New digital payments have been on the rise and with transactions becoming increasingly digital, fraudulent activities are also ramping up. Know how machine learning is an invaluable tool when it comes to detecting fraud patterns and anomalies proactively.
With digitization continuing its sweeping force across the payments landscape, fraudulent activities have been on the rise.
Today, when it comes to payments — customers in the financial landscape expect speed and greater agility. This explains why the likes of card-not-present transactions, digital wallets, and contactless payments have been gaining momentum almost replacing cash and physical card transactions.
With consumers opting for touch-free payment alternatives in view of health hazards, digital payments have ramped up making way for financial crime and fraudulent activities. It’s as simple as this — every electronic transaction becomes an entry point for fraudsters that are looking for loopholes in the system.
The global losses from cybercrime hit a record-breaking $1 trillion in 2020. If that wasn’t bad enough, we’re headed for much worse in the future. Provided that cybercrime keeps up at this rate, the estimated global losses can skyrocket to $10.5 trillion by 2025.
While there’s an ongoing arms race between bad actors and fraud prevention experts — it’s not all downhill from here!
The record adoption of digital payments creates unprecedented opportunities for fraudsters to tap into.
Organizations in the financial services industry can get a jump-start in fighting fraud by deploying advanced fraud-prevention solutions and cybersecurity technologies like machine learning.
We cannot fathom the type of fraud that will emerge as technology continues to evolve — but we can be prepared.
Financial institutions need to replace reactive approaches with a holistic, proactive approach to combat fraudulent activities. The best way to go about this is by leveraging all available data within the customer lifecycle and employing sophisticated machine learning algorithms to detect legitimate transactions from those that are illegitimate, in real-time.
In an ever-evolving digital payments landscape, fraud-prevention solutions must include dynamic rules and be scalable. Machine learning approaches present a huge opportunity to detect fraud —using advanced techniques to analyze contextual information, these approaches have the upper hand in detecting unknown and rapidly evolving fraudulent patterns.
You would agree with me when I say those rule-based legacy fraud solutions are a thing of the past.
Owing to its capabilities of detecting hidden fraud and leveraging fraud scoring, machine learning is a far more effective mechanism to fight fraud, especially in situations where historical data is not available.
Unsupervised machine learning (UML), in particular, simplifies verification to a great extent and self-tunes itself to detect fraud in real time.
Here are a few reasons why I believe machine learning is well-suited for detecting fraud:
Swedish philosopher, Nick Bostrom made a bold statement when he said – “Machine intelligence is the last invention that humanity will ever need to make.”
There’s no denying the fact that machine learning brings with it a world of possibilities to the ever-changing financial services landscape.
The time has come for financial institutions to look beyond legacy rule-based systems for anomaly detection. Furthermore, a switch from reactive to proactive models is important to keep pace with modern innovations and ever-changing fraud patterns. Machine learning has proven to be more effective than humans when it comes to identifying subtle trends or non-intuitive fraudulent patterns.
To stay in the game, the key is to leverage advanced machine learning algorithms — that are able to proactively detect fraud regardless of the payment methods or processes.
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Chief Executive Officer, Opus