DIGITAL BANKING
What role should AI and machine learning play in making open banking data more actionable for both consumers and businesses?
Erick Watson, CEO, Randamu AI should be used not just for predictive analytics, but to enable context-aware automation. Imagine a financial agent that knows when to refinance your mortgage, renegotiate a subscription or rebalance your portfolio, all based solely on your open banking data and expressed preferences.
To make this safe, explainable AI is critical, especially when tied to high-stakes decisions. We’ re already exploring how verifiable computing and decentralised oracles in Web3 can bring trust to AI outputs, concepts that also have crossover value here.
Data becomes truly actionable only when it’ s processed transparently and in a way users can audit and override.
Krishna Subramanyan, CEO, Bruc Bond A platform-first strategy naturally invites data-driven automation. For example, a platform using ML can automatically flag anomalous transaction patterns, prevent fraud before it scales, automate reconciliation, monitor risk in realtime and trigger compliance checks as transactions flow.
AI is the key to turning raw banking data into actionable and scalable insights. These technologies don’ t replace human oversight but instead make scalable, intelligent decision-making possible at speed. fintechmagazine. com 99