Skip to main content

Looking for Valuant? You are in the right place!

Valuant is now Abrigo, giving you a single source to Manage Risk and Drive Growth

Make yourself at home – we hope you enjoy your new web experience.

Looking for DiCOM? You are in the right place!

DiCOM Software is now part of Abrigo, giving you a single source to Manage Risk and Drive Growth. Make yourself at home – we hope you enjoy your new web experience.

What is check image analysis and how does it prevent fraud?

Terri Luttrell, CAMS-Audit, CFCS
July 9, 2024
Read Time: 0 min

Preventing check fraud with check image analysis

Check fraud continues to rise year after year despite most consumers' reduced check use. Check image analysis is a new tool for financial institutions to leverage in the battle against fraudsters. 

Check image analysis and check fraud

The rise in check fraud and new tools to detect it

The threat of check fraud looms more prominent than ever, posing significant risks to businesses and individuals alike. According to the Federal Trade Commission, nationwide fraud losses topped $10 billion in 2023. In February of 2023, the Financial Crimes Enforcement Network (FinCEN) issued an alert reiterating that fraud, including check fraud, is the largest source of illicit proceeds in the United States. In a recent survey conducted by the American Bankers Association, by the end of 2024, check fraud is projected to soar to a massive $24 billion, constituting 60% of all attempted fraud, despite fewer overall checks being written each year. 

In the ongoing battle to detect check fraud and prevent losses, financial institutions find themselves tasked with protecting their clients' assets while safeguarding their integrity. The rise in sophisticated fraud schemes necessitates innovative solutions, and one opportunity is the use of fraud detection software using check image analysis.  

What is check image analysis?

Check image analysis is using digital image processing methods and algorithms to analyze and extract information from images of checks. Instead of relying on manual data entry, which can be time-consuming and prone to errors, financial institutions use check image analysis to automate check processing and extract relevant information such as account numbers, check amounts, payee names, endorsement signatures, and other transaction details. This technique can be beneficial for fraud detection and prevention. 

With check image analysis, financial institutions can extract the attributes of a check, such as check stock, amount, and payee, to detect counterfeits, forgeries, and alterations. By using artificial intelligence and machine learning algorithms, check image analysis can evaluate all aspects of a check image with efficiency and accuracy, identifying anomalies that might be overlooked during manual investigations.  

How does check image analysis work?

Check image analysis detects check attributes like check stock, amount, and payee to detect forgeries, counterfeits, and alterations. It leverages visual check image comparison to decipher a check's authenticity and enable verification.

 

1. A check image is captured:

When a client deposits a check, either at a branch or ATM, by using remote deposit capture (RDC) or through a mobile banking app, the check is scanned to create a digital image of both the front and back. 

 

2. The check image is enhanced:

The captured images may undergo enhancement processes to improve clarity and readability. This process can involve adjusting brightness, contrast, and sharpness to ensure that the text and numbers on the check are clear and legible. 

 

3. Analysis of check image is performed:

Once the images are captured and enhanced, sophisticated software algorithms analyze them to extract meaningful information such as the check amount, account numbers, routing numbers, and the payer's signature. Optical Character Recognition (OCR) technology is used to interpret printed text on the check. OCR transforms an image from a bank check into machine-readable data.

 

4. Check data verified against the check image:

Afterwards, the extracted information is then verified against the data entered by the client during the deposit process. This methodology analyzes the received check image, verifies the routing and account numbers on the check, and ensures that these match what is on record at the financial institution. These steps help ensure accuracy and prevent errors.  

 

5. Fraud detection scenarios are ran against the check image:

Next the Advanced check image analysis systems include fraud detection mechanisms to identify potential signs of forgery, alteration, or counterfeit checks. Processes can involve comparing the check against a database of known fraudulent checks or using machine learning algorithms to detect suspicious patterns. 

 

6. The check image data is transmitted throughout the institution:

Once the check image has been processed and verified, the relevant information is transmitted to the bank or credit union's backend systems for further processing, such as crediting the client's account and initiating the clearing process with the payer's bank or credit union.  

 

Overall, check image analysis streamlines the check processing workflow, reduces manual labor, and improves accuracy and efficiency in handling check deposits. 

Check image analysis in fraud prevention

Check image analysis should be an integral part of any well-maintained check fraud detection and prevention program. Combined with a strong detection and fraud management system, it provides a powerful tool to reduce loss and protect customers from fraud. Check image analysis can help detect several types of check fraud.

 

Types of check fraud

There are three general types of check fraud: forgeries, counterfeit, and altered checks.  

  • Forgeries are checks where the drawer’s signature is forged or unauthorized.
  • Counterfeit checks are checks that have been created fraudulently to steal money.
  • Altered checks are checks that have been materially and purposefully altered to create fraud.

 

How check image analysis detects and prevents fraud

Check image analysis is a powerful tool for financial institutions to detect and prevent check fraud. From subtle alterations to the payee's name or signature to more obvious counterfeiting cases, check image analysis can flag suspicious transactions in real-time, allowing financial institutions to intervene swiftly and mitigate potential losses. Moreover, the ability to analyze substantial volumes of check images quickly enables proactive fraud prevention, reducing the likelihood of successful fraudulent attempts. 

For example, in a pilot program with a Southeastern U.S. bank, Abrigo Fraud Detection correctly identified 93% of the bank’s total fraudulent check value, equating to more than $330,000 in potential fraud loss avoidance. The cost of fraud can be significant not only in terms of monetary losses but also in damage to reputation and client trust. Investing in advanced check fraud detection software is not only a prudent decision but a strategic one. Financial institutions can stay ahead of the curve, safeguarding their interests and those of their valued clients.  

Benefits of check image analysis

As we mentioned earlier, the true costs of fraud are more than just the bottom line. However, there are many benefits gained by financial institutions that utilize check image analysis within their fraud detection software, including:   

  • Efficiency and accuracy: Check image analysis automates the process of reading and interpreting checks, reducing the need for manual data entry and increasing accuracy. This streamlines operations, leading to faster processing times and lower costs. 
  • Enhanced security and fraud detection: Check image analysis can help detect fraudulent activity by analyzing various features of the check, such as signatures, endorsement patterns, and watermarks. It can flag suspicious transactions for further investigation, enhancing security measures. 
  • Client experience: Faster processing times mean quicker access to funds for clients. By leveraging check image analysis, financial institutions can offer improved services and enhance the overall client experience, improving trust and satisfaction. 
  • Data analytics: Check image analysis generates valuable data that can be used for analytics purposes. Financial institutions can gain insights into client behavior, transaction patterns, and other trends, enabling them to make informed business decisions. 

Challenges and considerations of check image analysis

There are always considerations when adding a new tool or step to your approval process. While check image analysis offers numerous benefits, financial institutions also face several challenges and considerations when implementing this technology: 

  • Initial investment: Implementing check image analysis systems requires significant upfront investment in hardware, software, and training. Financial institutions need to assess the costs involved and ensure that the expected benefits justify the investment. A business case should include the cost savings from saved hard dollar fraud losses and client attrition.  
  • Integration with existing systems: Integrating check image analysis systems with existing banking software can be complex and time-consuming. Compatibility issues may arise, requiring modifications to existing systems or the development of custom interfaces. Selecting a vendor that understands the financial services industry, particularly financial crime, is imperative. 
  • Data security and privacy: Check images contain sensitive financial information, making data security and privacy paramount. Financial institutions must implement robust security measures to protect against unauthorized access, data breaches, and identity theft. The importance of security is familiar to financial institutions, and existing policies and procedures should be applied to any new software applications. 
  • Regulatory compliance: Financial institutions must comply with various regulatory requirements governing check processing, data handling, and client privacy, such as the Uniform Commercial Code (UCC), Check 21, and the Gramm-Leach-Bliley Act (GLBA). In addition, since fraud is now one of the FinCEN priorities, Anti-money laundering laws and regulations must be adhered to for all financial crimes.    
  • Client acceptance: Some clients may prefer traditional paper-based check processing methods and may be hesitant to adopt check image analysis technology. Financial institutions should educate clients about the benefits of image-based check processing and address any concerns or reservations they may have. Explaining that human-in-the-loop processes are still important when looking at checks within a certain gray area helps clients be fully ready to get on board. 
  • Training and change management: Implementing check image analysis requires training staff on the new technology and workflows. Financial institutions must develop comprehensive training programs to ensure that employees can effectively use the latest systems and processes. Additionally, managing organizational change and overcoming resistance to new technologies should be addressed with staff support. 
  • Volume and scalability: Financial institutions must ensure that their check image analysis systems can handle the volume of checks processed daily and scale effectively to accommodate future growth. Working with their fraud detection software vendor to understand how to reduce false positives is imperative. Performance issues or bottlenecks in processing can impact efficiency and client satisfaction. v

The importance of utilizing check image analysis to prevent fraud

Check image analysis is effective in tackling the rising threat of check fraud. Overall, the future of check image analysis for financial institutions will be characterized by continuous innovation and advancements in AI, machine learning, and related technologies, leading to more accurate, efficient, and secure check-processing workflows.

The benefits are clear: increased efficiency and accuracy streamline operations in addition to hard dollar loss prevention. Despite challenges such as initial investment and regulatory compliance, the future of check image analysis is increasingly valuable. Financial institutions that embrace these technologies can strengthen their defenses against fraud, safeguarding their integrity and protecting their clients' interests.

Abrigo Fraud Detection is stacked with all the tools you need to catch and resolve potentially fraudulent transactions quickly. Our innovative check fraud detection software combines AI/ML-driven check image analysis powered by Mitek, a national consortium of check fraud information, and Abrigo's configurable decision engine.

About the Author

Terri Luttrell, CAMS-Audit, CFCS

Compliance and Engagement Director
Terri Luttrell is a seasoned AML professional and former director and AML/OFAC officer with over 20 years in the banking industry, working both in medium and large community and commercial banks ranging from $2 billion to $330 billion in asset size.

Full Bio

About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

Make Big Things Happen.