Back in 1994, Bill Gates made the statement that banking is necessary, but banks are not. That phrase was repeated at the recentUnder pressure to reduce costs, gain efficiencies, and improve their customer experience, the majority of financial institutions clearly see beyond the hype of AI. They are just not always clear on how to actually get value from it.
One way to address the issue might be to revise the traditional cycle prevalent in today’s world of business. Adopting new practices in the financial environment usually starts with creating a use case, implementing necessary infrastructure, establishing regulatory compliance, and then finally aligning to business priorities.
“It’s not an either-or question. We must begin by understanding how predictive and generative AI complement each other, like the left and right sides of the human brain,” said Brüseke. “Predictive AI is like the left side, responsible for logic. It can assign probabilities, categorize outcomes, and support decisions. Generative AI, like the right side, can be used for creative tasks such as writing texts or creating human-quality images in response to prompts or requests.
Using a three-step model to process information, evaluate data to make decisions, and then take action, here’s Alpha Aleph’s recommendation on which type of AI to use for monitoring an account for money laundering. First use predictive AI to sift through high volumes of transaction data for subtle signs and continue using predictive to decide whether detected signals meet the threshold of likely laundering.
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