Fraudulent activity occurs daily, sometimes accidentally but too much through intentionally deceptive means. Financial institutions must concern themselves with things like embezzlement, misappropriation, mortgage fraud, identity theft, check fraud, and more.
Fraud takes a large toll on financial institutions. Identity theft, for example, is on the rise. According to the GIACT’s new report, U.S. Identity theft: The Stark Reality, “In the past two years, almost half (47%) of U.S. consumers surveyed experienced identity theft; well over one-third (37%) experienced application fraud and over one-third (38%) of consumers experienced account takeover.”
Until recently, those financial institutions had to manually discover fraudulent activity, oftentimes not until much later. With the invention of more advanced technology, such as artificial intelligence (Ai) and machine learning, fraud has been much easier to detect.
ElectrifAi’s has deep domain expertise in the banking, financial services, and insurance (BFSI) industry with many machine learning models available. One of those models, Bust-Out Fraud, was created to tackle a particularly devious fraudulent activity, bust-out fraud.
What is Bust-Out Fraud?
A bust-out is a type of credit card fraud where a person applies for a credit card, uses the card normally, and pays bills on time to establish a solid repayment history and get a larger credit limit, then quickly makes a lot of purchases, and maxes out the card with no intention of paying the bill.
How to Identify Fraudulent Customers Before Approving Credit
Machine learning can help you identify fraudsters early on before they rack up credit card charges with no intention of repaying the charge. Fraudulent transactions leave behind digital breadcrumbs that machine learning algorithms use to recognize patterns.
These patterns flag suspicious activity, such as an individual drastically increasing purchases. Detecting past fraud can prevent credit from being issued to an individual who might commit future fraud. This can be done manually, of course, but machine learning makes that manual process much faster with greater accuracy.
Imagine getting results in seconds that used to take hours or days manually sifting through thousands of data points. But machine learning doesn’t replace analysts; instead, it flags the information analysts need to make an informed decision. Those analysts are now much more efficient and can process more applications each day.
ElectrifAi’s Bust-Out Fraud model has helped banks and credit card companies quickly determine who is likely to commit bust-out fraud. This model is a collection of models that uses transactional history, payments, and non-monetary activity to detect and prevent fraud before it happens.
How ElectrifAi Can Help Your Business
ElectrifAi’s Bust-Out Fraud machine learning model has been used in the real world, helping banks and credit card companies achieve impressive business results. Below are just a few examples:
We can help your company achieve this and more by implementing the Bust-Out Fraud machine learning model seamlessly into your existing systems. With very little maintenance and minimal operations, you can easily benefit from the capabilities of machine learning.
The Bust-Out Fraud machine learning model is not the only model we have; in fact, we have a large library of models that can scale across your entire business. Our deep domain knowledge in the BFSI industry and expertise in Natural Language Processing (NLP) and Computer Vision can help you get faster, better, cheaper, and less risky solutions with amazing time-to-value.
If you would like to begin your digital transformation and see an incredibly high return on investment (ROI), contact us today! We would love to schedule a custom demo so you can see how your business can benefit from machine learning.