Fraud comes in all shapes and sizes
From account takeover to loyalty fraud, our fraud prevention tools help you keep your business, customers and data secure in the digital world.
Predictive models that identify the bad and the good.
Breached credentials. Malicious apps. Synthetic identity. Instant credit abuse. Burner phones. Mule accounts. Bot attacks.
Fraud is shape-shifting, highly efficient and quick to exploit – sometimes to devastating effect. Predictions from the insurance industry anticipate a sixfold increase in cyber premiums by 2025, costing businesses up to $20bn, as big brands hit the headlines almost daily as the latest casualties.
So with sophisticated threats now industrialised, it has never been more critical for organisations to detect, prevent and disrupt fraud, quickly and effectively.
Stop more fraud. Today.
Fraud takes many forms.
Anomalous behaviour, bot detection, account takeover monitoring and in-session user authentication with liveness detection. Easily consumed scores feed into your risk engine so you can block, investigate or approve.
We find the patterns within the data, to protect against tomorrow’s attacks.
Detect and stop:
- Automated attacks
- Non-payment of services
- Stolen/synthetic identities
- Account takeovers
- Loyalty fraud
- Draw downs
- Account abuse
We help you stop manual and automated fraud, wherever and whenever it strikes.
With 6.5bn breached account records identified, the threat of account opening fraud using harvested credentials is intensifying.
AimAnomaly prevents new account opening fraud by training models on fraud data and applying deep learning, to build self-evolving fraud detection algorithms. Learn faster and more effectively, to protect against manual and machine-based fraudulent account opening, to stop fraud before it has a chance to attack.
AimBrain also has three active authentication steps designed to check for liveness and prevent spoofing fraud, using facial authentication or lipsync authentication as a step-up security protocol.
New Account Fraud case study
A financial services provider needed to identify new account opening fraud in which fraudsters were using synthetic or breached identity credentials.
AimAnomaly was used to detect fraud patterns and profiles based on historic and new data. Within twelve weeks, we were capturing 75% of all fraud, and 60% of the fraud that the client had never been able to identify.
Kaspersky Lab detected more than 1.3m malicious installation packages in Q3 2018 alone, of which over 55,000 were mobile banking trojans, as advancements in phishing techniques and malware concealed in apps continued to flourish.
AimBehaviour uses thousands of data points to monitor a user throughout a session, by invisibly and continuously comparing their behaviour against their known user template. Risk scores are returned throughout the session and any change in behaviour monitored and flagged, so that additional security steps or session cessation can quickly be enforced.
AimBrain’s active biometric modules – face, voice and lipsync authentication – can be integrated as a step-up security process, to provide strong customer authentication when behaviour falls below minimum thresholds. Each features randomised challenges, to detect liveness and protect against presentation and replay attacks.
A tier two UK bank wanted to protect against account takeover and adhere to PSD2’s strong customer authentication requirements.
Within four weeks, AimFace facial authentication was deployed to provide additional user authentication and protect against account takeover and breached credentials.
Fraud can take down businesses with a single script, and survival relies on fighting both the known and the unknown, detecting and isolating new attacks quickly.
By leveraging machine learning and by drawing on patterns gathered across millions of authenticated sessions, AimAnomaly evolves with each interaction so that new automated attacks can be identified and extinguished, faster than ever before.
A services provider was experiencing a surge in new account generation in response to an acquisition campaign. However, this was exploited by a new type of bot attack, designed to create false accounts to cause future damage.
AimAnomaly was being deployed to check for new account fraud in a specific environment, but the monitoring allowed us to immediately identify this new threat and stop 34,000 attacks over 3 days.
Loyalty fraud, also known as first-party fraud, is systemic for organisations that offer sign-up rewards, as new accounts are opened specifically to take advantage of instant benefits or credits.
Leveraging machine learning and deep learning, AimBrain’s modules can consume vast datasets to create a template of fraudulent behaviour that becomes more accurate with every interaction. Using this, account requests can be red-flagged as early as enrolment, to stop loyalty fraud before it happens.
A tech services provider was experiencing a high volume of new account opening fraud, where genuine credentials were used to open accounts with the intention of defaulting on payments.
AimAnomaly was used to monitor tens of thousands of registrations per day. Within 12 weeks, we were able to stop registrations that would have cost millions of dollars in defrauding.
How does fraud hurt your business?
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Meet the AimBrain Fraud Prevention Toolkit
Spot attacks as early as new account fraud.
Stop and protect against fraud – any channel, any use case, any device. Get in touch today.