When you’re comparing different anti-fraud solutions for your financial institution, you’re likely to hear the phrase “machine learning” a lot. Machine learning is a type of artificial intelligence (AI) that mimics the human brain by using information and data to improve performance.
In other words, machine learning solutions get better at their jobs over time — they improve their ability to spot fraud more accurately.
However, all machine learning is not the same. To ensure you’re choosing a solution with the most effective machine learning for your fraud detection and prevention efforts, you should ask the following questions.
1. Is the solution flexible and future-proof?
Banking and AI can both move quickly, and you don’t want to invest in a solution that is at risk of becoming obsolete when technology evolves or banking practices change. Look for machine learning solutions that are flexible and responsive to both industry and technology changes.
2. Is it scalable?
In addition to being future-proof from a technological and industrial perspective, your solution also needs to be scalable. If your financial institution doubles the number of transactions it processes, for example, you need a solution that can keep up with that adjustment.
Anti-fraud solutions that aren’t scalable can block your growth. They may increase your risk of fraud activities during growth periods or force you to incur additional capital expenses. Talk with potential vendors about how their systems scale and what steps you will need to take to adjust the solution as your needs change.
3. Does the solution support multiple and custom models?
Fraud takes different forms, and if your fraud detection solution only uses one model, it will miss signs of fraud. You need a solution that incorporates multiple models but also lets you customize the models based on the nuances of fraud patterns in your specific area.
4. Can the solution pull data from multiple points?
Ideally, you also want an integrated machine learning solution that can pull data from multiple points. It should be able to assess data from multiple touchpoints including your app, website, ATMs, and other transaction channels, and it should use these data points to develop a more complex view of customer behavior.
Beyond just looking at internal data, the solution should also be able to analyze data from outside points and assess its potential contribution to the risk of fraud.
5. Can you explain the fraud-detection process?
You don’t need in-depth knowledge about the underlying mechanics of the fraud detection process, but you should have a sense of how it identifies transactions as potentially fraudulent. Talk with vendors about the decision-maker process used by the machine learning solution you’re considering and ask how it identifies its outputs.
6. How specific are the models?
Machine-learning anti-fraud solutions analyze transactions for signs of fraud based on their knowledge of normal and fraudulent behavior. The more specific the models used to distinguish between normal and fraudulent behavior, the more effective the solution will be. Ask the vendor if every transaction gets screened using a generic model or a specific model.
To explain what this means, here’s an example of a general fraud model. Someone who opens a bank account in Green Bay, Wisconsin is not likely to be making a purchase in Paris, France, and if their card is presented at a POS in Paris, it will be declined. That is a very rudimentary model that can create false positives.
A more specific model may note that Susan B. who opened an account in Green Bay is likely to travel to Paris once or twice a year on business, and based on that specific information, the system will allow her purchase to go through without flagging it as potentially fraudulent. Solutions that create more detailed models of customer behaviors are less likely to create false positives.
7. What type of support do you offer?
Find out what type of support the vendor offers. Will the solution be housed on your servers? Or does the vendor offer a cloud-based solution with software as a service (SaaS)? Does the vendor help with configurations? What type of uptime can they guarantee? Talk with the vendor about how they support the implementation and use of the solution.
8. Are the dashboards configurable?
Talk with the vendor about what information appears on the dashboard, and figure out what steps you need to customize the metrics. Some vendors offer a set dashboard, others will customize the dashboard for you, and still, others offer self-configurable controls.
9. Is case management flexible?
Finally, you should find out if you can configure and change the workflows you use. Flexible case management lets you self-configure workflows based on your financial institutions’ priorities and resources.
For instance, you may want the ability to prioritize which cases get escalated for manual review based on your staffs’ schedules. Or you may want tools that automatically distribute work to different departments based on the criteria you have identified.
Anti-fraud solutions are not optional if you want to run a strong financial institution, but simply because a solution claims to be backed by machine learning does not mean that it will meet your needs. Before investing in a solution, talk with potential vendors about what machine learning implies with their product or service.
At SQN Banking Systems, we create fraud prevention you can trust — contact us today to talk about our real-time, machine-learning fraud detection and prevention solutions.