ould AI be a solution to combat banking fraud? The interest in AI technology in the banking industry continues to grow, with 85% of business decision-makers believing it will add value and advantages to business in the future.

But bank organizations don’t have to (and shouldn’t) wait for the future to start leveraging the advantages of AI as it exists today. AI’s biggest advantages are built over time as algorithms collect more data and “learn” more about how to use it. Therefore, the benefits of AI are much like a savings account or other secure investment: the benefits begin the moment AI is deployed and will continue to grow uninterrupted the more you contribute to it.

Let’s look at how you can start filling the gap between ambition and results using AI for banking fraud detection.

Machine Learning’s Role in the Banking Industry

By definition, machine learning is based on algorithms that can “learn” new information from the data it collects. The more data AI has to work with, the more it learns and the deeper insights banks can receive from their AI technology.

Already, we’re seeing major banks put AI technology into practice that approaches fraud detection from a forward-thinking perspective rather than waiting until after fraud occurs to act. Recent data found that 63% of financial institutions believe that AI can prevent fraud, while 80% agree that AI plays a critical role in reducing fraudulent payments and attempts to commit fraud.

Machine learning technology can be deployed across multiple channels (e.g. transactions, loan applications, etc.) in the banking industry. Truthfully, this is a non-negotiable functionality if banks want to leverage AI to its fullest potential, as the banking industry at large consists of multiple features, functions, and products. As a result, AI can be used to detect fraud in more than one channel simultaneously simply by improving the way it finds anomalies in data over time.

Let’s look at some specific ways AI is currently being deployed in some of banking’s most critical and vulnerable service areas:

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