Everyone’s talking about AI. Most decks in banking and financial services now have at least one slide titled AI Strategy. But inside the institution, the real questions sound more like:
- How does AI really help us in practice?
- What’s a safe first step that won’t overwhelm our teams or scare our regulator?
- How can we implement AI when our systems and data were set up 20 years ago, in a world very much not made for AI?
The short answer to all of these is the same: your first AI product should be treated as a data project, not a magic model project.
Without solid data foundations, AI in bank environments is just an expensive experiment.
This guide is for financial institutions in high-growth markets (MFIs, rural and thrift banks, specialist lenders) who want to launch their first real AI use case in a responsible, measurable way.
Why financial institutions struggle with AI
Let’s get the uncomfortable bits out of the way.
Most of the obstacles have very little to do with algorithms and everything to do with the reality of your systems and organisation.
Data is everywhere and nowhere
This looks like:
- Core, LOS, LMS, channels, spreadsheets, manual uploads.
- Different business lines keep their own versions of the truth.
- Nobody is fully sure which numbers are official.
Legacy limitations on the core
You’re told not to run anything heavy on production because:
- Reporting tables weren’t designed for analytics workloads.
- Performance concerns mean you don’t touch the live system unless you have to.
- AI projects end up living on CSV exports and one-off scripts.
This is why AI integration with core banking systems is usually the hardest part, not the model itself.
Compliance and governance nerves
- Fear of black-box decisions regulators won’t understand.
- Limited ability to explain why a model made a decision.
- No clear audit trail for how data was used and transformed.
Capacity and skills
- IT and ops teams are already stretched keeping the lights on.
- Maybe you have one data person, maybe you don’t.
- External vendors talk about the best artificial intelligence software solutions for banks, but don’t live with your internal constraints.
The hype problem
Headlines are full of generative AI in banking, large language models, and futuristic copilots.
In reality, the biggest artificial intelligence banking efficiency improvements usually come from much simpler models (ranking, prediction, classification, decisioning) applied to clean, well-governed data.
None of this means you can’t launch AI, it just means your first step needs to be realistic. tightly scoped, and most importantly, backed by reliable data.
The benefits of artificial intelligence in banking
When AI is built on a solid data foundation, the benefits of artificial intelligence in banking are huge and show up in day-to-day operations.
At a practical level, the biggest benefits of AI in banking typically fall into four categories:
Faster, more consistent decision-making: AI models can pre-score applications, prioritise queues, and flag exceptions so human teams focus where they’re needed most. That means shorter time-to-yes for good customers and fewer delays caused by manual checks.
Better risk and portfolio management: By analysing patterns in repayment, behaviour, and transactions, AI supports earlier identification of risk, more accurate pricing, and smarter limit management, institutions can react to problems sooner instead of waiting for issues to show up in static reports.
Operational efficiency and cost reduction: AI can streamline processes like collections, case routing, fraud review, and service interactions. Even small improvements in these areas compound into meaningful artificial intelligence banking efficiency improvements over time.
More relevant, personalised customer experiences: With the right data access, AI helps banks and MFIs understand which products, limits, or messages are most relevant to each customer segment, leading to better engagement and higher lifetime value.
For most financial institutions, the real benefits of artificial intelligence in banking come from using data more intelligently so teams can make better decisions, faster, and with greater confidence.
What makes a good first AI project?
To see how AI helps banks in a way that leadership will actually believe, your first project needs to feel boringly practical.
A strong first use case usually:
- Uses data you already collect (transactions, repayment history, applications).
- Has a clear owner (risk, collections, operations, customer experience).
- Ties to one or two existing KPIs.
- Has low regulatory or reputational risk if it underperforms.
Examples for traditional institutions
You don’t need to start with full automated credit decisions. In fact, you probably shouldn’t.
Think instead about:
Collections prioritisation: Which overdue customers are most likely to self-cure vs need early intervention?
Queue/case prioritisation: Which applications or tickets should agents look at first today?
Cross-sell/upsell: Which existing customers are most likely to take a top-up or new product?
Operational efficiency: Which tasks or branches create the most avoidable delays?
These are the kinds of use cases where the benefits of AI in banking (faster response, better allocation of effort, improved customer outcomes) show up quickly and are easy to explain. In more conservative markets, position AI as decision support where the model enables better decision-making, but at the end of the day, a person always makes the final decision.
Before the model: build the data foundation
Here’s the hard truth: before you think about the benefits of artificial intelligence in banking, you need to fix how data moves inside your institution.
Across AI in banking and financial services, the institutions that succeed all do some version of the follow first.
Get data out of the core safely
You need a secure, up-to-date, read-only replica of production data. Why?
- You avoid putting extra load on the live core.
- Analysts and data people have room to work without fear of breaking anything.
- Everyone is working from a consistent, repeatable version of history.
For Oradian customers, this is exactly what Database Access provides: a secure replica of the production PostgreSQL database with full-fidelity data and no extra load on the live core ready to plug into your own tools.
Start with one domain
Pick one domain that matches your first use case, for example:
- Loans
- Collections
- SME customers
Connect core data for that domain to at least one or two relevant systems (e.g. collections system, CRM, channels).
Make the goal a simple, unified view that can easily be understood and validated.
Clean, define, and agree
You need consistent data.
- Define what default, cure, good customer, and high risk mean to you.
- Fix obvious issues: duplicates, missing IDs, inconsistent labels.
- Get risk, finance, and operations to sign off on the definitions.
If you can’t produce the same dataset twice in a row, you’re not ready for AI yet; you’re still in basic data engineering mode.
A 5-step path to launching your first AI product
So, how does AI help banks in the first year in a way that leadership will respect?
Step 1: Clarify the problem and metric
Choose one clear goal, for example:
- Increase right-party contact rate in collections by 10%.
- Reduce time-to-yes on SME loans by 20%.
- Reduce manual case handling in a specific process by 15%.
Tie it to a KPI you already track. If you can’t measure it today, fix that before you discuss AI.
Step 2: Set up the data foundation
Put in place:
- A secure replica of production data (no direct heavy work on the live core).
- Basic pipelines into a reporting or analytics environment.
- Agreed definitions and a simple data dictionary.
- Clarity on which teams can access which aspects.
You don’t need a huge data platform, just a stable, repeatable way to get the right data in front of the right people. This is exactly what we offer with Database Access.
Step 3: Build and test a modest model
For a first project, simpler is better:
- Use historical data from the replica.
- Start with interpretable models (e.g. logistic regression, trees) rather than deep learning.
- Involve risk and compliance early:
- Which variables are acceptable?
- How will you document and explain decisions or scores?
The goal isn’t to win an AI competition, it’s to build something your teams can trust, maintain, and improve.
Step 4: Run a controlled pilot
Don’t flip a switch across the whole institution. Start in a test environment or a tightly scoped segment:
- The model makes a recommendation; the people keep control.
- Run this for a defined period (e.g. 8–12 weeks).
Track:
- Uplift vs existing process
- Operational impact
- Edge cases: when is the model wrong, and why?
This is where you start to see artificial intelligence banking efficiency improvements in real operations, not just in a lab.
Step 5: Operationalise and iterate
If the pilot delivers value:
- Integrate the model into the real workflow:
- Collections queue ordering
- Application routing
- Simple risk flags
- Make results visible in existing dashboards/reports.
Set a review cadence:
- Are inputs drifting?
- Are results stabilising or deteriorating?
- Does the model need retraining?
This is also where you can consider bringing in more advanced techniques, or even generative AI in banking for customer communication or agent assistance, but only once your fundamentals are in place.
Different regions, different starting points
The right path for AI in banking and financial services depends heavily on your market.
Southeast Asia
- Stronger digital rails in many markets (eKYC, QR, instant payments).
- Regulators are actively engaged and watching AI developments.
Some possible good starting AI use cases include:
- SME credit overlays
- Line management and limit increases
- Collections and retention prioritisation
West and East Africa
- Highly mobile-centric.
- Big opportunity in alternative signals and transactional behaviour.
Some good first AI projects could focus on:
- Probability of repayment
- Wallet and MFI data patterns
- Basic fraud and anomaly detection
Highly conservative and heavily supervised markets
Position AI as augmented decisioning: the model ranks or recommends, humans decide.
Emphasise:
- Transparent models
- Documented governance
- Strong audit trail for how decisions were made.
Your first AI product needs to fit in with your data reality and your regulator.
Common mistakes to avoid
If you want to avoid becoming another AI pilot that went nowhere, watch out for these:
- Starting with the most exciting problem, not the most tractable one.
- Buying into AI before fixing data access and quality.
- Letting vendors lead with tools instead of your outcomes.
- Relying on manual exports and one-off SQL queries.
- Leaving risk and compliance out until the end.
- Expecting AI to fix broken processes instead of highlighting where to fix them.
The best artificial intelligence software solutions for banks won’t save a project that’s built on unstable foundations. Tools matter, but data and process come first.
What does a successful AI project look like after 12–18 months?
You don’t need a lab full of PhDs to be in a good place with AI. You need one or two AI-powered use cases live, with clear owners and metrics and a stable data foundation:
- Production replica
- Simple, documented pipelines
- Shared definitions
- Business teams who understand the models well enough to question and improve them.
- A short roadmap: the next 2–3 use cases, all reusing the same data backbone.
At this point, artificial intelligence banking efficiency improvements stop being theoretical. You see the benefits of AI in banking show up as:
- Shorter decision times
- Fewer manual interventions
- Better allocation of staff effort
- More consistent risk outcomes
Where Oradian and Database Access fit in
If you’re already on Oradian, you’re actually closer to all of this than many institutions. Your core is already cloud-native. With Database Access, you can turn that into a secure, read-only replica of your production PostgreSQL database with full-fidelity data and no extra load on the live core, ready to plug into your own warehouse, BI tools, or AI stack. That solves the hardest part of AI integration with core banking systems: getting safe, reliable, consistently updated data in a place where your teams can work. From there, launching your first AI product stops being a moonshot and becomes a structured, 90-day project.
Take the first, most foundational step with Database Access
If you’re serious about AI but want to avoid the hype traps:
- Pick one realistic use case.
- Get honest about your data access and quality.
- Treat your first AI project as a data and process project, not a model showcase.
If you’re already on Oradian, you don’t need a huge transformation programme to start seeing the benefits of AI in banking, you need a safer, smarter way to use the data you already have. That’s exactly what Database Access is for. With Database Access, your team gets:
- A secure, read-only replica of your production PostgreSQL database
- Full-fidelity, always-fresh data with no additional load on the live core
- Direct connections into your existing stack; data warehouses, BI tools, notebooks, fraud engines, or AI platforms
- A consistent data layer you can reuse across reporting, analytics, and AI use cases
In other words, it solves the hardest part of AI integration with core banking systems: getting reliable, governed production data into an environment where you can safely experiment, train models, and put them into controlled production.
From there, launching your first AI product stops being a risky leap and becomes a structured project:
Phase 1: Database Access switched on, replica live, connected to your tools
Phase 2: First AI or advanced analytics use case defined, data prepared, models tested on real signals
Phase 3: Controlled pilot in production, with clear metrics and governance
If you’re serious about exploring how AI helps banks in a way that’s measurable and safe, the most practical first move is to fix the data foundation and make your core work with you.
To talk through what a first AI product could look like for your institution, with your data, your systems, and your regulator, email us at vanda.jirasek@oradian.com with your top priority (e.g. collections, SME risk, CX) and how you’d measure success and we’ll work with you to map out a 90-day plan.
