For many institutions that offer credit (traditional banks, neo-banks, auto loan lenders, consumer credit companies, etc.), outmanoeuvring the increasingly sophisticated traps laid by internet scam artists is too often synonymous with staggering costs—in time, human resources and money. Yet some companies out there are managing to fight fraud effectively, while increasing their profits.
Their secret? Innovation and—more specifically—machine learning.
According to WPI, credit fraud could lead to 45 billion in annual losses by 2023. If fraud were a country, it would be the fifth largest in the world. And as the digital revolution speeds along and business increasingly moves online, attempts at credit fraud are climbing right along with it.
While fraud continues to flourish, credit institutions are facing a twofold challenge—interest rates have dropped over the last ten years, particularly for consumer loans. Low interest rates might be great for borrowers, but this shift has made it harder for credit lenders to absorb the cost of fraud.
Today, they need to sell twice as much credit just to cover the loan defaults and fraud losses that are chipping away at their net banking income, already dwindling under the weight of rock-bottom interest rates.
Credit lenders are doing a good job of defending themselves against credit fraud, preventing 90% of incidents before they happen.
The problem is that in order to cover their losses—and more importantly, to increase their revenue—they need to eliminate the remaining 10% of fraud still slipping through the net.
But when it comes to battling this increasingly cumbersome residual fraud, traditional fraud detection systems are no longer enough.
First, they overwhelm employees with security alerts that need to be processed manually and generate too many false positives that must then be dealt with, requiring companies to hire additional staff.
Second, from a customer service perspective, these measures are counterproductive, increasing waiting times for legitimate clients. Applicants submit an online credit request in minutes, then wait for days (or even weeks) to get an answer. It’s a frustrating process and not everybody is patient enough to wait.
The Outcome: Lower ROI, wasted time, added pressure on staff and a poorer customer experience. All this to recover… 1% of losses.
In order to break the cycle and outwit scam artists who are becoming increasingly agile and tech savvy, many credit lenders have realized they need to take a different approach.
From Carrefour Banque to Renault Finance, more and more companies are augmenting their existing fraud detection systems with machine learning and AI-driven solutions.
Easy to set up, quick to produce results, and customizable to any business or industry, ML-driven solutions like Bleckwen Credit Fraud Services can process much more data than traditional systems, continuously learning and fine-tuning their fraud-detection radar. The result? Unparalleled performance.
Between July 2020 and January 2021 alone, Carrefour Banque & Assurance was able to prevent €23.18 in fraud for every €1000 of consumer credit granted, and €7.41 for every €1000 of revolving credit granted, thanks to machine learning.
And the benefits don’t stop there. ML-driven credit fraud detection solutions like Bleckwen Credit Fraud Services generate 10x fewer alerts and false positives than rule-based systems, pinpointing the real potential fraud attempts better and more accurately.
The Outcome: Your staff saves valuable time and energy, but still makes the final call in cases of potential fraud.
In addition, the solution’s logic and interface are intuitive, producing results that are easy to understand—a big plus for operational efficiency (teams know where to look for information), regulation compliance and customer service.
Want to give us a try? Contact us today to start your free trial
In only three months, you’ll see the difference Bleckwen Credit Fraud Services makes…
Need more convincing? Download our Carrefour Banque Customer Story and find out how the company was able to boost its revenue thanks to machine learning fraud detection.
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