How are Nigerian banks using AI for fraud detection and to keep digital users
In this article, we take a look at how Nigerian banks are using AI for fraud detection and to keep digital users. Give it a quick read to see how you can cut fraud.
Case Study: FairMoney
Find out how Oradian's API-first, cloud-native core banking platform transformed FairMoney’s core banking infrastructure, making it possible to launch the products that would secure FairMoney's spot as one of Africa's most popular digital banks.
From too much downtime to 99.9% uptime
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Discover more insights Core banking RFP: Procurement’s guide to buying an AI-ready core
This guide is written for procurement teams running a core banking RFP who want to be sure they select an AI-ready core banking platform.
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.
90-Day AI pilot planning template
This 90-Day AI Pilot Planning Template helps banks turn AI from a slide in a board deck into a live, measurable pilot.
In four simple sections, it guides your product, tech, data, risk, and compliance teams to align on one realistic use case, the data it needs, and a safe 90-day timeline.
It also covers governance, success metrics, and rollback plans so you can move fast without adding uncontrolled risk. Download the template to leave your next AI discussion with a concrete plan.
The digital-first bank’s guide to AI in 2026
Financial institutions are pouring $97 billion into AI by 2027, but here's what nobody tells you: 95% of AI projects fail to deliver on their promises. The reason? Bad data. Not bad algorithms, not insufficient budget, just terrible, siloed, inaccessible data that no AI can work with.
Before you buy another chatbot platform or fraud detection system, ask yourself: can you actually access your own banking data in real-time? Can your data scientists query your core without crashing production systems? Do you have a single source of truth, or are you duct-taping together CSV exports and hoping for the best?
This guide shows you how to build the foundation that makes AI actually work. We cover everything from credit scoring with alternative data to operational automation that cuts costs by 40%. But most importantly, we show you why your data layer matters more than any algorithm and how to fix it before you waste money on AI that goes nowhere.
Think bigger. Go further.
Come and see the future with us. Talk to one of our core banking experts.