Leveraging AI and ML for Detecting Financial Fraud

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Leveraging AI and ML for Detecting Financial Fraud

CFO Tech Outlook | Thursday, May 23, 2019

FREMONT, CA: There is so much hype around the use of artificial intelligence (AI) and machine learning (ML) in fraud detection but has a long track record of being successful. Machine learning can learn patterns in datasets without the help of a human analyst, and AI has broader applications of analytics from driving a car to identifying a fraudulent transaction. The entire financial sector is most impacted by AI and machine learning when it comes to fighting fraud.

One of the most advantageous features of ML algorithm is that they can analyze large bulks of transaction data and alert suspicious transactions with accurate risk scores in real time. This makes banks and financial organizations far more operationally efficient while detecting more fraud. The algorithms take into consideration the customer’s location, the device used and other contextual data points to develop an accurate picture of every transaction which improves decision making and better protects customers against fraud without impacting the user experience.

Cyber threats are developing so fast, and the vast amount of data has become nearly impossible for fraud analysts to identify anything that looks suspicious. This scenario requires an agile analysis and extraction of cross-channel data while detecting fraud in real-time. With AI, data analysis is done in a flash of a second, which reduces the amount of manual work spent on monitoring all transactions because fewer cases require personal attention.

False positive has become an associated term with the financial industry’s attempts to fight fraud. The biggest challenge of the financial institution is to minimize the number of false positives being created and thereby save on money and time. False positives can be drastically reduced with the use of AI and ML as these technologies are capable of analyzing a much broader set of data points and connections between fraud points. As ML algorithms can recognize patterns in vast amounts of structured and unstructured data, it makes them significantly better at detecting new and emerging fraud attacks. 

A fraud prevention system based on manually defined rules and policies can no longer protect today’s digital banking ecosystem. If financial institutions want to stay away from fraud, they need a fraud detection solution that harnesses AI and ML. They are vital pieces in the overall fraud prevention puzzle that protects customers and fight financial fraud.

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