AI use case prioritisation matrix for banks

Every bank is under pressure to do something with AI, but not every use case should be first.

This matrix helps product, tech, data, risk, and compliance teams decide where to start and what to park.

Use the template to score AI ideas across impact, feasibility, data readiness, regulatory sensitivity, and time-to-value – so you can move beyond hype and focus on use cases that are both realistic and meaningful.

Fill in the form below to download the AI use case prioritisation matrix.

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AI fraud detection guide for digital-first banks in 2026

Fraud in emerging markets just changed. Rule-based fraud detection systems are obsolete. Here's why: more than 50% of fraud now involves AI, but most detection systems can't adapt faster than fraudsters iterate.

The good news is that institutions using AI fraud detection can achieve 90-99% accuracy. But only if they solve data infrastructure first.

This playbook covers everything from deepfake-enabled account opening to fraud-as-a-service marketplaces. But the centrepiece is this: the same institutions that detect fraud in real-time are the ones whose cores process transactions as events, expose customer data through APIs, and let teams investigate without a vendor ticket.

Your fraud tools are only as good as your data layer. This guide shows you how to build it.

Think bigger. Go further.

Come and see the future with us. Talk to one of our core banking experts.