Using Machine Learning to Combat Digital Banking Fraud Part 2

Machine Learning Finds the Proverbial Needle in the Haystack

Machine learning is what enables these behavioral analytics to work in the background, monitoring all of the action and unique characteristics to find those items that are most likely needles in the huge haystack of activity. Behavioral analytics also help to identify hidden or evolving patterns of fraud. Once identified, high-risk activity can automatically trigger an action, such as requiring step-up or multi-factor authentication. It be communicated, often in real-time, to fraud analysts. The risk scoring helps prioritize the accounts fraud analysts need to focus on first, which dramatically improves productivity and fraud detection.

In addition to the alert and score, the fraud analyst can see the risk factors contributing to the score, which aids in their investigation. In short, clients are able to achieve dramatically higher levels of fraud detection while reviewing fewer alerts. This also means that fewer end-customers are potentially inconvenienced by the investigative process. The real beauty of machine learning is that the models learn from past cases, to improve and refine their ability to differentiate criminals from genuine transactions.

Unsupervised Models Find Fraud Faster

Guardian Analytics uses unsupervised behavioral analytics, which provide the following additional benefits:

  • Self-learning – as fraud schemes evolve, the unsupervised model learns and adapts itself
  • No rules to write – no static thresholds to write and have updated in less than a month
  • No threat specific – the unsupervised model learns threat signature as they come

Using unsupervised models means that the financial institution does not have to deliver confirmed fraud tags upon which to train the model. The result is, the client saves a tremendous amount of time and effort and sees a return on their investment faster than before.

Machine learning and behavioral analytics are the keys to successfully identifying suspicious activity and preventing fraud. By maximizing fraud detection while reviewing the smallest number of alerts, financial services companies are able to protect their customers, assets, and shareholders while minimizing the disruption of false positives (when out of pattern activity is actually genuine). In fact, adaptive models learn from this new information, so it will be recognized as genuine for that customer next time. By partnering with a technology company that helps you cost-effectively fight fraud, your team can focus on those suspicious activities that truly warrant their time and skills.

Guardian Analytics is the pioneer and leading provider of behavioral analytics and machine learning solutions for fraud detection and anti-money laundering software for financial institutions and enterprise organizations. Hundreds of financial institutions have standardized on Guardian Analytics’ innovative solutions to mitigate fraud risk and stop the sophisticated criminal attacks targeting retail, commercial, and enterprise banking clients. To learn more, request a demo.