Imagine this scenario: You're working as an onboarding agent at a bank or lending institution, and you come across a picture of a pay stub that seems just a little bit… off. You can’t put your finger on why—the contact details are correct, the margins are aligned, the font is consistent, there are no obvious spelling errors, and all the line items add up as expected, but something about this file is whispering “fraud” in your ear. What do you do?
For many reviewers, this is an everyday occurrence as more applicants submit image versions or screenshots of common documents like paystubs, bank statements, and W-2s, as part of the digital onboarding process. Unfortunately, detecting image fraud is a complex and difficult task, further complicated by the fact that most traditional fraud detection techniques and digital fraud solutions are not designed to work on image files like JPEG, PNG, and IMG.
In this post, we explore the world of image fraud and highlight the tools, techniques, and best practices that can help banks and financial institutions protect themselves from this growing risk.
Key Takeaways
- Image fraud is a growing threat in the financial sector, as driven by the advent of generative AI and improved access to editing software programs.
- Image fraud, like other forms of document fraud, is a gateway to more serious financial crimes and identity theft, often resulting in monetary losses to financial institutions, businesses, and individuals.
- Tools such as AI-powered document fraud detection, computer vision algorithms, metadata analysis software, and multimedia authentication techniques aid in the quick and accurate detection and prevention of image fraud.
What is image fraud?
Image fraud refers to the act of manipulating, altering, or forging photos or images in order to achieve a desired result. Within the banking or financial services context, image fraud involves the use of edited image files or the creation of counterfeit materials, such as bank statements, pay stubs, or identity documentation, for malicious purposes, such as securing a loan or line of credit that the applicant otherwise would not qualify for.
How is image fraud different from document fraud?
Image fraud is a form of document fraud that involves the use of image files, such as JPEG, PNG, and IMG. Unfortunately, image fraud is often even more difficult to detect than traditional document fraud because many digital fraud detection solutions are not compatible with image files.
The Rise of Synthetic Image Fraud in the Financial Sector
Image fraud has been a risk for as long as Photoshop has existed. For decades, fraudsters have used traditional image processing techniques and software solutions to create, alter, or manipulate digital images to deceive reviewers.
In recent years, fraudsters added a new tool to their arsenal: artificial intelligence. Using these online tools, as well as traditional software programs, fraudsters are now able to produce incredibly realistic counterfeit images that are often indistinguishable from authentic images to the naked eye.
This has led to the emergence of a new sub-category of fraud: synthetic image fraud.
What is image synthetic image fraud?
Synthetic image fraud is when people use AI-enabled tools to create images, as well as traditional manipulation techniques, such as image retouching, splicing, and copy-move attacks, to forge or edit financial documents.
Tools and Techniques for Detecting Image Fraud
The continuous evolution of image manipulation techniques underscores the importance of developing and implementing effective tools to detect and prevent image fraud. Because no single solution or method provides complete protection, organizations should employ a variety of tools and techniques in the fight against image fraud.
In the following sections, we will explore several methods for detecting image fraud, including computer vision algorithms, metadata analysis, and automated fraud detection software. By implementing these tools and techniques, as well as educating and raising awareness around the potential risks of image fraud, financial institutions can better protect themselves from the damaging consequences of financial fraud.
Computer vision algorithms and digital solutions
Computer vision algorithms are powerful tools used to analyze images for inconsistencies and detect signs of manipulation that may not be easily noticeable to the human eye. Some of these techniques include:
- Double-quantization effects: The double-quantization effect is an algorithm designed to identify quantization artifacts that arise when JPEG compression is applied multiple times. If this effect is identified in an image, it often means that the image has been altered or stored by a graphic editor at least once.
- Error level analysis: Error level analysis detects foreign objects injected into the original image by analyzing the quantization tables of pixel blocks throughout the image.
- Digital watermarking: Digital watermarking is a commonly used technique that embeds a unique, invisible mark into an image. This mark can be used to verify the source and authenticity of the image, often withstanding manipulation attempts.
- Digital signature: A digital signature uses cryptographic algorithms to create a unique signature for an image. If the image is altered in any way, the signature will change, indicating that the image may not be authentic.
While computer vision algorithms and other digital techniques are instrumental in detecting image fraud, they are not infallible, as subtle differences in the image may still go undetected. Nevertheless, these algorithms play a crucial role in identifying manipulated images and protecting the integrity of digital information.
Metadata analysis
Metadata analysis is another valuable technique for detecting image fraud. By examining the exchangeable image file (EXIF) information—or the metadata associated with an image, such as the date and time it was taken, the camera utilized, and the location—analysts can identify potential alterations that are indicative of image manipulation.
However, as with computer vision techniques, metadata analysis is also not foolproof because metadata itself can be manipulated or removed entirely. As a result, metadata analysis should be used in conjunction with other detection methods, such as computer vision algorithms and automated fraud detection software, to ensure a comprehensive and reliable assessment of image authenticity.
Automated fraud detection software
Automated fraud detection software is a powerful tool that leverages machine learning algorithms to detect anomalies in images and provide lenders with comprehensive visualizations and data regarding the validity of each image. By comparing the original image to a database of known images and detecting discrepancies, automated fraud detection software can rapidly and accurately identify falsified images.
In addition to detecting image fraud, automated fraud detection software can also help businesses:
- Save time and money by reducing the cost of manual fraud detection processes
- Monitor transactions and events in real-time
- Implement regular software updates
- Employ multiple security layers
- Train staff on the software’s use and current fraud detection techniques
By implementing these measures, businesses can enhance their fraud detection capabilities and safeguard their financial assets and reputation.
Prevention Measures and Best Practices
As screenshots and photo submissions become more common during the onboarding or application processes, businesses must adapt the customer experience to support this need and implement anti-fraud measures to detect fraudulent activity.
In this section, we will explore best practices to help companies incorporate an image fraud element within the broader fraud strategy and toolset:
1. Drive education and awareness among employees.
Increasing awareness among employees of the growing role of image fraud and its consequences is the first step towards prevention.
Education and awareness initiatives can inform individuals about the methods and strategies used by fraudsters, simple ways to recognize and detect fraudulent images and what to do if they suspect an image has been manipulated. By understanding the potential ramifications of image fraud and the review process the company has put in place, employees will have a clear sense of the actions they should take when they encounter a suspicious image.
2. Leverage a combination of digital authentication techniques.
As discussed above, there are several high-tech solutions that companies can employ to verify the authenticity of an image and identify files that have been manipulated. While these techniques are far superior to manual reviews and other traditional techniques, no single solution provides complete and total protection. As with any fraud strategy, it is wise to employ a variety of solutions to minimize gaps in coverage and maximize protection.
3. Promote ethical and responsible financial behavior.
For many fraudsters, digital crimes–especially when committed against large banks and financial institutions–may seem like a victimless crime. But the implications of these actions are far and wide, as the need to offset losses can translate into higher rates and fees for all customers and the inability to serve a wide customer base. Educating customers on the consequences of fraud (both to themselves and to the ecosystem as a whole) is one way to potentially reduce instances of fraud.
In tandem, financial institutions can offer helpful resources to promote responsible financial behavior and ways to improve their financial standing. This might include courses on credit building, reducing debt, calculating a manageable level of debt or planning for future financial needs, such as buying a home or saving for college.
Finally, by taking a firm stand on fraud publicly, it is often possible to signal to fraudsters that the company is well prepared to identify cases of fraud and pursue legal action, prompting many to think twice about targeting the organization.
Combatting image fraud with Inscribe
The rise of image fraud poses a significant threat to the financial sector. However, by employing a combination of detection tools and preventative measures, the industry can effectively combat this threat.
Inscribe is a risk intelligence platform that helps companies reduce the risk of fraud through automated document reviews. Our AI-enabled solutions can quickly and accurately detect signs of fraud in documents and images, enabling risk ops teams to make good decisions about which customers to accept.
The fight against image fraud is a continuous one, but with vigilance, innovation, and the right tools and practices, we can stay one step ahead of the fraudsters.
Ready to learn more about how Inscribe can help your organization automate document reviews of all kinds? Reach out today to schedule a personalized demo and learn more about our full range of services.
