How Can AI Fraud Detection Help the Banking Industry?

Could AI be a solution to combat banking fraud? The interest in AI technology in the banking industry continues to grow, with 85% of business decision-makers believing it will add value and advantages to business in the future. 

But bank organizations don’t have to (and shouldn’t) wait for the future to start leveraging the advantages of AI as it exists today. AI’s biggest advantages are built over time as algorithms collect more data and “learn” more about how to use it. Therefore, the benefits of AI are much like a savings account or other secure investment: the benefits begin the moment AI is deployed and will continue to grow uninterrupted the more you contribute to it. 

Let’s look at how you can start filling the gap between ambition and results using AI for banking fraud detection.

Machine Learning’s Role in the Banking Industry

By definition, machine learning is based on algorithms that can “learn” new information from the data it collects. The more data AI has to work with, the more it learns and the deeper insights banks can receive from their AI technology.

Already, we’re seeing major banks put AI technology into practice that approaches fraud detection from a forward-thinking perspective rather than waiting until after fraud occurs to act. Recent data found that 63% of financial institutions believe that AI can prevent fraud, while 80% agree that AI plays a critical role in reducing fraudulent payments and attempts to commit fraud. 

Machine learning technology can be deployed across multiple channels (e.g. transactions, loan applications, etc.) in the banking industry. Truthfully, this is a non-negotiable functionality if banks want to leverage AI to its fullest potential, as the banking industry at large consists of multiple features, functions, and products. As a result, AI can be used to detect fraud in more than one channel simultaneously simply by improving the way it finds anomalies in data over time.

Let’s look at some specific ways AI is currently being deployed in some of banking’s most critical and vulnerable service areas:

AI Fraud Detection for Transactions

Cybercrime remains one of the most expensive threats to consumers and the banking industry, costing an estimated $600 billion every year in the United States alone. Online transaction fraud accounts for the biggest slice, with an expected cost totaling more than $200 billion over the next five years. Or, to put another way, every $15 out of $1,000 spent online will be the byproduct of fraudulent activity.

Banks and financial institutions are inherently vulnerable to fraud and scams, which is why being able to detect illicit activity isn’t an option. As digital banking apps and online spending continues to grow, so must the efforts to detect and prevent fraud. 

One of the problems financial institutions face is the fact that fraud can take many shapes and forms. For example, someone who has stolen a person’s credit card information and identity and is engaging in fraudulent spending may fly under the radar because they’re using legitimate card numbers and personal details. Many banks have a number of false positives per day that typically go under a manual review process, but in doing so, banks risk inconveniencing a customer who is trying to conduct authentic transactions.

Machine learning is being used as a solution to detect transaction fraud before it occurs. This not only serves to protect customers from fraudulent effects but also reduces or eliminates friction for customers whose transactions are erroneously flagged.

AI Fraud Detection for Applications

Simple applications, such as payday advance loans, credit cards, and opening a direct deposit account, only require a few pieces of personal information. This alone makes it easy to commit application fraud. If a thief were to obtain sensitive data like a social security number, they could easily complete an 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.

Fraudulent mortgage loans are less frequent but just as costly. One study found that Q2 in 2019 saw that 0.81% of all mortgage loan applications contained fraudulent information. That was about 1 in 123 applications on average. 

Alarmingly, it’s not just career cybercriminals that are conducting mortgage fraud, but also industry insiders like bank officers, brokers, appraisers, and other related professionals. These activities are typically to commit fraud for profit, in which an individual misuses the mortgage lending process to steal funds from homeowners or lenders.

In fact, research shows that the banking industry is the hardest hit when it comes to occupational fraud, with about 17% of all reported fraud cases. These take the form of kiting, check tampering, and billing schemes, but identity theft and credit card fraud are becoming more common as online banking grows.

AI can help combat and defeat application fraud by detecting illicit activity early in the process. Algorithms can look for connections between applications for credit cards and loan applications, as well as monitor newly opened accounts to stop financial damage before it occurs.

AI Fraud Detection for Anti-Money Laundering

While money laundering isn’t always easy to detect, AI’s ability to monitor spending and deposit patterns over time can alert staff to anomalies and block payments before they can be completed. Algorithms can pull from a variety of data points, from transaction origination to the end destination and more, to identify deviations from normal patterns. 

The goal is twofold: first, AI can help ensure that payments are being made willingly by the individual. And second, AI can help reduce false positives that could occur with traditional fraud detection methods.

How DataVisor is Bringing AI to Banking

Many AI solutions depend on the initial data points that are used to learn to detect anomalies, but DataVisor’s proprietary algorithms are able to leverage unlabeled data sets to further expand their potential. 

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 to train 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. Banks can benefit from rapid time to value by taking a more proactive approach to staying ahead of fraudsters.

Discover how DataVisor is bringing AI to the banking industry through early detection and actionable results by scheduling a free demo.