AI-powered decision intelligence is reshaping finance

Artificial intelligence is reshaping financial services, driving automation, smarter decision-making and greater efficiency. As financial institutions seek greater transparency and reasoning in their AI applications, AI-powered decision intelligence is emerging as a critical capability.

According to theCUBE Research’s latest analysis, discussed in “The Next Frontiers of AI” podcast, emerging AI frameworks — most notably, retrieval-augmented generation models, causal knowledge graphs and AI reasoning — are reshaping how financial institutions navigate an increasingly complex, dynamic landscape.

The financial sector has long embraced cutting-edge technologies, leveraging AI to optimize risk management, automate processes and improve customer interactions, according to theCUBE Research’s Scott Hebner. However, as businesses look beyond traditional predictive models, they seek more advanced AI capabilities that provide greater transparency, reasoning and AI-powered decision intelligence.

Hebner, the podcast’s host, was joined by Jayeeta Putatunda, lead data scientist, director – AI center of excellence, at Fitch Group Inc., located on Wall Street. “Financial services have always led from the front in predictive analytics and deterministic models, but we must be cautious in our approach to gen AI,” she said. “Given the industry’s regulatory nature and the high stakes involved, we are adopting AI carefully, ensuring governance and risk control at every stage.”

How AI-powered decision intelligence is transforming finance

AI in financial services is rapidly evolving beyond basic automation and predictive analytics, according to Putatunda. Financial institutions increasingly focus on AI-powered decision intelligence to shape strategy and drive results.

“We need to solve use cases that actually drive the most value to our clients, users and even our internal teams,” Putatunda said. “If we can help with operational efficiency, reduce manual workload or enhance deep research, we will win in a significant way.”

Ensuring transparency and explainability is a key challenge in implementing AI in finance, according to Putatunda. Traditional AI models often function as “black boxes,” making it difficult for financial leaders to trace how decisions are made. As a result, many institutions are turning to AI-powered decision intelligence to improve visibility into the decision-making process.

“One of the biggest areas of concern is explainability,” Putatunda said. “In predictive models, we had processes to trace back decisions, conduct weight analysis and determine which inputs had the most impact. With AI, it becomes harder to establish that level of transparency.”

Building trust and governance in AI-driven finance

Trust remains critical in AI adoption within financial services, especially given the industry’s stringent regulatory requirements. The integration of knowledge graphs and causal AI can help enhance transparency, explainability and governance, according to Putatunda.

“Causal knowledge graphs create a dynamically adaptable data lineage that allows LLMs to ground their outputs in factual, explainable relationships,” she said. “This improves AI transparency and enhances compliance and governance frameworks.”

Additionally, AI models need to ensure they are free from biases and provide consistent, reproducible outputs. Unlike other industries, financial institutions require AI models that adhere to strict auditability and regulatory compliance measures, according to Putatunda.

“Financial firms need models that are not only accurate, but also auditable and traceable,” she said. “We must build a safe, sustainable AI pipeline that integrates human oversight at every stage.”

Looking ahead: The future of AI in financial services

The next wave of AI adoption in finance will focus on creating integrated AI ecosystems that combine multiple intelligent agents. These agents will collaborate on complex problem-solving, according to Putatunda.

“We need to move beyond single-task solutions and create goal-based AI agents that can dynamically retrieve and analyze information from multiple sources,” she said.

As RAG, causal AI and decision intelligence evolve, financial institutions can innovate while ensuring compliance and risk control. As AI technologies develop, they will redefine how financial services operate and set the stage for broader applications across industries.

For a deeper dive into this discussion, part of  “The Next Frontiers of AI” podcast series, check out the full conversation:

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