If you’re building a fintech or digital bank today, you probably don’t need another deck explaining why AI matters. You already know why it does.
You might even already have:
- A credit scoring model in production
- A fraud engine with machine learning under the hood
- Some experiments with chatbots, or even generative AI for support scripts
And yet, you might be left feeling like you’re missing out on a lot of the value you may have expected from AI. You’ve got smart people, solid models, maybe even access to what everyone calls the best AI tools for enhancing digital banking. But day-to-day, you’re still fighting:
- Stale or partial data
- Fragile pipelines
- Limited visibility into what’s really happening in production
That’s because the core problem usually lies in the data foundation and how tightly (or loosely) everything is connected to your core.
This piece is for product, risk, and engineering leaders who want to make AI useful without rebuilding their entire bank.
Which AI is best for digital banking services?
Search results today are filled with banks asking and vendors answering:
- What are the best AI tools for enhancing digital banking?
- What’s the best AI technology for digital banking?
- Which AI is best for digital banking services?
- What’s the most effective AI for online banking?
Which can make it easy to think your next breakthrough will come from choosing a different model, framework, or vendor. In practice, the pattern we see is the opposite: the teams that are leading the future in AI for banking are the ones that treat data access, quality, and observability as first-class product features.
If a company’s training data is stale, online features are inconsistent, and production signals are hard to trace, their new AI functionality is not going to work. So instead of starting with “Which AI is best for digital banking services?” The better question is: how do we give our existing AI a clean, reliable, and constantly updated view of the business? That’s a data problem and it starts at the core.
Why your current AI isn’t delivering what it should
Most fintechs and digital banks hit similar issues once they move beyond their first MVP:
Training vs. reality drift
Models often train on a clean historical dataset, then get deployed into a messy, evolving production environment:
- New products added
- Fields repurposed
- Edge cases not represented in training data
There’s no simple way to reconcile what the model thinks the world looks like with what’s actually happening right now.
Fragile data plumbing
Maybe you have:
- Custom ETL jobs pulling from your core
- A semi-manual pipeline into a warehouse or data lake
- A separate pipeline for your fraud provider or scoring engine
Any schema change, new product, or performance hiccup can ripple through everything. Suddenly your most effective AI for online banking tool is offline because a column name changed in the core.
Gaps between teams
Risk, product, and engineering often look at slightly different numbers. Support and ops see yet another view. Nobody is fully sure which metrics match what the models are seeing.
The result is that AI becomes something that happens somewhere in the background instead of something everyone can reason about and iterate on.
The data foundation you truly need
If you strip away the marketing, modern AI for digital banking comes down to a few essentials:
- Reliable access to production-grade data
- A way to work with that data without touching the live core
- A consistent, reusable path from raw data into features into decisions into metrics
That’s where Database Access comes in for Oradian customers. Database Access provides a secure, always-fresh view of your core. With Database Access, you get:
- A secure, read-only replica of your production PostgreSQL database
- Full-fidelity, continuously updated data
- No extra load on the live core, so you don’t risk downtime or performance degradation
From there, your team can plug that replica into:
- Your data warehouse or lake
- Notebooks and ML platforms for experimentation
- Fraud engines, scoring services, and decisioning tools
- Your feature store and monitoring stack
Instead of bespoke pipelines from the core to each destination, you have one high-quality source of truth feeding everything.
AI use cases that benefit from better data
Once the data foundation is in place, you don’t need to hunt for exciting new AI products, you can start by upgrading what you already have. Each of these use cases are solid examples of areas where AI heavily benefits from better data.
Risk & credit decisioning
- Better features from core data support information around repayment patterns, utilisation behaviour, product combinations, channel usage.
- Use Database Access to generate richer features and keep them up to date in your scoring system.
- Monitor model performance closely with live-like data rather than periodic exports.
Fraud & anomaly detection
- Feed your fraud engine a more complete view of transactions, devices, accounts, and customer history.
- Use the replica to run new fraud patterns in shadow mode before pushing them live.
- Shorten the loop between pattern detection, rule/model update, and impact measurement.
Collections and retention
- Rank customers by their probability to self-cure vs need intervention.
- Combine behavioural signals (e.g. app logins, partial payments, product usage) with core data.
- Let AI decide who to call or message first; let humans decide how to engage.
Personalisation and offers
- Use core transaction data, balances, and tenure to find micro-segments.
- Serve more relevant limits, instalment options, or product recommendations.
- Tie this directly into product experiments and A/B tests using the same data.
Where RPA and AI in banking actually fit
A quick note on RPA and AI in banking, because this gets blurred a lot.
- RPA (Robotic Process Automation) is great at mimicking human clicks and keystrokes across systems. It’s useful for stitching together legacy interfaces and automating repetitive tasks.
- AI, in this context, is about making better decisions: predicting, ranking, classifying, and recommending based on patterns in data.
For most fintechs and digital banks:
- Use AI to decide what should happen (approve, decline, escalate, prioritise, offer).
- Use RPA or orchestration to execute how that decision gets applied across systems.
Both work better when they’re grounded in a clean, trusted representation of your core data rather than when each bot or model is working from its own fragmented view.
A plan to get more from the AI you already have
Ready to get started? Here is a realistic approach for an Oradian-based fintech or digital bank.
Phase 1: Turn your core into a usable data source
- Enable Database Access on your Oradian tenant.
- Stand up the secure, read-only replica of your production database.
- Connect it to your warehouse/lake and core analytics tools.
- Audit what your current models and vendors are actually using for input today.
Goal: One shared, trustworthy stream of production-grade data for all your AI and analytics work.
Phase 2: Upgrade one existing use case
Pick one high-impact area you’re already using AI in such as credit risk, fraud, collections, or CX. Then:
- Use the replica to build richer features and cleaner datasets.
- Retrain or reconfigure your existing model using this improved data.
- Run the new version in shadow or limited-traffic mode, comparing old vs new.
Goal: Show that better data + the same (or only slightly improved) model can outperform your current setup.
Phase 3: Operationalise and make it visible
- Push the improved model/logic fully live (with guardrails).
- Surface its impact in dashboards your leadership already trusts.
- Document the pattern
- Use that pattern as the template for the next AI enhancement.
Goal: Turn AI projects into a repeatable data into model into decision into metric loop.
Oradian: the base of your AI stack
Here’s the honest answer to what the best AI for digital banking services is: there is no single best AI for everyone. Instead, the most effective AI for online banking is the one that’s:
- Fed by clean, reliable, production-grade data
- Easy to monitor, audit, and iterate
- Tightly integrated with your core, channels, and decisioning flows
If you’re running Oradian, Database Access gives you that missing layer:
- A secure replica of your production PostgreSQL database, always up to date
- No additional performance risk to your live core
- A clean, reusable data backbone for any AI tool, engine, or model you choose
You can still pick whichever modelling framework, vendor, or AI technology for digital banking you prefer. The difference is that now, they’re all working from the same, solid foundation.
Ready to lead the future in AI in 2026?
If you’re already investing in AI but suspect you’re not getting full value from it, the fastest way forward usually is figuring out a better way to use the data and models you have. With Oradian and Database Access, you can:
- Turn your core into a live, trusted data source
- Strengthen the AI you already run in risk, fraud, and CX
- Launch new pilots in weeks, not quarters
If you’d like to explore what that could look like in your stack, reach out to vanda.jirasek@oradian.com with the AI use case you care about most right now and the metric that would prove it’s working. From there, we can work with you to map a plan that makes your AI measurably more effective without rebuilding everything from scratch.
