ING Bank is blending traditional machine learning with generative AI to boost efficiencies as it innovates with customer needs in mind. 

“We have to pick and choose domains where deploying tech generates high impact and value,” Marco Li Mandri, head of advanced analytics strategy at the $1.2 trillion bank, told FinAi News. “We also blend traditional AI and gen AI in certain processes to make them more efficient and accurate.”

Amsterdam-based ING has deployed gen AI in five key processes since 2023: 

• Customer support; 

• KYC; 

• Lending; 

• Marketing and personalization; and 

• Software development. 

Boosting productivity 

The bank has provided generative AI tools to 5,000 of its software development engineers, boosting productivity by up to 20%, Li Mandri said. 

“If we make our engineers faster, the entire bank goes faster,” Li Mandri said. 

The deployment of gen AI has also enabled customer service representatives to be up to 70% more efficient by reducing call times and accurately solving problems, he said. 

For KYC, traditional machine learning can verify documents and IDs submitted by potential customers while gen AI can go a step further and check if the customer is on any sanctions list, Li Mandri said.  

“We see up to 50% more productivity in those KYC tasks,” he said. 

The bank, which has been developing its own traditional AI models for years to deploy internally, is taking a similar approach with its gen AI models, LiMandri said. He added that ING has “ a significant partnership with Google and for certain use cases we work with them.” 

He added that while “many companies struggle with finding a good problem fit for the tech,” ING has a 90% success ratio implementing gen AI pilots in its operations. 

To calculate the ROI of gen AI, cost reduction and revenue generation are equally important to the bank, LiMandri said. 

“In the first part of last year, we focused on many cost efficiency elements use cases, like contact centers and know your customer,” he said. “Now we are focused on the revenue aspect driven by hyper personalization.” 

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