Using AI and ML to Win Against Fraud

Artificial intelligence (AI) and machine learning (ML) are undoubtedly the most popular technologies driving transformation across all markets and disciplines.

AI and ML can make sense of vast amounts of data to drive intelligent decision-making. Thanks to this, businesses can streamline internal processes to increase performance efficiencies and offer new, innovative solutions and services to their customer base.

Financial services is one of the sectors where AI and ML are having the most impact, especially when it comes to fighting fraud, which is continuing to grow as cybercriminals find new ways to access customer accounts.

Account takeover is one of the most challenging fraud types for consumers. This type of fraud grew considerably in 2018: the number of attacks tripled in the previous 12 months and losses reached $5.1 billion.

Significant sums are at stake, for consumers and financial institutions alike. How will AI and ML help to fight the increasing fraud threat?

Accurate Data Analysis

One of the most appealing features of ML algorithms is that they can analyze large bulks of transaction data and signal suspicious transactions with highly accurate risk scores in real-time. These complex patterns are difficult for analysts to identify, but can easily be detected by a risk-based analytics approach. Hence, banks and financial organizations are far more operationally efficient while detecting more fraud.

The algorithms take into consideration multiple elements, among which the customer’s location, the device used and other contextual data points to develop a precise picture of every transaction. This approach improves real-time decisions and better protects customers against fraud, all without impacting the user experience.

This trend will continue over the coming years. Thanks to significant technological development in this area, organizations will increasingly rely on machine learning algorithms to decide which transactions are suspicious.

Free Fraud Analysts

New cyberthreats are developing so fast, and combined with vast amounts data to analyze, it has become nearly impossible for fraud analysts to identify anything that looks suspicious in a timely fashion. An innovative approach that enables an agile analysis and extraction of cross-channel data while detecting fraud in real-time is required.

With AI, data analysis is completed in milliseconds, efficiently detecting complex patterns that can be difficult for a human analysts to identify. This reduces the amount of manual work spent on monitoring all transactions, because fewer cases require human attention.

The quality and efficiency of fraud analysts’ work also increases as their workload becomes more manageable. By removing the burden of time-consuming tasks, they can focus on the most important cases, for example, when risk scores are the highest. This reduces cost of anti-fraud operations, and increases the efficiently rate of successfully processed, genuine transactions due to better risk assessment.

Reduce False Positives

The term ‘false positive’ has become closely associated with the industry’s attempts to fight fraud. As such, one of banking’s biggest challenges is to minimize the amount of false positives being generated, thereby saving time, money and avoiding needlessly frustrating customers.

The prevalence of false positives can be drastically reduced with AI and machine learning. The technology is capable of analyzing a much larger set of data points, connections between entities and fraud patterns, including fraud scenarios not yet known to fraud analysts. This means fewer customers will be falsely rejected for fraud concerns, in turn minimizing the labor and time costs associated with allocating staff to review flagged transactions.

Detect Attacks

As previously mentioned, ML algorithms are able to recognize patterns in vast amounts of structured and unstructured data. This makes them significantly better than humans at detecting new and emerging fraud attacks.

Whether it’s the ability to predict traffic spikes from unusual sources, or build up detailed profiles of customers to detect anomalies before they develop, more effective attack detection is one of the key benefits offered by AI and ML. And, as these tools become more powerful, the outlook for banks and financial institutions will improve exponentially.

Meet Regulatory Compliance

In today’s digital banking ecosystem, a fraud prevention system based on manually defined policies and rules can no longer keep up. If they want to stay ahead, financial institutions need a fraud detection solution that leverages AI through supervised and unsupervised ML.

Machine learning allows organizations to analyze data with context across devices, applications and transactions, and requires very little manual input. Hence, policies can be continually changed, which is key for maintaining regulatory compliance over time (i.e. PSD2). This can save banks time and minimize the potential of costly fines.

Ultimately, it’s important to remember that these different elements can’t be considered in isolation. They are all key pieces in the overall fraud prevention puzzle, coming together to help the banking industry protect customers and fight financial fraud, the multibillion-dollar problem.

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Tim Bedard

Tim Bedard

Tim Bedard is responsible for OneSpan’s Trusted Identity Platform security solutions for financial services. With more than twenty years of IT security experience, Tim has successfully launched multiple cloud-based security, compliance and identity and access management (IAM) offerings with responsibilities for strategic planning to go-to-market execution. Previously, he has held leadership positions in product strategy, product management and marketing at SailPoint Technologies, RSA Security and CA Technologies. Tim is active security evangelist at industry leading tradeshows and events.

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