Banks push for cost-effective, multimodal AI tools

Financial institutions are moving beyond pilot projects to implement production-grade, explainable and cost-effective AI solutions that can meet operational and regulatory demands. AI has evolved rapidly since fintech Arteria AI was founded in 2020, Amir Hajian, chief science officer, tells Bank Automation News in this episode of “The Buzz” podcast. The company provides banks with AI-powered digital documentation services. “2020 was a very simple year where AI was classification and extraction, and now we have all the glory of AI systems that can do things for you and with you,” Hajian says.  “We realized one day in 2021 that using language alone is not enough to solve [today’s] problems.” The company began using multimodal models that can not only read but search for visual cues in documents. AI budgets and strategies vary widely among FIs, Hajian says. Therefore, Arteria’s approach involves reengineering large AI models to be smaller and more cost-effective, able to run in any environment without requiring massive computer resources. This allows smaller institutions to access advanced AI without extensive infrastructure. Hajian, who joined Arteria AI in 2020, is also head of the fintech’s research arm, Arteria Cafe. One of Arteria Cafe’s first developments since its creation in January is GraphiT — a tool for encoding graphs into text and optimizing large language model prompts for graph prediction tasks.  GraphiT enables graph-based analysis with minimal training data, ideal for compliance and financial services where data is limited and regulations shift quickly. The GraphiT solution operates at roughly one-tenth the cost of previously known methods, Hajian says. Key uses include: Regulatory compliance; Anti-money laundering; Predictive modeling; and Low-data problem solving. Arteria plans to roll out GraphiT at the ACM Web Conference 2025 in Sydney this month.