Financial Services Must Move Beyond Pattern-Matching AI, Executive Says

Financial institutions use machine learning for credit scoring, fraud detection, anti-money-laundering surveillance, risk models and customer segmentation. Vallikat Peethamber, founder of VectorPeak Technologies, argues that most systems remain sophisticated pattern matchers trained on historical associations. That limitation becomes critical when markets shift or regulators demand an explanation for a credit or risk decision: correlation alone cannot show what caused an outcome, what a policy intervention would change, or what would have happened under different conditions.

In an article published by Finextra on Aug. 25, 2026, Peethamber proposed a “Thinking Enterprise” architecture with separate channels for content and structural relationships, coupled with causal graphs, intervention analysis and counterfactual reasoning. He said the approach could support novel fraud detection, credit risk, AML and sanctions compliance while aligning with the EU AI Act, the Bank of England’s model-risk principles, DORA and the FCA’s Consumer Duty. The article announced no customer deployment, investment amount or implementation timetable, framing the idea as an infrastructure shift rather than a product launch.