Customer loyalty programs manage thousands of member accounts while processing millions of transactions from hundreds of locations. At this rate and volume of data processing, detecting fraudulent behavior is a hurdle that may be extremely difficult to overcome using conventional techniques. Faisal Abidi, the co-founder of RNF Technologies, emphasized how, if properly applied, machine learning (ML) and artificial intelligence (AI) solutions may offer just the kind of leverage they need to defend themselves against potential monetary and reputational losses brought on by the exploitation of their vitally important loyalty system.
The Advantages Of Loyalty Programmes
- It evaluates danger and trust in the present.
Fraud analysts are limited by their human nature. For instance, a single fraud analyst can analyze about 50 transactions per day, according to best practices for manual fraud reviews. However, it becomes more challenging to sustain manual evaluations when an e-commerce organization experiences more interactions.
However, real-time evaluation of hundreds of interactions for trust or risk is possible with machine learning fraud detection. And because it can work alone, AI can swiftly fill up knowledge gaps in humans.
Even the best analysts cannot fully process all the factors because there are too many of them and not enough time. Machine learning can do whatever criteria a fraud analyst might verify in less than 200 milliseconds, nearly instantly.
- It is precise, proactive, and retroactive.
Fraud protection with machine learning is quick and precise. Additionally, tech companies have used the technology proactively and in the past. Supervised and unsupervised machine learning is used to evaluate previous and current contacts’ actual risk and trust.
After identifying risk indicators in one contact, machine learning looks for comparable elements in other encounters. And when machine learning is used retrospectively, it is possible to see the risk information underlying earlier transactions and use that knowledge to inform present interactions.
Data scientists use millions of prior interactions, including account setup, sign-in, and checkout, to train machine learning models. When applying machine learning, you may observe how the technology would have decided your previous transactions.
You can see the exact number of transactions that your business may have approved and benefited from.
- It is not limited to fraud detection.
As pointed out by Faisal Abidi, who is also the co-founder of Phonato Studios, machine learning develops on its own, spotting risk and trust patterns that a human analyst working alone wouldn’t be able to. The technology gains more knowledge about which ways of danger and trust are more dominant over others as time goes on.
While machine learning is excellent for detecting fraud, it is capable of much more. Additionally, firms use it to manage and assess risk across new payment and delivery channels, hence reducing friction in client experiences.
Imagine that you wish to start accepting mobile payments, and a customer sends you their first ever mobile payment. Machine learning models can forecast payment behavior using data from prior mobile payments and similar customers.
Now suppose you sign up that consumer for your loyalty program. Again, machine learning may provide you with more details about the lifetime value of a customer and forecast their purchasing patterns, allowing you to market to them more effectively.
Conclusion
Several pieces of research show that machine learning solutions can unquestionably be advantageous in efforts to stop and identify fraud within loyalty programs. The final hurdle is to include them in our loyalty program and ensure they can consistently process massive amounts of transactions in real-time. The main objective is to increase security throughout the loyalty experience, which is why the work is believed to be worthwhile.
