Today, businesses understand that fraud is age-old, and also eternal — it can never be totally eliminated. Adding to an already losing battle; on average fraud costs companies three dollars for every dollar of fraud loss. From automated fraud prevention systems to manual order reviews, business leaders are forced to implement programs to combat losses, then manage costs as tightly as possible.
By focusing on cutting costs instead of boosting revenue organizations are doing themselves a disservice. As part of our “Behind the Science” series, I’d like to discuss how organizations can turn their fraud management strategies into revenue drivers by taking advantage of both human expertise and automation technology.
Flipping fraud prevention from a cost center to revenue optimized fraud management requires a paradigm shift. Fraud management strategies must pivot from stopping transactions to approving them. The key is in reducing customer friction and providing seamless experiences today’s customers want. In the age of instant gratification and one-click shopping, consumers have a tremendous amount of choice and will go where they get the best experience and feel the safest.
When it comes to the internal implementation of fraud management strategies, the focus remains on how people engage with technology to produce the right outcomes. “Will robots replace human jobs?” is a popular headline lately. When it comes to fraud prevention, the short answer is no. Artificial intelligence and machine learning algorithms are not going to completely replace fraud managers and analysts, but of course, there is more to it than that.
Our philosophy: functionally superior technology brings out the best in human expertise.
At Emailage, our position is fraud prevention and digital identity solutions should enhance human expertise, not replace it. The active intelligence our Digital Identity Scoring — supported by the network effect — offers a way to make humans more efficient and fraud management strategies generate more revenue, not less.
Here’s how machine learning can help optimize human fraud analyst teams:
- Pattern detection – A name or email address associated with multiple fraud incidents could be flagged and transactions with it automatically declined.
- Custom modeling – By analyzing data from multiple industries and regions, machine learning can be used to create automatic rules and models using the patterns that matter most to your organization.
- Flagging anomalies – Not everything fits into a pattern. Fraud management tools like our DigitalIDentity Score maximize the efficiency of analysts by flagging only the most suspicious transactions for manual review.
With the power of big data, machine learning, and network intelligence, organizations leveraging our functionally superior approach can free up fraud analysts to provide deep expertise that improves risk programs.
Differentiated perspective creates significant economic advantage
Inside your organization, the goal is to approve more transactions and stop as many fraud losses as possible. For your clients, the goal is to create best-in-class experiences.
You can fight fraud and improve client experiences simultaneously. Partnering with a fraud management team who builds custom models designed to maximize the efficiency of your human teams, automatically approve your best customers and flip your fraud management strategy to one that drives revenue while reducing overhead costs.
Learn more about how machine learning and artificial intelligence can be used to grow revenue in your business. Download the full presentation below!