Fraud detection continues to be a challenge for banks and financial institutions. Though fraudulent transactions are rare, representing only a minority of transactions and activities, they, unfortunately, can represent large financial losses. Fraud cost Americans a total of about $56 billion last year, with about 49 million consumers falling victim to it.
The good news is that with advances in fraud analytics, systems can learn, adapt and uncover emerging patterns for preventing fraud.
“Unfortunately, most organizations still use rule-based systems as their primary tool to detect fraud,” notes enterprise software and analytics provider SAS on their blog. Rules can do an excellent job of uncovering known patterns; however, rules alone cannot find unknown schemes, adapt to new fraud patterns, or handle fraudsters’ increasingly sophisticated techniques.
This is why fraud detection can be uncovered by predictive analytics and anomaly detection, powered by machine learning and artificial intelligence within data science. Let’s have a closer look.
Predictive Analytics and Anomaly Detection
Predictive analytics are models that extrapolate from current data to predict what might happen in the future. When prediction is required, machine learning algorithms are used, and preprocessed data can be fed into the algorithm to learn how to effectively predict future events. The more data that is available, the better the predictive model will be. Existing data is used to “train” the model and predict future behaviors.
Shallow learning is an ineffective and outdated process whereby the data needs to be cleaned and transformed for the algorithm to even work. In contrast, deep learning transforms the data by itself without any human preprocessing or intervention required to set the initial rules. It is practically hands-off and learns on its own, and thereby achieves better performance.
Of course, tracking instances of fraud, such as identity theft, and minimizing the damage caused, is a top priority for financial businesses. Unfortunately, many firms cannot keep up with the volume and level of sophistication of fraud schemes, and the assortment of point solutions they have in place might thwart some criminal activity. Still, it also can prevent legitimate accounts from being established during the onboarding process.
Financial institutions need an outsourced provider of customer onboarding, such as Instnt, to ensure that the most advanced tools are in place and that they aren’t missing the opportunity to serve legitimate customers who open accounts.
Detecting anomalies or unusual behaviors in a customer’s bank account or transaction history are tougher to predict because these situations are only revealed to be anomalous — and potentially fraudulent— after each occurrence.
It’s even tougher with onboarding because for a new customer (one that has not been onboarded in the past), opening an account would not be considered anomalous.
Challenges of Fraud Detection
Detecting and preventing fraud is not without its challenges. For machine learning to be effective, the availability of large volumes of quality data is essential. As mentioned above, fraudulent transactions represent only a small portion of the overall transaction volume for a bank or financial institution.
Many organizations do not have sufficient existing data to enable them to move to advanced data analytics immediately. On top of that, some organizations that do have the necessary information may be unwilling to reveal sensitive data due to privacy issues.
A system’s predictive power hinges on its ability to form correlations in data, which may be missed if too few cases of fraud are available. Data scientists must work to overcome this challenge and can employ several techniques to do that — from specialized techniques for outlier detection to data transformation methods, such as oversampling or undersampling, according to Netguru.
Instnt’s Fraud Detection and Prevention
Instnt is the first fully managed digital customer onboarding service for businesses with up to $100MM annually in fraud loss insurance. With a codeless integration on websites or apps, Instnt can reduce rejection rates by 50% without friction or fraud, grow top-line revenue, and lower operational costs by 30%.