How Much Can Application Fraud Cost Banks? And a Bonus Downloadable ROI Calculator!

Application fraud is a growing problem for banking institutions, costing them billions in unrecoverable funds. A KPMG report stated over half of banking institutions report they recover less than one-quarter of their fraud losses each year. That makes it imperative for companies like DataVisor to help mitigate banking application fraud. 

In this article, you will learn how to mitigate the risk of banking application fraud and we will introduce our new downloadable banking fraud ROI calculator.

Understanding Bank Application Fraud

Globally, the banking landscape has changed. Face-to-face banking has been replaced by the demand for digital payments and transactions. As these online processes expand, so does the threat vector for all kinds of fraud, abuse, and outright theft. KPMG surveyed global banking institutions in 2018 and found that 61% report the instances of fraud are increasing, particularly in the area of application abuse.

Banking application fraud occurs when fraudsters use stolen information to build an authentic looking credit or account history, then use this data to establish new accounts or gain access to higher lines of credit. When it comes time to pay these bills, the fraudster simply disappears, leaving the bank holding their debt. 

Some of the methods for bank application fraud include:

• Call center attacks are where a majority of bank application fraud occurs. Customers can apply for credit online but also by phone. Call center workers cannot determine whether the data shared by customers is fake, and traditional fraud detection is no help.
• Data breaches cost companies more than six million data records every day around the globe. Bank application fraud occurs when digital criminals use stolen personal data to create fake identities to apply for loans or impersonate existing customers and take over accounts. 
• Intercepted mail occurs when fraudsters apply for credit card accounts under a stolen identity and then steal the card directly from the mailbox by using informed delivery, which alerts them to when the card will arrive.
• Senior scams target an older person’s vulnerabilities by tricking them into supplying their personal data, which is then used to apply for credit. 

As the level of sophistication for banking application fraud increases, how can financial institutions mitigate their risk by using advanced technologies? DataVisor has the answer.

Calculating the ROI of Fraud Detection

Using fraud detection as a path to increase revenue is a smart growth strategy. By reducing losses associated with fraud, you’re automatically adding to your bottom line. 

To calculate the ROI of fraud detection, banks should compare the number of applications they receive each year to the percentage of approved applications that are typically fraudulent. From there, you can factor in the average value of each fraudulent application to find your total fraud loss. 

Banks should also take into account the time and resources spent on manual application review, as well as the long-term financial impact that fraudulent false positives have on good customers. 

We’ve combined all of these functions into our new Application Fraud ROI Calculator, which you can download for free. Use our tool to input your own figures to calculate your fraud detection ROI for your business.

Fighting Banking Application Fraud with Unsupervised Machine Learning

Traditional fraud detection simply cannot keep pace with the frequency and sophistication of today’s bank application fraud attacks. The answer to stopping this malfeasance is Unsupervised Machine Learning (UML). 

Traditional rule-setting fraud security requires an understanding of the techniques used by cybercriminals. UML detects new fraud attacks with no historical labels, large datasets, or extensive training. This intelligent technology helps financial organizations adapt quickly to new and emerging fraud, without the traditional six-month window necessary to update rules and SML models. The cost savings of using UML tools are staggering; one leading U.S. credit card issuer saved $15 million in the first year alone.

To determine how your organization can lessen the financial impact of bank application fraud, download our free ROI calculator. The tool calculates fraud loss savings when leveraging tools like dCube. 

Banking application fraud doesn’t have to be a growing problem at your institution. The technology exists to meet fraudsters head on and defeat them at their own game. Talk to our team today to find out how we can help. Download our Banking Application Fraud ROI Calculator to calculate how much this type of fraud can cost you.