COVID-19 has brought varying degrees of uncertainty and financial hardship to businesses and individuals alike. While many are struggling with losses of income and livelihood, cybercriminals are ramping up efforts to line their own pockets. The Small Business Administration (SBA) recently announced that a glitch in their online application process may have exposed more than 8,000 personal records from companies that used the portal to seek financial relief. The ones affected by the breach are small business owners who were applying for loans as part of the Economic Injury Disaster Program. And even though the SBA immediately disabled the affected portion of the website and addressed the issue, it’s uncertain as to how the data may be or has already been misused. Are you ready to assess your current fraud prevention readiness? Download this ebook now! Exposing the Flaws of Online Lending Consumers are increasingly jumping on board with completing loan applications online. They receive faster approvals and can complete applications outside of normal banking hours. But as fewer financial transactions occur face to face in favor of online convenience, a myriad of potential privacy and identity issues emerge. For starters, there’s the risk of using real identities and personal data to commit fraudulent acts. The recent SBA incident creates the very real worry that hackers could use the exposed records with a business owner’s real information to apply for additional loans with other banks. Because these credentials are authentic, they may have an easier time bypassing Know Your Customer (KYC) processes or other screening requirements. There’s also the risk of loan stacking, in which fraudsters apply for several loans in a short period of time with no intention of repaying them. They take advantage of rapid response times and the delays in updating credit files to raise quick cash. In terms of the stimulus relief bill for small businesses, those who have had their records exposed may find that fraudsters have beaten them to the punch when applying for various forms of aid. This means that well-intentioned applications may be denied if it looks as though they’ve already applied for those funds, thus preventing businesses from receiving much-needed (and much-deserved) relief. How Loan Application Fraud Detection Software Can Mitigate These Risks Crime never takes a holiday, and given that fraudsters don’t discriminate when choosing their victims, it’s unsurprising that they may try to capitalize on the generous loans offered by the SBA to help businesses and their employees during a critical time of need. Loan application fraud detection software can help to reduce the risk to business owners who applied for the SBA loan (or any other type of loan) by using automation at scale to scan records and report anomalies. Reviewing loan applications for signs of fraud requires a proactive approach that can detect issues as they occur, rather than conduct reactive reviews that could have severe consequences to your organization and customers. DataVisor uses unsupervised machine learning that doesn’t require intense training to be able to detect various forms of loan application fraud at scale in real-time. See DataVisor’s loan application fraud detection software in action by requesting a free demo today. Combating Fraud with Machine Learning DataVisor leverages unsupervised machine learning (UML) that doesn’t require rigorous data training and can review larger data sets at scale to better identify fraud. This gives financial organizations a chance to act on fraud in real-time, resulting in fewer false positives so that banks and merchants can provide a better experience to their customers. To learn more about UML and machine learning fraud detection, click here to schedule a free demo. View posts by tags: Application Fraud Machine Learning Related Content: Digital Fraud Trends How is the Financial Fraud Landscape Changing as the World Adapts to COVID-19? Digital Fraud Trends Responding to COVID-19 Product Blogs Can Machine Learning Combat Fraud in the Insurance Industry? about Claire Zhou Claire is a Senior Product Marketing Manager at DataVisor with over 5 years of marketing experience in security and fin-tech. She is passionate about empowering enterprise customers with AI-based solutions. Her expertise spans data analytics, cybersecurity, and fraud prevention. Claire has an MBA from UCLA. about Claire Zhou Claire is a Senior Product Marketing Manager at DataVisor with over 5 years of marketing experience in security and fin-tech. She is passionate about empowering enterprise customers with AI-based solutions. Her expertise spans data analytics, cybersecurity, and fraud prevention. Claire has an MBA from UCLA. View posts by tags: Application Fraud Machine Learning Related Content: Digital Fraud Trends How is the Financial Fraud Landscape Changing as the World Adapts to COVID-19? Digital Fraud Trends Responding to COVID-19 Product Blogs Can Machine Learning Combat Fraud in the Insurance Industry?