Five Ways Artificial Intelligence and Machine Learning Can Fight Financial Fraud
One of banking’s biggest challenges is to minimize the number of false positives being generated, thereby saving time, money, and avoiding needlessly frustrating customers.
As two of the most prominent technology buzzwords around, it’s no secret that artificial intelligence (AI) and machine learning are driving transformation across virtually all industries and disciplines. They are helping businesses streamline internal processes to improve efficiencies, make sense of vast amounts of data to drive intelligent decision making, and create new, innovative services to improve the customer experience.
One sector in particular where AI and machine learning are having a major impact is financial services. This is especially true when it comes to fighting fraud, which is continuing to grow as cyber-criminals find new ways to access customer accounts.
For example, account takeover – one of the most challenging fraud types for consumers – grew significantly in 2018, with the number of attacks tripling in the previous 12 months and losses reaching US$ 5.1 billion (£3.9 billion).
So, with such significant sums at stake for both consumers and financial institutions, how are AI and machine learning technologies helping to fight the growing fraud threat?
Accurate Data Analysis
One of the most important features of machine learning algorithms is that they are able to analyze vast amounts of transaction data and flag suspicious transactions with highly accurate risk scores in real-time. This risk-based analytics approach detects complex patterns that are difficult for analysts to identify, meaning banks and financial organizations are far more operationally efficient while detecting more fraud.
The algorithms take into account several factors, including the customer’s location, the device used, and other contextual data points to build up a detailed picture of each transaction. This approach improves real-time decisions and better protects customers against fraud, all without impacting the user experience.
And 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.
Frees up Fraud Analysts
With the acceleration of new cyber-threats combined with vast amounts data to analyze, fraud analysts are in the near impossible task of identifying anything that looks suspicious in a timely fashion. FIs therefore need to take an innovative approach that enables the quick analysis and extraction of cross-channel data while detecting fraud in real-time.
With AI, data analysis is completed in milliseconds, efficiently detecting complex patterns that can be difficult for a human analyst to identify.
This reduces the amount of manual work spent on monitoring all transactions, because fewer cases require human attention. As described on SCMagazine.com, the quality and efficiency of fraud analysts’ work also increases as their workload becomes more manageable, removing the burden of time-consuming tasks and allowing them to focus only on the most important cases, for example, when risk scores are the highest. This reduces the cost of anti-fraud operations, and increases the efficiency rate of successfully processed, genuine transactions due to better risk assessment.
- Accurate Data Analysis
- Frees up Fraud Analysts
- Reduction of False Positives
- Effective Attack Detection
- Achieve Regulatory Compliance
Reduction of False Positives
With the level of complexity in today’s financial infrastructures, the term ‘false positive’ has become closely associated with the industry’s attempts to fight fraud. One of banking’s biggest challenges is to minimize the number of false positives being generated, thereby saving time, money and avoiding needlessly frustrating customers.
AI and machine learning play a big role in this area. Because they are 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 – the prevalence of false positives can be drastically reduced.
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.
Effective Attack Detection
As explained on SCMagazine.com, machine learning algorithms are adept at identifying patterns in vast pools 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 machine learning. And, as these tools become more powerful, the outlook for banks and financial institutions will improve exponentially.
Achieve Regulatory Compliance
A fraud prevention system based on manually defined rules and policies can no longer keep up in today’s digital banking ecosystem. To stay ahead, financial institutions need a fraud detection solution that leverages AI through supervised and unsupervised machine learning.
Machine learning allows organizations to analyze data with context across devices, applications, and transactions, and requires very little manual input. This means policies can be constantly adapted, which is essential 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 the multi-billion dollar problem that is financial fraud.
This article was originally published on SCMagazine.com on March 28,2018.