How to Fight Fraud Attacks With Machine Learning

Greg Hancell, fraud expert at OneSpan, talks about how financial institutions can fight fraud using machine learning. Hear his insights on why explainable artificial intelligence is important, and how banks can get started with continuous monitoring and contextual authentication.

Building a Fraud Detection Platform using AI and Big Data

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Yuaho Zheng, Director of Engineering at DataVisor, talks about building a fraud detection platform using AI and Big Data at the 2019 AWS Santa Clara Summit.

AI/ML Data Poisoning Attacks Explained and Analyzed-Technical

Adversarial artificial intelligence and machine learning is a growing threat in cybersecurity and data science. Algorithms are vulnerable to new types of stealthy and effective cyberattacks. Threat actors can and have altered the machine learning and artificial intelligence models without ever gaining access to their systems. This is called data poisoning or model poisoning. Find out more.

00:00 The implications
00:56 Data Poisoning Explained
02:20 The Poisoning Threshold
04:21 Attack Objectives
05:07 Backdoor Attacks
07:27 Attack Vectors
10:38 Vulnerability
12:46 Attack Strategies
14:34 Previous Successful Attacks

Sources Cited:
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Machine Learning and AI for Fraud Detection – Learn How It Works

Fraudsters change their attacks based on your business model and risk management techniques. Learn how machine learning and AI can help your defense be up-to-date.

Read our post to machine learning for fraud detection here:

More anti-fraud video resources on:

• Device fingerprinting:
• Data enrichment:
• Fraud detection API:

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Fighting Faster Payments Fraud with Machine Learning

Fraud follows speed. Faster payments leave no time for manual review of transactions and no room for high false positives. Learn how machine learning based fraud detection can mitigate fraud in real-time with low false positives, ensure positive customer experiences, and transform fraud operations from loss-recovery to loss-prevention.