Topics Types of Bank Fraud 12 Most Common Types of Bank Fraud Account Takeover (ATO) Fraud Advance Fee Fraud Check Fraud ACH Fraud Real-time Payment Fraud First-Party Fraud Wire Fraud Zelle Fraud Types of Card Fraud Credit Card Fraud Debit Card Fraud Lost or Stolen Card Fraud Card Skimming Card Cloning Chargeback Fraud Card Not Present (CNP) Fraud Fraud Defenses Anti-Money Laundering (AML) Behavioral Biometrics Crowdsourced Abuse Reporting Device Fingerprinting Real-time monitoring Email Reputation Service IP Reputation Service SR 11-7 Compliance Supervised Machine Learning Suspicious Activity Reports (SARs) Tokenization Transaction Monitoring Two-Factor Authentication (2FA) Unsupervised Machine Learning Fraud Tactics Bot Attacks Call Center Scams Credential Stuffing Data Breaches Deepfakes Device Emulators GPS Spoofing Money Mule Scams P2P VPN Networks Phishing Attacks SIM Swap Fraud URL Shortener Spam Web Scraping Fraud Tech Anomaly Detection Device Intelligence Feature Engineering Generative AI Identity (ID) Graphing Network Analysis Natural Language Processing Fraud Types Application Fraud Transaction Fraud Payment Fraud Pump and Dump Scams Bust-Out Fraud Buyer-Seller Collusion Content Abuse Cryptocurrency Investment Scams Money Laundering Loan Stacking Romance Scams Rug Pull Scams SIM Swapping Synthetic Identity Theft Cryptocurrency Scams Pig Butchering Scams Loan Stacking What is Loan Stacking? Loan stacking refers to the practice of getting approval for multiple loans or lines of credit simultaneously within a short period. Loan stacking generally happens online and can be done by either individuals or businesses. It is not illegal to “stack” loans, but financial institutions lose billions of dollars every year to the process because many loan stackers commit application fraud – intentionally default on the loans they take out. There are three types of loan stacking: credit shopping, credit stacking, and fraud stacking. The first two, while problematic for financial institutions, are nonetheless legal. Credit shopping is where borrowers apply for multiple loans to get the best interest rate. Credit stacking is where legitimate buyers apply for credit without realistically having the means to repay. The third type is fraud stacking, in which fraudsters apply for multiple loans with no intention of paying them back. What Should Financial Institutions Know About Loan Stacking? The growing availability of instant credit approval from financial institutions has allowed consumers and fraudsters alike numerous opportunities for loan stacking. Financial institutions are losing billions of dollars every year because of loan stacking by fraudsters and legitimate borrowers. Large, organized crime rings often orchestrate loan stacking schemes that aim for huge payouts from banks. Fraudsters use sophisticated, involved strategies like identity theft and bust-out fraud to achieve maximum profit from loan stacking. For example, a fraud ring might create identities using stolen social security numbers and personal information obtained from phishing schemes. The fraud ring would apply for hundreds of loans distributed among multiple banks using the stolen identities. Once the loans are approved, the fraudsters gradually incubate each account- emulate legitimate user behavior, make the payments on time, and then suddenly “bust out” maxing out all of the accounts. The fraudsters disappear, and the lenders incur significant losses due to the defaults. Five individuals were recently arrested for allegedly attempting to steal more than one million dollars from five major credit unions. The fraud ring spent a year gradually filing more than one hundred loan requests electronically using stolen names and social security numbers. The fraud ring managed to steal more than $200,000 with this fraud stacking scheme. Financial institutions need a solution that detects fraudulent loan and credit card applications before they reach the collections stage. DataVisor Detects Loan Stacking Application-level detection is critical for achieving genuinely proactive fraud management. Whereas most legacy systems still in use today operate solely at the transaction level—and are inherently reactive by nature—advanced, AI-powered solutions such as DataVisor’s dCube incorporate application-level analysis as well. This enables systems to flag suspicious activity before attacks launch and damage is caused. In the case of loan stacking, a reactive, transaction-level approach cannot identify fraud or instigate action until after malicious activity takes place. dCube, on the other hand, can correlate patterns and surface cross-account links that indicate coordinated application activity while an attack is still being planned, or early enough in the process that no damage is caused. This is particularly important for instances such as the attempted credit union theft, in which fraudulent accounts incubated for extensive periods of time before being put to use. Coordinated management of incubating malicious accounts can only be detected by solutions such as dCube; solutions that make holistic analysis and contextual detection possible, and which leverage the power of unsupervised machine learning to flag suspicious accounts early enough to prevent downstream damage. Additional References Solution: Financial Services Solution: Application Fraud Source: How fraudsters are gaming online lenders, American Banker Source: Massive loan fraud ring busted: Hundreds of victims targeted, ABC, Inc., WLS-TV Chicago Source: $8B In Bad Credit Card Debt Write-Offs Worry US Banks, PYMNTS.com Source: Big Four U.S. Banks Hit With $12.5B In Credit Card Losses, PYMNTS.com