Catching fraud? We'll catch more.

AimAnomaly uses deep learning to recognise even the subtlest of abnormalities that could signal fraud, so you can stop it before it happens.

Protect against bad accounts & defrauding

Whether it’s large-scale or manual fraud you’re fighting, AimAnomaly has you covered.

Fraud is a shape-shifting, endless concern. Plug one leak and another springs; synthetic identity fraud, first party abuse, stolen credentials and more, each with the single, unwavering objective of personal gain, at the expense of your business or reputation. And these aren’t insignificant figures. In one report, it was estimated that a third of ecommerce account creations in Q1 2018  were fraudulent, compared to just 2.1% in financial services, in the same quarter.

AimAnomaly Detection uses supervised and unsupervised learning to flag abnormalities than could signal fraud, from the large scale right down to the manual onboarding stage. Even if you haven’t pinpointed the data patterns yet, we help you isolate and immobilise fraud, before it gets let in the door.

AimAnomaly: Benefits to you

Detects more fraud

Our models are trained on your specific UI, and the application of deep learning means that entire sessions are monitored, for greater accuracy and performance.

Reduces processing overheads

Richer learning from larger datasets means a more accurate risk classification capability. Apply investigative resources more efficiently to suspicious enrolments or transactions.

Simple integration into risk engine

Our risk-based return scores can easily be integrated into your risk engine, from a simple 'if' logic to a more comprehensive platform. We integrate into a wide range of IAM, ID&V and Orchestration platforms.

Full service step-up & step-down

AimBrain has a unique suite of 100% proprietary authentication tools. Use behavioural analysis throughout a session and step-up to active authentication using our face, voice and video/audio modules.

AimAnomaly Detection: Packed full of features

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Uses ensemble learning

Using a variety of machine learning and deep learning models, we find the fraud patterns that others can't.

Stops fraud across any channel

Bad actors aren’t limited to one channel, so neither should your anomaly detection be. Deploy our tech across any web or mobile channel.

Standalone or add-on authentication

Use AimAnomaly Detection on its own, or integrate it with other authentication tools as a step-up process where you need additional user verification.

Comprehensive fraud reports and bot alerts

Receive an overall anomaly score plus individual itemised scores for each class you require, to build an evolving picture of attack profiles.

Low footprint technology

We send lightweight JSON scores back to you in real-time, for consumption into your business logic or fraud orchestration platform.​

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Broad behavioural monitoring

Detect anomalous behaviour and then continuously authenticate customers in-session using AimBehaviour to protect against ATO fraud.

Recognise and stop fraud before it happens with AimAnomaly Detection

How is fraud damaging your business today?

Try us for free with our dashboard

hear our technology developments first

Tell us how fraud attacks, and we'll design the solution.

Our expertise in deep learning means that if there’s a fraud pattern to be found, we’ll find it and flag it. 

New account fraud

In 2017, NAF grew by an incredible 70%. Spot signals that suggest fraud at the point of registration, such as familiarity or multiple account registration, across mobile or web applications.

Instant credit abuse

As fraud detection improves, criminals are quick to target more accessible products such as short term loans. Use behavioural monitoring to check for signs of fraud before issuing credit.

Account takeover

ATO fraud, whether via breaches or malware, exploits the vulnerability of trusting knowledge (passwords and usernames) and possession (devices). Spot behavioural changes even in seemingly-authenticated sessions.

Claims submission

With one insurance scam per minute in the UK alone, it's critical to stem the flow of abuse. Recognise suspicious submissions, based on behavioural human-machine interactions.

Prepaid card fraud

Prepaid card fraud resulted in losses of $500 million to US businesses in 2015, and is on the ascent. We train models to recognise those looking to default on future payments.

Contact us for a no-nonsense chat

AimAnomaly Detection uses fraud data to build and enhance profiles of fraud instances, safeguarding you against future attacks.

Invisible, continuous anomaly detection that works across any channels, keeping you and your customers safe. Contact us for a no-nonsense chat today.

Just the facts

Download the AimAnomaly Detection Fact Sheet (PDF, 1MB)

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