ING Bank is planning to deploy agentic AI in its lending processes. 

“In retail, we already use traditional machine learning to approve loans in an instant way,” Marco Li Mandri, head of advanced analytics strategy at the $1.2 trillion bank, told FinAi News. “These models can check creditworthiness of a consumer, past records and approve them instantly.”

Amsterdam-based ING plans to use gen AI for wholesale lending and agentic AI for mortgage lending, he said. 

“We can significantly grow our mortgage operation if we make the process more efficient,” Li Mandri said.  

“We are starting to experiment with mortgage agents to support the entire underwriting process, like assessing documents, validating against existing policy, supporting the analyst in mid-office and back-office tasks to shorten the process.” 

ING originated $8.9 billion worth of mortgages in the third quarter, compared to $6.2 billion in Q3 2024, according to its earnings report. 

AI in lending on the rise 

ING is not the only financial institutions evaluating AI for lending, Ravi Khokhar, global head of cloud for financial services at consultancy Capgemini, told FinAi News.  

“Many customer-facing interactions like KYC, loan applications and underwriting have remained largely unchanged for 10 to 20 years and are poised for AI disruption,” he said. 

FIs that have rolled out AI-driven lending processes include: 

• $1.7 trillion Citi; 

• $486 million Varo; and 

• Fintech Bluevine. 

In fact, 10% of financial services companies are in the process of implementing AI agents for lending and other decision-making tasks, or plan to begin doing so, in 2026, according to a Capgemini report published Nov. 12. 

Banks are cautious about their lending decisions and how they affect their risk profile and portfolio, Khokhar said, adding that agentic AI can take all factors into account in the lending process.. 

Commercial lending is more complex than retail lending for credit cards, Khokhar pointed out. Such complex decisions require agentic AI to evaluate all rules while factoring multiple data points to boost efficiency and reduce risks. 

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