We are all limited on time and resources, so being able to automate your customer analysis using machine learning and receive reliable insights should be a capability of your fraud management tool.
How important is to know a good customer vs. a bad customer when tackling payment fraud?
It’s not a new revelation that customer loyalty is a table stake for sustainable growth for your business. We know that loyal customers are more profitable and yield a higher lifetime value. And it's common sense to invest in understanding what drives loyalty, value, and the triggers of repeat purchases.
But the purpose of fraud prevention solutions is to detect and help prevent fraud; unfortunately, many solutions are rampant with false positives. Auto-declining suspicious transactions can also lead to good customers being rejected. False declines can be the bane of your existence.
Dealing with false positives
False declines represent good transactions that are declined due to suspicion of fraud. And a false decline often means that customer is lost not just for that transaction, but for future purchases as well and they could go somewhere else. You desire to make every sale possible, so false declines can eat into your profits and may cause your customers to navigate away from your websites for future orders.
Inevitably, good transactions are declined when implement your fraud strategy. According to the AITE 2019 report1, 55 percent of merchants report that their fraud prevention solutions automatically decline between 3.1 percent and 7.5 percent of all transactions. And this could be higher today due to the additional measures taken during challenging times.
As a result, you should manage your ecommerce channels with finesse to increase sales while improving security in an environment in which threats are ever-growing and consumers demand a quick, easy and safe checkout experience.
What can you do to identify and target good transactions to overcome bad actors?
Have the right analysis tools.
Ensure that your fraud management tool is able to analyze your customer transactions based your customers’ behaviors.
- Uses machine learning to analyze the usage of customer identity attributes like email, account number, etc.
- Detect changes in identity usage and identify whether those changes are improper or legitimate.
- Use the insights to customize your fraud/risk rules to capture similar or repeatable purchases.
Shift your approach.
Focus on the positive behaviors of your customer transactions. Fraud and risk management strategies tend to start with the detection of fraudulent transactions, which can reject genuine orders. But balance your risk with your bottom line, by optimizing your payment processing to identify behaviors that provide a seamless experience for returning and lower your manual review rate based on positive behaviors.
Spend less time investigating new customers.
Have access to a database that can identify customers outside of your network. And automate this process so less time is spend trying to authenticate new customers and expedite the orders to enhance their experience with your shopping cart.
We are all limited on time and resources, so being able to automate your customer analysis using machine learning and receive reliable insights should be a capability of your fraud management tool. Learn how Decision Manager’s Identity Behavior Analysis can help easily recognize good and bad consumer transactions and automatically detect new customers that increase your accept rates and boost your bottom line.
1The E-Commerce Conundrum: Balancing False Declines and Fraud Prevention, July 2019, AITE report