The boundary between banking and everything else is dissolving.
- Telcos are offering micro-loans embedded in data top-up flows
- Digital wallets are used by millions of urban consumers
- Gig platforms are advancing wages before payday
- Marketplaces are underwriting the SMEs selling on their platform.
This is no longer a future scenario. It is the reality for many digital-first banks and lenders in Nigeria, the Philippines, Indonesia, and the other high-growth markets where Oradian’s customers operate.
The institutions best positioned to win this moment are not always the banks; they are the platforms, telcos, and wallet providers who already have the user relationship, the transaction data, and the distribution.
But there is a catch: owning the customer relationship is not the same as having the infrastructure to build a durable, AI-ready financial stack on top of it. Most non-bank platforms discover this the hard way once they start stitching together point solutions, running into core banking bottlenecks, and find that their data is too fragmented to power the AI use cases that would make embedded finance genuinely competitive.
This article is a practical guide to getting that foundation right, regardless of institution type.
Why embedded finance without the right stack will stall
In Indonesia alone, digital payments are projected to more than double, growing from A$630 billion in 2024 to A$1.3 trillion by 2030. Across Southeast Asia, 83% of consumers are already using digital wallets, and 60% are making payments via QR code. In Nigeria, MSMEs, the backbone of the economy, employing over 80% of the workforce are actively seeking faster, digital-first access to credit.
Platforms and telcos are sitting at the centre of these flows. They see the transactions, they know the customers, and they have the channels. What they often lack is the infrastructure to translate that position into financial products at scale and the data architecture to make those products intelligent.
The typical failure pattern looks like this: a platform launches a lending or wallet product using a patchwork of APIs, a third-party credit engine, and manual processes at the edges. It works at low volume. Then it hits friction:
- Loan decisions take too long
- Fraud incidents go undetected until after the damage
- Personalisation is impossible because data lives in three different systems
- Every product change requires months of vendor negotiation.
What the platform actually needed, from the start, is a modern core banking infrastructure, a governed data layer, and architecture designed for AI augmentation. These are the foundation of any embedded finance play that intends to last.
The three layers of an AI-ready embedded banking stack
Think of the embedded banking stack in three layers:
- The core
- The data layer
- The AI/intelligence layer
Each one depends on the one below it. Skipping a layer, as many platforms do, means building on sand.
The embedded banking stack
- Layer 1 – The core: Real-time processing, open APIs, configurable products, multi-channel support, financial accuracy
- Layer 2 – Data: Governed replica of core data, unified customer view, analytics-ready, off-core query environment
- Layer 3 – Intelligence: AI credit scoring, churn prediction, personalisation, fraud detection, collections routing
Layer 1: The core
A cloud-native, API-first core banking platform is the non-negotiable starting point. For platforms and telcos entering financial services, this is the engine that makes real-time deposits, lending, and payments possible and that keeps the ledger accurate across millions of concurrent transactions.
The key capabilities to look for are not necessarily the ones that feature most prominently in vendor pitches. What matters operationally is:
- Real-time transaction posting so that a salary notification fires the moment funds arrive, not hours later
- Configurable product logic so that a telco can launch a new airtime-credit product without a six-month vendor change cycle
- Multi-channel consistency so that an agent transaction, a USSD interaction, and an in-app action are part of a single customer record
- Open APIs so that the core can be embedded into the platform’s own product experience, rather than requiring the customer to leave for a separate banking app
A legacy core, or a modern core with limited configurability, will create a ceiling on every commercial ambition. Every new product becomes a project, partnerships require custom integration, and AI use cases are blocked waiting for data that cannot be easily extracted.
Layer 2: The data layer
This is where most platforms underinvest and where most AI projects fail. According to data from surveyed businesses, poor quality data has a detrimental effect on the majority of organisations, even those actively using data to improve customer experience.
For embedded finance, the data layer serves a specific purpose: it gives every team from product to risk to growth to compliance a consistent, accurate, near-real-time view of what is happening across the customer base, without putting load on the live core system.
The practical mechanism is a secure, read-only replica of the production database, kept in continuous sync but isolated from operational workflows. This replica becomes the foundation for every downstream AI and analytics workload:
- Model training
- Fraud pattern detection
- Churn analysis
- Segmentation
- Personalisation
Without it, data scientists are working from stale exports, analysts are maintaining conflicting spreadsheets, and AI models are operating on incomplete inputs. 95% of AI projects fail to deliver on their promises, often because the data foundation was not in place before the algorithms were built.
For telcos and platforms, this layer is particularly important because the most valuable signals about financial behaviour are often generated outside the banking product itself in top-up frequency, transaction timing, platform usage patterns. A well-architected data layer can incorporate these alternative signals and make them available to AI models in a governed, auditable way.
Layer 3: The AI and intelligence layer
Only once Layers 1 and 2 are stable does the AI layer become viable. But when it is, the possibilities are substantial and directly tied to growth outcomes.
The AI use cases that generate the highest return in embedded finance are not the most technically complex. They are the ones closest to the core customer journeys: credit decisions, fraud prevention, churn rescue, and personalised product matching. Here is how each plays out in a platform or telco context.
AI credit scoring for thin-file borrowers
Platforms and telcos have access to behavioural data including top-up regularity, transaction timing, purchase patterns that traditional credit bureaus do not see. AI models can use these signals to assess creditworthiness for unbanked individuals globally who may be missing a formal credit history. Studies show AI-driven scoring improves default prediction accuracy by 15–25% over legacy methods, while reducing default rates by up to 30%.
Real-time fraud detection
In markets where digital trust is still being built, one major fraud incident can set adoption back years. AI fraud models analyse transaction patterns, device fingerprints, and behavioural anomalies across millions of events catching fraudulent activity in real time rather than after the damage is done. AI systems now achieve 90–99% accuracy, compared to 35–70% for traditional rule-based approaches.
Churn prediction and retention
By monitoring logins, transaction frequency, balance patterns, and support contacts, AI can flag customers likely to go inactive in the next 30–60 days…before they leave. This creates an intervention window: a tailored offer, a friction-point fix, or a proactive support outreach. But it only works if the data layer provides timely, accurate signals to act on.
Personalised product matching
Rather than sending the same offer to every user, AI segments customers by likely financial need:
- An SME that will need working capital in 30 days
- A salary earner who could benefit from automated savings
- A gig worker approaching their borrowing limit
Platforms that get this right see measurably higher conversion and lower churn.
Intelligent operations and automation
AI can also turn inward: classifying support queries, routing collections workloads by self-cure probability, automating document parsing in onboarding. For lean platform teams, automation can be the difference between scaling with headcount and scaling efficiently.
What telcos and platforms get wrong when they enter financial services
The most common mistake is treating embedded finance as a feature addition rather than an infrastructure decision. A telco that bolts a lending product onto its existing stack without investing in core banking infrastructure and a proper data layer will find itself rebuilding from scratch within two years, usually at significant cost and reputational risk.
A second common failure is assuming that data volume substitutes for data quality. Telcos and platforms do have an enormous amount of data. But raw volume is not the same as a governed, queriable, consistent data asset. Models trained on fragmented, inconsistently formatted data will underperform and in credit and fraud contexts, underperformance has direct financial consequences.
A third pitfall is underestimating the compliance surface. Embedded finance in dynamic markets means navigating e-KYC regulations, data residency requirements, central bank reporting obligations, and consumer protection rules that vary across jurisdictions. An AI-driven credit decision that works in Lagos may need entirely different data inputs and governance controls to comply in Cebu or Surabaya. The infrastructure decisions made at the foundation level determine how expensive regulatory adaptation will be later.
A practical path to AI readiness: the 90-day starting point
Transforming an embedded finance stack does not require a multi-year programme. The foundational work that enables the first meaningful AI use cases can begin in 90 days.
The sequencing that works consistently for platforms and telcos entering financial services looks like this:
Days 0–30: diagnose the foundation
- Audit current data access: can teams query transaction and customer data off-core, in near-real-time, without manual exports?
- Map the current product journey end-to-end and identify where core system limitations are creating friction or delays
- Identify the single highest-value AI use case (credit scoring, fraud detection, or churn prediction) and confirm whether the data exists to support it
- Align leadership on what AI readiness actually requires
Days 30–60: build the data layer first
- Establish a governed, read-only data replica of your core banking system, accessible to analytics and data science teams without operational risk
- Standardise customer IDs and transaction logging across all channels: app, agent, USSD, and partner
- Run a focused data quality audit on the specific dataset that will feed the priority AI use case
- Set up the measurement baseline: what does success look like for the first AI pilot, and how will it be tracked?
Days 60–90: design and launch the first pilot
- Design the first AI pilot with clear success criteria, a small test cohort, and a defined kill-switch if outcomes are not as expected
- Run the model in shadow mode alongside existing processes before going live: compare decisions, catch edge cases, build internal confidence
- Document governance: risk and compliance should co-own the pilot, not review it after the fact
- Plan the next 90-day cycle before the first one ends, using data from the pilot to prioritise the next highest-impact opportunity
What to look for in a core banking partner
For platforms and telcos, the choice of core banking partner is a major strategic decision. The right partner determines how fast you can move, how much your engineering team will be burdened by integration work, and how ready your infrastructure will be for the AI use cases that will define competitive advantage in the next three years.
The capabilities that matter most in a B2B2C, embedded finance context are:
- API-first architecture: so that your product and growth teams can launch new features and integrations without waiting on vendor change cycles
- Native data access: a governed, continuously-synced replica of your production data that analytics and AI teams can work with directly
- Cloud-native scalability: to handle the spiky, high-volume transaction patterns that come with platform-scale distribution
- Multi-channel consistency: treating agent, USSD, app, and partner-channel interactions as part of one coherent customer record
- Configurable product logic: so that a new SME credit product, a digital savings account, or an embedded partner integration can be launched and iterated on quickly
- Support model accountability: a partner who can respond when something goes wrong at scale, not just during the implementation phase
The institutions that have moved fastest in this space including scaling to millions of active accounts, cutting loan approval times from days to hours, launching new products as market opportunities emerge are those that treated the core and data layer as strategic infrastructure, not a commodity decision.
The embedded finance window is open but infrastructure decides who captures it
For telcos, wallets, and platforms in dynamic markets, the embedded finance opportunity is one of the most significant in a generation.
What will separate the platforms that build durable financial businesses from those that stall at product-market fit is the quality of the foundation beneath the experience.
- A modern core that can support real-time processing and rapid product iteration
- A governed data layer that gives every team the same accurate picture of customer behaviour
- An AI layer that is built on that foundation, not bolted onto something fragile
The good news is that this foundation does not have to be built from scratch. The right core banking partner provides it and for platforms already operating on Oradian, the data infrastructure that AI requires may already be in place.
The 90-day window to start is now.
The institutions that move first on this foundation will have a compounding advantage that will be very difficult to close.
Ready to grow?
Our whitepaper, First Login to Lifelong Customer: The Growth Playbook for Banks, covers the full picture of how you can achieve effective, stable growth this year and beyond, from identifying your priority customer segments to designing the lifecycle that takes users from first login to long-term advocacy. It’s written for digital-aspirant financial institutions in dynamic markets and ends with a practical 90-day roadmap you can start immediately.
Understand the AI foundation in detail
For a more technical look at how to build AI readiness from the data layer up, our whitepaper The Digital-First Bank’s Guide to AI walks through the key use cases, from credit scoring to fraud detection to personalisation and operational automation. It includes a readiness checklist your product, tech, risk, and compliance teams can complete together.
Start your embedded banking journey today
Whether you’re assessing your core infrastructure, planning your first AI pilot, or looking for a banking partner who can support embedded finance at scale, we’d like to hear about what you’re building. Book a call with the Oradian team and we’ll help you work out what the right foundation looks like for your institution.
