The trend of online lending continues to grow. Fundera research shows that 49% of small businesses applied for a loan last year, and of those, 32% turned to online lenders. That’s up from 19% just three years prior. Online and mobile loan applications are now available at 91% of financial institutions, indicating that even traditional banks believe in the value an online lending approach provides. As a whole, the digital lending industry is expected to reach $587.27 billion by 2026. Clearly, digital lending has a bright future, but lenders must start updating and preparing their fraud prevention approach now. This up-and-coming channel is new territory for many financial institutions, and fraudsters are eager to exploit a new frontier for their own personal gain. The Most Common Fraud Risks in Digital Lending All lenders face some form of risk related to fraud, for example: Mortgage loans are susceptible to loan slamming or loan shotgunning,All forms of credit are vulnerable to submissions of false proofs of income, undisclosed debt, and some measure of identity theft fraud. Credit card issuers are particularly exposed to loan staking, and first-party fraud. These risks increase substantially when financial institutions migrate to online credit applications because these new channels lack the personal interaction between borrowers and lenders’ representatives that have traditionally safeguarded the latter against fraud. With in-person loans, lenders have the advantage of seeing a borrower in person, asking for identification, and watching their non-verbal behavior; however, these luxuries do not translate to digital lending, where identity verification can be much more challenging. Simple online applications, such as payday loans, credit cards, and personal loans only require a few pieces of personal information. This alone makes it easy to commit application fraud. If a thief obtains sensitive data like a social security number, it is very easy to submit a false application and create devastating results for the victim. Research shows that loan fraud is the most costly form of identity theft, averaging about $4,687 per instance. In addition to identity-related challenges, digital lenders face a number of unique obstacles, including false income representations, deceitful employment information, and straw borrowing, among many others. A well-rounded fraud prevention strategy should not only address each of these risks, but also adapt to future fraud trends as criminals continue to find new ways to exploit digital lending systems. How AI Fraud Detection Makes Lending Easier for Lenders In this context, the most relevant form of artificial intelligence is machine learning, which allows algorithms to “learn” new information based on the data they process. The more data they work through, the more they can learn and apply that knowledge in the future. Here are some of the main ways in which AI can help combat and defeat criminals targeting credit providers: Adding supervised machine learning (SML) to fraud detection efforts offers improvements over rules-based systems because of the ability to generalize patterns from previous instances of fraud. SML models can leverage many more features than a manually created rule and simultaneously weight features more accurately. Lenders can prevent losses by detecting illicit activity earlier in the application process with unsupervised machine learning (UML). Algorithms can look for connections between applications and any other events to stop financial damage before it occurs. The lending industry can control fraud without adding expensive high friction multi-factor authentication steps to the credit application process with machine learning because this form of artificial intelligence outsmarts fraudsters by going beyond just relying on anomalies and adopting a holistic approach that finds correlations across events. ML, combined with advanced analytics and decision tools, can help lenders and other financial services providers to understand fraud attacks with more detail and incorporate learnings to their strategy automatically. How Datavisor Helps Lenders Put AI Fraud Detection to Work Traditional machine learning models are dependent on labeled training data that takes a few months to arrive. Then, financial institutions need to spend another few months training the model. By the time the new model goes live, a lot of fraud has already occurred. To shorten the learning curve, DataVisor predominantly relies on unsupervised machine learning, in which algorithms require no training data or extensive training period. Lenders can benefit from rapid time to value by taking a more proactive approach to staying ahead of fraudsters. Here are all the details about how DataVisor makes application fraud a thing of the past for its clients in the digital lending industry. Still curious? If you’re curious about machine learning and would like to brush up on the subject, check out our Dummy Handbook for Machine Learning. View posts by tags: Related Content: Quick Takes Infamous Fraud Cases and Their Implications for Modern Fraud Experts Quick Takes What Is Expense Fraud and How Can You Detect It? Quick Takes Does There Have to Be a Tradeoff Between Fraud Prevention and CX? about Parinitha Marnekar about Parinitha Marnekar View posts by tags: Related Content: Quick Takes Infamous Fraud Cases and Their Implications for Modern Fraud Experts Quick Takes What Is Expense Fraud and How Can You Detect It? Quick Takes Does There Have to Be a Tradeoff Between Fraud Prevention and CX?