Machine Learning And Cyber Security
Machine Learning And Cyber Security Fraud attacks are becoming increasingly advanced, reaching record levels and scale of attacks across the world.
A study by the PWC Global Economic Crime and Fraud Survey showed that 49% of major businesses recently surveyed across 123 territories have been exposed to some form of fraud attempt. Hackers have access to increasingly sophisticated technology, and are backed by powerful organizations including state organizations and organized crime.
In 2017, a new malware specimen emerged around every 4.2 seconds. And now, fraud breaches have an added layer of complexity by using machine learning algorithms in their attacks.
While the cyber-security market is rapidly evolving to cope with and defend against these attacks, the speed and unpredictability of machine-learning based attacks makes it difficult for regular systems to keep up. Simple predictive models, rule-based detection systems, and manual methods are insufficient for tracking attacks based on AI. Instead, security systems need to incorporate machine-learning technology, which will improve efficiency and fraud detection by removing the need to continually monitor the fraud system.
Supervised machine learning looks at historical data and finds patterns beyond regular predictive analysis, while unsupervised machine learning looks at relationships among emerging variables and future insights. Machine-learning driven systems allow for combined supervised. And unsupervised machine learning that can quickly analyze large volumes of data. Furthermore, and find patterns and make decisions without relying on rules.
AI can categorize information according to different variables, including product lines and selling season. In addition, all of the anti-fraud decision-making can occur in real time and continuously. Instead of having to wait weeks for chargebacks and online fraud recognition. Analysts also receive a well-rounded report on the case, and can immediately evaluate a transaction with its historical context.
AI-based fraud recognition is also flexible. Companies do not need to double down on harsh regulations after encountering fraud; they can let their systems do the work and keep their clients’ experience familiar and positive. AI systems can also provide faster responses based on their analysis.
Programs like Omniscore, by Kount, uses predictive machine learning to generate a risk score. Companies can set threshold limits on variables such as payment rejection, chargeback limits, and risk reduction, and provide immediate response if the AI-generated score falls within thresholds. For example, instead of making clients wait for verification for a purchase, a machine-learning system can immediately process a transaction based on the risk score.
This also reduces false positive alerts, such as credit card declines, allowing for a smoother client experience. Finally, incorporating regulations and business policies into AI-scoring can be an efficient way to prevent business fraud.
Increasing digitalization and interconnectivity means that businesses need to find ways to scale their operations with consumer protection. Electronic transaction-based revenues gaining ground on cash-based transactions. McKinsey estimates that global digital commerce volume will reach over $6 trillion by 2022.
As consumers increasingly embrace mobile and digital commerce, and businesses deal with large volumes of data and channels, real-time solutions are required for fraud prevention. Machine learning is offering a way to reinvigorate previous strategies in fraud management.
Machine Learning And Cyber Security