Loyalty fraud is evolving. Fraudsters are getting smarter, and so must the solutions that protect brands – and loyal customers – against them. Using artificial intelligence is no longer the future of fraud protection: it’s a critical pillar.
Beyond increased customer fraud protection, implementing AI helps brands achieve four broader business and organizational goals.
4 key business goals AI helps achieve:
- New revenue streams – Because using AI increases customer fraud protection, brands are able to entertain new revenue streams they may have previously considered too risky. Square, for example, used AI to allow anyone using its service to accept credit card payments. Banks that relied solely on rule-based systems were unable to efficiently determine good versus fraudulent card behavior for smaller businesses. Unable to control their losses, banks eventually opted to reject an entire segment of the business population in an effort to minimize risk. By integrating AI, Square’s fraud protection was strengthened and it was able to provide a broader array of businesses with the ability to accept a credit card payments, opening up huge, previously untapped audiences and revenues.
- Increased accuracy over time – The if/then code used for simple rule-based protections can be invaded and mimicked by fraudsters. A simple hack can reverse-engineer the system so that flags that remain on an account are actually “good” users (loyal customer accounts) that a rule-based machine inaccurately marks as “bad” (fraudulent). Because AI is integrated into predictive modeling, machines can digest tons of data, their accuracy in predicting fraud is largely improved. Over time, because of AI machines are able to generate various probability-of-fraud scores to protect loyal customers from inaccuracies and interruptions.
- Less maintenance and fewer resources – As time passes and more rules are created, it becomes increasingly difficult to monitor and maintain huge rule-based systems. In the implementation of AI solutions, behavioral analysis can occur in real time. Machines “learn” various account signals and can develop probability scores to detect fraudulent activity before a transaction occurs. The result: fewer scams and fewer resources needed to mitigate instances of fraud with customers.
- Scalability through automation – Automation can aggregate scores, not individual rules. Predictive modeling machines analyze huge amounts of data in real time to discover patterns indicative of fraudulent activity that would take humans years to uncover. These capabilities make machine learnings far more accurate and scalable than human and rule-based approaches. As companies grow and the number of reward members increases, machines are able to scale up with them.
Pairing AI with rule-based
As scammers’ strategies and tactics for committing loyalty fraud improve, AI machines learn and evolve to keep brands ahead of the curve. Without AI, simple maintenance and analysis of a rule-based system can become increasingly cumbersome.
Instead, pair rule-based and AI protections. The two systems can – and should – work together to provide the fraud protection brands need. Rule-based detections are a great guardrail against loyalty program fraud. A machine learning approach can do the bulk of the heavy lifting, allowing businesses to predict fraud, scale their protections and adapt in real time.
The combination saves valuable time and resources, improves efficiency and, because AI can predict fraud before it occurs, it decreases a potential friction point, improving the customer experience.
And to learn how Connexions’ fraud solution – Rewards Shield – is helping organizations reap these benefits, click on the image below.