Today’s fraudsters are more sophisticated than ever, making catching them more difficult than ever before. Increased access to data gives them the opportunity to attack online and offline in many new ways, hacking, tricking and faking their way past barriers. In order to have the ability to fight the knowledge, resource and dexterity of modern fraudsters, companies are increasing security measures, which bring inevitably a certain amount of customer disruption to the user experience.
One unfortunate by-product of this is that in their endeavours to stop fraudsters, businesses can run the risk of treating good, honest customers like fraudsters. No user enjoys the time and effort spent managing enhanced security, including frustrations with differing password polices and the struggle to remember them all.
The biggest concern here, obviously, is the resulting irritated customers. Modern consumers have clear-cut expectations, they expect engagement with a brand to be easy and transactions they make to be safe. The fight against fraud can create friction in this experience, but it needn’t be that way.
The anti-fraud portfolio
Businesses need to apply fraud mitigation strategies that reflect the value and level of confidence needed for each transaction to strike the balance of keeping customer disruption down while maintaining the necessary level of fraud management. It’s about finding the right size fraud solution.
A multi-layered approach to authentication is the ‘gold standard’ for identifying legitimate customers. The challenge here is striking the right balance of questioning so as to prevent an adverse reaction from the customer. Having access and insight into universal consumer behaviour down to the transaction level is necessary for fraud mitigation in the future and builds the framework to create a positive experience for consumers.
A powerful predicting tool
Machine learning has become an invaluable advanced tool in the fight against fraud. While this is a great technology, a solid machine learning-based solution requires specialised expertise to apply rigorous methodology in data analysis.
Machine learning is a type of artificial intelligence that provides computer systems the ability to learn without being programmed for every eventuality they experience. The field develops software that teaches itself adapt to new data and make a sensible decision to use it.
It’s very similar to the more mature field of data mining. Both methods look through data inputs for recognisable and actionable patterns. It’s just that machine learning doesn’t flag a pattern or exception to a person, it is programmed to be able to adjust its own outputs.
When in play, the systems begin to process every transaction that comes through, creating hundreds, thousands, however many rules that are needed.
Because of the speed of activity it’s smarter for businesses to take a hybrid approach to managing the decisions made, to ensure the software’s decisions are checked and balanced. That allows tempered risk thinking to be applied to the artificial intelligence. Balancing machine learning techniques with using characteristic-based analytics can help to reduce false positives and the potential unhappy customers if it happens that unbalanced decisions are made by the software.
When it comes to fraud, it’s essential to always think ahead. Businesses do this when it comes to revenue growth and marketing to new customers, and fraud detection and prevention are no different. When fraudsters are actively researching and testing new ways to sneak ahead, the business community must be even more determined to protect their customers and themselves.