As online banking and shopping continue to expand, so, too, will bank fraud.
When it comes to bank fraud detection, there are a number of techniques banks can use, including internal processes and protocols and adhering to governmental guidelines.
One of the most effective, however, may well be fraud detection technologies that incorporate AI, machine learning, and big data. In this article, we will look at:
- definitions of AI and Machine Learning;
- how these technologies apply to fraud detection; and,
- the impact of these technologies on the fraud detection space.
What are AI and Machine Learning?
To begin, let’s start with an understanding of AI (Artificial Intelligence) and Machine Learning. Many people conflate the two technologies or else think they are two unrelated things.
AI is, in fact, the larger category in which ML is included.
AI is the overall collection of techniques and technologies that involve, in essence, teaching machines to think like humans.
Machine learning is a subset of that, and it involves the process whereby a machine can be trained to get better at a task without a programmer having to program those changes explicitly.
Current Fraud Detection Practices
How do banks and financial institutions currently identify fraudulent transactions as part of their cyber security protocols?
Today, the vast majority of banks and financial institutions use two techniques in identifying unauthorized transactions:
- manual review
- and rules-based reviews
Manual review is just what it sounds like — a fraud detection employee manually reviews transactions and accounts on a regular basis.
Rules-based reviews involve reviewing a transaction if it has violated either an internal rule or a governmental rule. For example, a transaction for a very large amount might trigger an internal rule that says, “Any transaction over X dollars must be assessed for fraud.”
While effective, both of these approaches are labor-intensive. This pain point can be ameliorated to a large extent by the automation delivered by AI and Machine Learning.
Applying AI and Machine Learning in Fraud Detection
AI and Machine Learning have many applications in the world of fraud detection.
As technologies continue to improve and more and more financial institutions adopt those technologies, AI and ML may well become the most powerful tools in the anti-fraud toolbox.
How? By automating processes and learning over time how to work more effectively.
Machine learning involves designing algorithms that automatically find ways to work better and more efficiently based on historical data and previous feedback.
The big data capabilities of today’s software solutions means that AI-driven, machine learning solutions can scan and learn from scads of data, making them even more powerful.
Since bank fraud follows certain patterns, software solutions that leverage AI, ML, and big data will be able to recognize and flag fraud much more effectively than humans.
The Benefits of AI in Fraud Detection
The benefits of AI in fraud detection are myriad, but can be summarized through the following:
AI-driven solutions clearly far outpace manual or human-driven efforts.
Machine learning also enables the processing of mountains of data that would take humans much more time to process. Given the volume of transactions in the online banking world, this speed is essential.
Machine learning solutions learn over time, constantly improving efficiency.
Human efforts, of course, can ebb and flow depending on the environment. This increased efficiency will make banks more successful at quickly spotting new techniques used by criminals when committing bank fraud.
Online transactions will only increase for banks and businesses. Humans running manual transactions will not be able to keep up. AI is inherently scalable.
In fact, ML-driven solutions are only happy to scale up; after all, more data means more opportunities to improve.
AI and machine learning will also make for a more accurate fraud detection process. They will be able to stop wild goose chases and will also make it easier to identify less obvious signs of fraud.
Finally, criminals are using similar technologies. Large-scale fraud enterprises are employing AI and machine learning solutions themselves. Rather than fall behind, banks and financial institutions need to adopt such technologies quickly to create a more even playing field with fraudsters.
Use Cases for AI in Fraud Detection
There are many use cases for AI in the world of fraud detection, including but not limited to detecting transactional anomalies, verifying customer IDs, and reducing the occurrence of false positives.
Detecting Anomalies Automatically
Software powered by AI and ML, for example, can scan transactions for anomalies automatically, then notify a human fraud detector when a suspect transaction is identified.
The software will learn from the process, as well.
Each time the human auditor confirms or rejects a suspected instance of fraud, the software will integrate that data into the ML model so that it makes “smarter” choices moving forward and learns to recognize acceptable deviations from a norm.
Verifying Customer IDs
When creating a new account, banks and financial institutions need to adhere to strict protocols (including PSD2 in the EU) when it comes to verifying the account holder’s identification.
This can be a lengthy and cumbersome process.
AI is well-equipped to automate the process. For example, devices that use biometrics to provide access to a financial account can become better at identifying false log-ins with the help of AI.
By assessing keystroke patterns, 3D facial recognition, and voice recognition, AI can make the security protocols for these solutions much more secure.
AI can also help during initial account setup, scanning submitted IDs to determine their veracity and ensure that a legitimate entity is attempting to establish a financial account.
Reducing False Positives
One important aspect of bank fraud technology is that it can help reduce the occurrence of false positives.
Banks and other financial institutions have to put a lot of time and money into identifying bank fraud, but this, unfortunately, often comes with a high number of false positives.
According to one study, some financial institutions even report false-positive rates as high as 90 percent. That means that only 10 percent of their fraud detection efforts are actually working effectively.
The higher the number of false positives a bank comes up with during a bank fraud investigation, the more time and money they have wasted.
AI may prove to be especially effective at combating these false positives. AI has already made some impressive headway in the world of AML detection, resulting in a reduction of false positives at 55 percent of financial institutions included in one study.
Improving the Auditing Process
Machine learning also has significant implications in the compliance checklist and auditing process that should take part in any financial institution.
Many banks use surprise audits to catch any fraudulent transactions that may have escaped notice during the initial fraud detection process.
Machine learning improves audits by learning to identify fraud patterns in an audit. Again, as with detecting anomalies in real-time, machine learning will improve its performance as it participates in audit after audit, becoming more adept at spotting real fraud and winnowing out false positives.
AI and machine learning leveraged in partnership with big data have the ability to transform fraud detection at banks and financial institutions around the world.
Given the speed with which fraudsters are adopting online financial transactions, implementing these new fraud technologies should be an integral part of any bank’s fraud detection strategy.