Artificial Intelligence: A Powerful Weapon in the Battle Against ATO
The digital economy has given rise to disruptive business models that make it convenient for customers to transact online. Customers can easily set up and operate accounts online by providing personal information at sign-up and log-in, respectively. This ease and convenience also has a flip side. If fraudsters gain unauthorized access to accounts through data breaches, hacking, phishing, malware, and other methods, they can use the personal information stored in those accounts to commit other types of identity fraud. One of the most malicious forms of identity fraud is account takeover, which wrests all control of the compromised account and the associated payment information into the fraudster’s hands.
ATO is Expensive
Digital businesses are facing financial losses due to account takeover. In addition, they suffer damage to their reputation, which at times can be irreversible. To fight account takeover, businesses can choose to introduce checks and measures requiring customers to prove their identity at every step. But such measures only add to customer friction. Today’s customers expect instant and seamless transactions; and businesses cannot risk customer churn by wavering on these counts. Therefore, businesses must look for efficient ways to fight account takeover without disrupting the customer experience.
Accurately differentiating between a genuine customer and a fraudster is the first step in preventing account takeover attempts. Multi-factor authentication (MFA) and two-factor authentication (2FA) are among the measures that businesses are deploying in addition to username-password combinations for account access. However, using sophisticated evasion techniques, fraudsters are still able to bypass the stepped-up verification mechanisms, take over accounts, and maximize their exploits.
Businesses need smart solutions that can accurately identify dubious log-in attempts and automatically reject them while auto-approving genuine customers. Artificial intelligence has emerged as a powerful technology that can help businesses efficiently fight account takeover. AI-powered fraud-fighting solutions use digital intelligence to evaluate users, assign scores, and approve genuine users in real time. Users with scores below a defined threshold are automatically rejected or flagged for further human review, as per previously defined rules.
Artificial intelligence and machine learning are particularly useful in helping businesses analyze volumes of data to gain actionable insights in real time. Businesses can use these data-backed insights to accurately identify devices used for account takeover. Machine learning models can spot anomalies in account usage patterns that help unearth potential fraud attempts. Further, automated rules can learn from the latest data sets, helping businesses to confidently adapt to evolving and complex fraud.
Simility’s Proven Expertise
Simility, a PayPal service, leverages artificial intelligence to help global businesses efficiently fight account takeover. Simility’s advanced Adaptive Decisioning Platform, features custom modeling, entity resolution, dynamic segmentation, and network link analysis to help businesses effectively mitigate ATO without disrupting user experience.
Some of the features that make the Adaptive Decisioning Platform a perfect solution to help fight account takeover are:
- Device Recon: Powerful Device Recognition technology enables businesses to gather digital intelligence about the devices and networks being used to access an account. Over 350 raw parameters, network references, fraud-relevant computations, and fuzzy unique identifier per device power Simility’s accurate identification of unique devices, networks and the associated risk signals to help identify rogue devices, block repeat offenders, and unearth fraud rings.
- Behavioral Biometrics: As a part of Simility’s Feature Engineering, the Adaptive Decisioning Platform analyzes subtle behavioral patterns of each user to track keyboard dynamics including pressure on the keys, time spent on a web page, and mouse movement. Building on this information, Simility uses industry-specific rules and models to accurately identify a fraudster from a group of genuine users.
- Anomalous Usage Pattern: Users have a certain account usage pattern. Any deviation from a user’s normal usage pattern is indicative of possible fraud. The Adaptive Decisioning Platform unearths anomalies in the user’s account usage pattern and red-flags them for further investigation, thereby helping to prevent accounts from being used for criminal activities.
- AutoML: Leveraging its fraud-fighting expertise, Simility has developed Python and R libraries that multiply the efficiency and provide convenience to fraud analysts. With Simility’s AutoML, businesses can train, test, and set into production Simility AI models within a few clicks. Simility provides rule threshold recommendations and new rule suggestions to catch evolving fraud. And, with Simility’s explainability, businesses can clearly understand what makes a particular case fraudulent.
- Risk-based Authentication: Simility’s real-time responses help businesses confidently auto-approve genuine customers without compromising user experience or account security. Depending on the decision, reasoning, and risk score, businesses request additional information, making the authentication process more comprehensive and stringent for potentially risky profiles, thereby helping to safeguard business interests.
Simility’s layered approach—based on the above features—combined with an appropriate level of human intervention empowers businesses to sustain business growth and confidently fight ATO.
To learn more about how Simility’s Adaptive Decisioning Platform helps detect and block account takeover attempts to help businesses protect revenue, schedule a demo now.
Latest posts by Sharon Lucero (see all)
- Do You Have a Legacy Fraud Solution? Here Are Eight Tell-Tale Signs - October 16, 2019
- Artificial Intelligence: A Powerful Weapon in the Battle Against ATO - June 12, 2019
- Recommerce at Risk: How To Beat The Fraudsters On Resale Marketplaces - May 8, 2019