Fraud is not a security problem. It is a growth problem. Every digital bank that has lost customers after a fraud incident, watched a trusted user churn following a disputed transaction, or seen onboarding drop-off spike after a false positive knows this to be true.   Fraud presents a major growth issue and the question you need to ask yourself in 2026 is not whether fraud will affect your growth, it is whether your infrastructure is capable of stopping it before the damage becomes permanent.  Digital banking in Nigeria, Indonesia, the Philippines, and similar emerging markets is growing fast. Internet penetration in Indonesia sits at 66.5% of the population, mobile adoption in Nigeria is at 87%, and across Southeast Asia, 48% of banks expect to see a reduction in branch numbers as digital adoption accelerates. With that growth comes an expanding attack surface. Cybercrime is now projected to cost trillions in damages in a single year, and the consequences for individual institutions are severe: one in three consumers say they would switch banks over an outage, and one in five say they have already switched banks at some point in their lives because of a fraud incident.  That last number is the one that should be kept in front of every growth conversation. 

Why fraud kills growth, not just trust 

The instinct is to treat fraud as a risk department problem. Risk sets the rules, operations investigates the cases, and the growth team carries on acquiring users. But in practice, fraud affects growth at every stage of the customer lifecycle.  At acquisition, overly aggressive fraud rules create friction that kills conversion. Legitimate users are declined at KYC, flagged during onboarding, or blocked on their first transaction.   They do not complain.   They leave.   At engagement, a single fraud incident that is handled slowly or communicated poorly can undo months of relationship-building. Users who feel exposed or ignored do not churn quietly, which results in them warning others.   At retention, institutions that cannot investigate and resolve disputes quickly lose high-value customers disproportionately, because those customers have the most options.  Fraud is also a data problem in the most direct sense. The institutions that catch fraud early are the ones with a complete, unified view of customer behaviour across every channel and product. The institutions that miss it are the ones where transaction data is siloed, delayed, or unavailable to the systems trying to detect anomalies. 

What modern fraud detection actually requires 

Rule-based fraud detection had a reasonable run. The problem is that the rules are public. Fraud patterns evolve faster than rule sets can be updated, and in emerging markets where transaction volumes are growing and new payment rails are being adopted rapidly (QR payments, instant transfers, embedded lending) the attack surface is changing continuously.  AI-based fraud detection works differently. Rather than checking transactions against a fixed list of known bad patterns, machine learning models analyse the full behavioural context of a transaction: the device, the time, the location, the amount, the relationship to prior activity, and dozens of other signals simultaneously. AI systems have demonstrated a 90–99% accuracy rate in fraud detection, compared to the 35–70% accuracy rate of traditional rule-based models. This difference is the gap between catching fraud in real time and discovering the damage after it is done.  The example is instructive: if a normally low-balance savings account in Manila suddenly attempts a large transfer from a new device at 2am, an AI model can instantly flag the transaction, block it, or route it for human review without friction for the tens of thousands of legitimate transactions happening at the same time.   False positives are their own growth killer and every legitimate transaction blocked is a customer who experiences your bank as an obstacle. 

The infrastructure problem underneath the fraud problem 

Here is where most digital banking fraud conversations stall: institutions recognise that AI-based detection is the right direction, but they cannot get there because the data foundation is not in place.  Effective fraud detection requires a complete, unified view of customer accounts, login activity, and transaction history. That data needs to be available in near real-time, accessible to analytics and model-serving systems without adding load to the live core and connected across every channel so that an agent transaction, a USSD interaction, and a mobile app action are treated as part of one consistent relationship.  For institutions running on legacy cores, that data is fragmented, delayed, or simply inaccessible to the teams and systems that need it. The fraud model is only as good as the data feeding it and incomplete or delayed data feeds lead to missed detections and false alarms in equal measure.  This is why fraud readiness and AI readiness are the same conversation. A secure, read-only replica of the production database is what gives fraud detection systems the raw material they need to perform. It is also what allows institutions to run fraud models in shadow mode before deploying them in production: testing detection algorithms on real current transactions to see how they perform without impacting customers, then shortening the loop between discovering a new fraud pattern and updating the system to respond to it. 

What a fraud-ready institution looks like in practice 

The practical markers of a fraud-ready institution are not exotic. They are mostly infrastructure and process questions: 

• Can your fraud team investigate a disputed transaction in hours rather than days? That requires governed, real-time access to core data, not a ticket to the core banking vendor. 

• Can your detection systems update in response to new fraud patterns without a full release cycle? That requires a core with programmable surfaces and open APIs, not a locked-down legacy system where every change requires months of custom work. 

• Can you run a fraud detection pilot in isolation, measure its accuracy against your actual transaction history, and deploy it with a clear kill switch? That requires the combination of a modern data layer and the internal governance discipline to design pilots properly: clear success criteria, documented approvals from risk and compliance, and fall back paths if something does not perform as expected. 

• Can you communicate with customers in real time when something goes wrong? Trust recovery after a fraud incident depends almost entirely on speed and transparency. Customers who are informed quickly and resolved fairly are far more likely to stay. Customers who wait days for a response, or who never find out why a transaction was blocked, do not come back. 

The growth framing 

The reason to frame fraud as a growth problem rather than a risk problem changes what gets prioritised, who owns the conversation, and which investments get approved.  A CRO presenting a fraud budget to a CFO is asking for cost but a CTO presenting fraud infrastructure to a CEO as a prerequisite for sustainable growth at scale is asking for capability. The second conversation is more likely to unlock the right investment, and it is a more accurate description of what is actually at stake.  In 2026, digital banking growth belongs to institutions that are closest to the user: those that understand who they are serving and can deliver reliable, trustworthy experiences at scale. Fraud readiness is not a constraint on that ambition. It is the foundation it rests on. 

Ready to assess whether your institution is fraud-ready?  

Download Oradian’s Digital Bank’s Guide to AI in 2026 for a practical AI readiness checklist, including the data and core infrastructure requirements for fraud detection that works  Get the guide here.

Start using AI effectively with the right core and data-layer backing you 

Ready to move out of pilot and into action? Speak to our team about what a fraud-ready data layer looks like in practice by emailing vanda.jirasek@oradian.com to book a meeting.