Liv Bank · Q3_2026_Liv_1

Gap-to-target (lowest first)

What it means

AI strategy is a slide in the CTO's board deck, not a funded program with accountable owners. The closest thing to AI governance is an informal policy about not putting customer data into ChatGPT. Fraud detection is stuck in the rules era with 90%+ false positive rates, drowning investigators while sophisticated attacks go undetected. Credit decisioning leaves money on the table — banks using AI credit models report up to 25% more approvals with reduced defaults, but legacy scorecards remain the standard. Data infrastructure supports one-off analyses but cannot sustain production AI workloads.

Next steps

Commission an AI Value Diagnostic to inventory current spend, map stalled initiatives, and establish ROI visibility. Identify 3 high-impact use cases with quantified business cases — start with the proven trifecta: fraud false positive reduction (40-60% achievable with ML), credit model modernization (25%+ approval rate improvement), and document automation in lending (80%+ reduction in manual review). Establish minimum viable model risk management before scaling further.