The battle against payment fraud is never ending. It evolves constantly, with hackers creating increasingly sophisticated ways to breach data systems and steal identities. In 2018, malware and ransomware constituted major threats, followed by social engineering attacks and phishing attempts.
Online and mobile transactions are particularly vulnerable to breaches, and passwords and codes can no longer provide adequate security against cyber attacks. Innovative technologies are being developed to meet growing fraud challenges. According to Capgemini, fraud detection systems using machine learning and analytics minimize fraud investigation time by 70% and improve detection accuracy by 90%.
Machine learning fraud detection
Machine learning enables the creation of algorithms that process large datasets with many variables. The system reveals hidden correlations between user behavior and the likelihood of fraud. Machine learning systems enable faster data processing and are less dependent on manual effort.
MasterCard has adopted machine learning to monitor variables such as transaction size, location, device, and purchase data. The system assesses account behavior and offers real-time assessment regarding the nature of the transaction. The system reduces the number of false declines in merchant payments. According to reports, merchants lose about $118 billion annually due to false positives, while customers’ losses amount to nearly $9 billion.
Blockchain for digital identity
One of the main problems related to fraud is that personal data is shared by a wide range of organizations, providing hackers with easy pickings. By definition, blockchain or Distributed Ledger Technologies (DLT) is decentralized. This factor enables a new approach to identity management. Data can be shared across different transactional channels while enabling robust protection of user identities. Users will have the ability to create encrypted digital identities, and the need for multiple usernames and passwords will be eradicated.
Behavioral biometrics for unreplicable authentication
Behavioral biometrics authentication is based on the principle that no two human beings share the exact same behavioral patterns. When it comes to the use of mobile phones, each user has their own typing pattern, swipe speed and finger pressure. Through machine learning and by analyzing behavioral patterns, smart algorithms can continuously authenticate user identity in the background of a session without interrupting the user experience.
User behavior analysis
One of the classic methods to detect fraud is to track deviations from usual customer behavior. A smart fraud system will pick up anomalies such as high values and unusual locations, new IP addresses, time of day, changed shopping patterns, and more. After detecting these activities, an advanced algorithm will raise a flag and assign a higher likelihood of fraud. The system will then send a verification request to the card owner in real time.
All of these methods constitute highly effective tools to fight sophisticated fraud. Small and medium-sized companies may find it more affordable to engage external data science experts or acquire third-party software rather than building an in-house team.