Understanding Fraud in the Technology
Fraud in the technology industry costs businesses and consumers billions of dollars each year. These costs are in the form of lost revenue from fraudulent transactions and payments, higher operational expenses to combat fraud, loss of customer trust, and a diminished brand reputation.
Criminals commit fraud by using a range of tactics to steal or divert money and services from honest customers. Examples of this type of fraud include:
Account takeover fraud, which occurs when a bad actor obtains valid login information to hijack a customer’s account and use it for illicit purposes. This type of fraud is on the rise, due to the dark market demand for stolen credentials such as email addresses and passwords. New account opening fraud, in which a bad actor creates new accounts by using bits and pieces of real identity data to obtain services or access offers. This type of fraud is also on the rise, as it provides a means for bad actors to bypass anti-fraud measures such as two-factor authentication (2FA).
Fraud in the Technology Sector
Payments fraud, including wire fraud and card not present (CNP) transactions. These types of fraud involve the use of stolen credit cards and other forms of digital payment data, which can expose organizations to increased risk and impact customer experience.
Understanding Fraud in the Technology Industry
In the era of increasing online commerce, companies must balance their commitment to fraud prevention with the need for a positive customer experience. Tighter fraud and security controls often result in friction, which can negatively affect customer satisfaction. Those who recognize this tension and work to strike a balanced approach between best-practice fraud control and customer experience will be better equipped to protect their business from escalating losses and create a standout client journey.
Understanding fraud in the technology industry
To fight fraud and meet consumer expectations, leading technology companies are employing an array of techniques to identify suspicious activity. These include machine learning, image recognition and digital analysis based on Benford’s Law to reveal unexpected patterns in data. This helps to reduce noise and false positives while boosting the effectiveness of fraud detection.
Detecting and preventing fraud in the technology sector
The varying types of fraud schemes that can occur across industries present different challenges to companies. For example, revenue fraud can occur in a number of ways, such as recording sales transactions with fictitious customers or accelerating the recognition of certain sales to meet earnings targets. This type of fraud is facilitated by pressure, which can come from internal sources such as bonus payments tied to revenues or pressure to deliver specific financial results, and opportunity, which can be created by inadequate or ineffective internal controls.
A leading bank, for instance, used a combination of analytics and a customer-centric approach to help bridge the gap between its fraud prevention processes and customer expectations. Instead of simply denying transactions that flagged for possible fraud, it sent customers in certain segments mobile notifications to let them verify their identities, which greatly reduced false positives and helped the bank improve customer service and loyalty. The resulting strategy enabled the bank to reduce losses and operating expenses while creating a stronger client experience.