How AI Models Streamline Fraud Detection and Analysis

With fraudsters exploiting new technologies and leveraging sophisticated tactics to compromise card credentials and account data, fraud analysts on the front line, like yourself, know they must have tools to move faster and smarter.

Tools that leverage AI models fit the bill for fraud managers looking to streamline fraud detection and analysis efforts. AI-powered models collate vast piles of data into actionable strategies for stopping fraud. All without you wasting hours of your time on analysis.

Artificial Intelligence and machine learning have become buzzwords in the fraud detection space, but not not all fraud leaders understand the vast benefits of this technology as it relates to optimizing your fraud mitigation efforts. Finding the right structure to integrate all the data that fraud managers must analyze into one system can be challenging. AI models set the right foundation to build from.

McKinsey’s research indicates that 31% of activities could be automated with machine learning. AI models are the algorithms that allow fraud managers to tap into the value of machine learning technology. We’ve broken down why your fraud teams should rely on AI-driven models to help streamline fraud detection and analysis.

AI Models Apply Predictive Analysis

AI models capture past patterns to encapsulate that knowledge into a repeatable algorithm to predict future outcomes. AI models allow fraud teams to find greater value deep within data by spotting patterns that aren’t likely to be detected in manual fraud analysis. AI models also help you focus efforts toward deciphering what data is relevant, and what is not — and how to make sense of all that data in order to detect future potential issues.

AI Models Automate Fraud Detection

AI-powered technology is useful because of its ability to automatically process data to create predictive fraud models — enabling issuers to strategically manage fraud loss and reissue management strategies. Automation helps fraud managers cut down on the manual processes that bog down your time and resource investments.

AI models built on machine learning frameworks automatically and quickly learn from patterns of normal behavior. This allows the technology to detect patterns that are out of the norm. For fraud transactions, AI models can adapt to changes to identify suspicious behavior before fraud has actually occurred. AI models also allow fraud teams to analyze transactions with greater contextual review far faster than a human’s mind could spot the same trends.

AI Models Spot Hidden Risks

In addition to reducing time and effort of analytic efforts, AI models can spot fraud trends hidden from human eyes. Moving from reactive to proactive analysis greatly streamlines a fraud team’s ability to get ahead of emerging fraud threats. By capturing and analyzing a past fraud incident, AI models can containerize the knowledge necessary to know  where hidden risks are more likely to occur.

Instead of using fraud detection software that spot fraud as it’s occurring, or after the fact, AI models allow fraud leaders to take a proactive approach to mitigating incidents before they occur. Effective fraud strategies occur when fraud patterns are spotted before the fraud hits your institution, not after the fraud has occurred.

AI Models Capture More Trends

Tools that rely on AI and machine learning models to analyze data with far greater detail and speed is where fraud managers can truly find value in AI models. Relying on AI models allows fraud managers to spot data trends they didn’t even know to look for, which helps them plan for more potential outcomes. AI models help teams decide which data is relevant, and helps provide support to create effective and sustainable fraud mitigation efforts.

Fraud teams rely on data and analytics to empower your fraud strategies, but without the models to create a structure to apply AI and machine learning solutions, fraud managers and analysts will continue to fall short on effectively focusing your fraud management efforts. Through the application of high-performance software, AI models have created the framework for financial institutions to apply advanced computing abilities that have a broad-scale reach to allow financial institutions to create stronger fraud mitigation strategies.

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