Using exemplars highlights significant elements. Well-organized data enhances fraud detection accuracy, reducing false positives and improving customer experience.

• Making sure there is commonality across sources for structured data — and using the same structure across all data
• Extracting “sense” from unstructured data, for example, by using natural language processing to identify data elements that are of value to decision making
• Adding context to data, using exemplars to identify data that is indicative of a certain type of behavior, such as fraud or non-fraud

Analyzing Data

Algorithms and models need to be applied to your data to deliver actionable insights. By leveraging sophisticated algorithms and machine learning models, organizations can sift through vast amounts of information to detect anomalies that may indicate fraudulent activities. These models are designed to adapt and learn from new data, ensuring they remain effective even as fraudsters evolve their tactics. Implementing a variety of models allows for a comprehensive approach to fraud detection, with each model tailored to recognize specific patterns or behaviors. For instance, predictive analytics can forecast potential fraud scenarios by examining historical data trends, while real-time analytics swiftly identifies suspicious transactions as they occur. The integration of advanced algorithms not only enhances the accuracy and speed of identifying fraud but also minimizes false positives, thus maintaining a seamless experience for legitimate customers.

The type of models required is dependent on the scenarios faced, and in almost every case, a layered approach will provide optimum results. This methodology involves deploying a combination of models, each tailored to address specific elements of the fraud detection process. For instance, while rule-based models can be effective for identifying known patterns of fraud that have been encountered previously, machine learning models excel at detecting anomalies in real time by adapting to new patterns of fraudulent activity. Additionally, anomaly detection models are crucial for spotting unusual behaviors that deviate from established norms, offering insights that might be missed by other methods. This layered strategy not only enhances the overall accuracy of fraud detection but also helps minimize false positives, ensuring that legitimate customer transactions are processed swiftly and efficiently. By continuously updating and refining these models, organizations can effectively respond to the ever-evolving tactics of fraudsters, maintaining a robust defense against application fraud.

The quality of the decisions available from the data you have is determined by the quality and variety of models available to you. The same models do not provide the same level of accuracy in every scenario. For example, if you are facing a new type of application fraud, or an attack on a new channel, models that have been trained to determine fraud through comparison to past attacks will not recognize them. In these scenarios, you need models that are self-learning and can determine outlier behavior that looks suspicious. If you are seeing instances of fraud that have previously been experienced, then models that have been trained using tagged data are valuable.

Annoy Fraudsters, Delight Your Customers

In today's digital world, application fraud is a growing threat requiring proactive solutions. Fraudsters consistently adapt to evade traditional security, making it essential for companies to use innovative methods like AI and machine learning. FICO develops systems to detect fraud early, analyzing behavioral patterns in real time while improving legitimate customer experiences. By blending preventative technology with user-friendly verification, businesses can protect against fraud and maintain customer satisfaction. Organizations must continually update their security strategies to combat cybercriminals' evolving tactics.

We take a layered approach to tackling application fraud with solutions including:

• Application fraud detection — machine learning algorithms use behavioral analytics in real time to define the likelihood of an application being fraudulent.
• Identity resolution — advanced fuzzy matching technology works in real time to uncover the links between applications and accounts. Variations on the same name, email address, or home address used in multiple apparently unconnected applications is indicative of criminal activity.
• Identity proofing — there are complimentary 3rd party solutions where applicants can use their mobile phone “selfie” technology and their identity documents to prove identity documents are valid and belong to them. 

As our solutions work in real time, they facilitate fast account opening, and our advanced AI and machine learning approach ensures that false positive rates are kept to a minimum — all enhancing the experience for legitimate customers.