Embedded finance is no longer an experiment. Payments, lending, accounts, cards, and identity services are increasingly delivered directly inside non-bank platforms—e-commerce marketplaces, mobility apps, SaaS products, and vertical industry tools. For banks, this shift represents a major opportunity: transforming internal banking capabilities into Banking-as-a-Service (BaaS) products that power entire ecosystems. 

But exposing banking functionality is not simply a matter of opening APIs. It requires a carefully engineered platform that can scale across tenants, protect core systems, enforce compliance, and provide the developer experience fintechs expect. Embedded Finance 2.0 is defined not by what services are offered, but by how safely, reliably, and quickly they can be integrated. 

Let's go over the technical patterns that enable banks to build modern BaaS platforms—without compromising stability or regulatory posture. 

From Internal Banking Systems to External Products 

Traditional banking systems were designed for internal consumption: tightly coupled flows, synchronous integrations, and limited external exposure. Embedded finance flips this model. Banks must expose: 

…to hundreds or thousands of external partners with different risk profiles, volumes, and business models. 

This requires a platform mindset: internal capabilities become products, with contracts, SLAs, onboarding flows, monitoring, and lifecycle management. 

At the heart of every BaaS platform sits a robust API gateway. This is not just a routing layer—it is the enforcement point for security, performance, and governance. 

authentication and authorization (OAuth2, mTLS, JWT) 

request validation and schema enforcement 

rate limiting and throttling per partner 

API versioning and backward compatibility 

For banks, the gateway acts as a blast-radius limiter, ensuring that partner traffic can never overload or directly access core systems. 

Tenant Isolation: Designing for Multi-Partner Scale 

Embedded finance platforms are inherently multi-tenant. Each fintech or merchant partner must be isolated from others—technically, operationally, and from a risk perspective. Isolation happens at multiple layers: 

At the API level, tenants are separated by credentials, scopes, quotas, and routing rules. 

At the data level, tenant identifiers are embedded into every request and enforced through schema design, sharding strategies, or even dedicated databases for high-risk partners. 

At the runtime level, critical services may be deployed in separate namespaces or clusters to contain failures. 

This approach ensures that a single misbehaving integration cannot impact the rest of the ecosystem—a non-negotiable requirement for regulated banking environments. 

Unlike internal systems, BaaS platforms face unpredictable traffic patterns. A partner may launch a marketing campaign or experience sudden growth that multiplies API calls overnight. 

Advanced rate limiting strategies allow banks to: enforce per-tenant and per-endpoint quotas, apply burst control with graceful degradation, prioritize critical flows over non-essential ones, dynamically adjust limits based on risk or SLA tier.

These mechanisms protect backend systems while still allowing partners to scale confidently. 

Secure Partner Onboarding: From Contract to Production 

One of the biggest friction points in embedded finance is onboarding. Manual processes slow growth and frustrate partners. Modern BaaS platforms treat onboarding as a technical workflow. A mature onboarding pipeline includes: automated credential provisioning, sandbox environments with realistic test data, contract-driven API documentation (OpenAPI), webhook configuration and validation, compliance checks integrated into the flow. 

Security is enforced from day one through scoped credentials, approval workflows, and progressive access. Partners move from sandbox to production only after passing functional, security, and compliance gates. 

Event-Driven Architecture Behind the APIs 

While APIs are the entry point, event-driven systems power the internals. Kafka or similar streaming platforms enable: 

asynchronous processing of payments and settlements 

decoupling between partner traffic and core systems 

replayability for audit and dispute resolution 

Events also enable ecosystem integrations, allowing banks to notify partners about status changes, funding events, or compliance actions without constant polling. 

Observability as a Regulatory and Operational Requirement 

In embedded finance, observability is not optional. Banks must be able to trace every request, transaction, and state change across systems and partners. 

distributed tracing across APIs and event streams 

real-time metrics for latency, error rates, and throughput 

Strong observability supports not only operational excellence, but also regulatory audits, incident investigations, and SLA enforcement. 

Exposing banking services increases the attack surface. Embedded finance platforms must be secure by design: zero-trust principles for every request, strict schema validation to prevent injection attacks, continuous security testing (SAST, DAST), secrets management and key rotation, anomaly detection on traffic and usage patterns. 

Security controls are enforced consistently across tenants, environments, and deployment cycles. 

How OceanoBe Helps Banks Build BaaS Platforms 

OceanoBe works with banks and financial institutions to transform internal capabilities into scalable, compliant BaaS platforms. Our teams help design and implement: 

tenant-aware architectures and data models 

secure onboarding pipelines for partners 

observability stacks for regulated environments 

CI/CD pipelines with embedded compliance checks 

We don’t just expose APIs—we help banks productize banking. 

Embedded Finance Is an Engineering Discipline 

Embedded finance is not a side project. It is a strategic shift that requires robust engineering foundations. Banks that succeed will be those that treat BaaS as a platform—designed for scale, safety, and developer experience from day one. 

With the right architectural patterns and an experienced technology partner, banks can move beyond experimentation and become reliable infrastructure providers for the next generation of financial products. 

Engineering Embedded Finance 

The technical foundations behind Embedded Finance 2.0 and how banks can productize internal capabilities 

Learn how banks can build secure, scalable Banking-as-a-Service platforms using API gateways, tenant isolation, observability, and event-driven architectures. 

How Banks Can Expose Banking-as-a-Service Safely and at Scale

Scaling a payments platform is not just about adding more compute or deploying additional instances of a service. At some point, databases—not application servers—become the bottleneck. When millions of transactions, settlements, chargebacks, ledger updates, and compliance checks hit the system every hour, the underlying data layer needs to scale horizontally. 

This is where data partitioning and sharding become essential architectural tools. They enable systems to distribute load, minimize contention, isolate failures, and maintain predictable performance—even under intense transaction throughput. 

In payments, however, partitioning is more complex than in typical web workloads. Financial events require strict consistency, traceability, and idempotency guarantees. A straightforward "divide the data by range" approach can lead to reconciliation gaps, broken ledger invariants, and compliance issues. Let's explore how sharding strategies apply to modern payments architectures—and how to design them carefully to preserve correctness. 

As transaction volumes grow, simple vertical scaling (bigger machines, more CPU, faster disks) eventually hits diminishing returns. Payments platforms encounter several fundamental limits: 

Hotspot accounts can generate a disproportionate load. 

Global tables (customers, merchants, balances) become contention points. 

High write throughput from payment events overwhelms single-node databases. 

Compliance audits require queryable histories without degrading live traffic. 

Partitioning allows data to be divided across multiple storage nodes. The result is better throughput, reduced contention, and a structure where workloads can be parallelized cleanly. 

But it must be done with an understanding of financial constraints: strong consistency for balances, exact ordering of ledger events, and predictable reconciliation. 

Most large-scale payment platforms use a combination of the following strategies. 

1. Functional Partitioning (Service-Level Separation) 

Before sharding tables, the first step is separating domains: Payments, Settlements, Disputes, Merchant onboarding, Fraud detection, Ledger and reconciliation. Each domain owns its data and persistence layer. This creates natural boundaries and allows teams to scale services independently. 

When a single datastore reaches its limits, horizontal sharding splits tables across multiple physical partitions. 

Data is distributed based on a hashing function—often using fields like: 

This approach provides good uniform distribution and avoids hotspots. However, cross-shard queries become more complex. 

Useful for time-ordered data such as: transaction histories, settlement batches, event logs.

Range sharding simplifies time-based queries but risks uneven shard load if certain periods become high traffic. 

A central routing service maps keys to shard locations. This enables dynamic rebalancing, which is critical when shard load is unpredictable over time. 

Payments architecture must account for constraints that typical SaaS workloads don’t face. 

Financial systems must maintain strict consistency for: balances, ledger entries, transaction ordering.

A common pattern is shard-by-account or shard-by-merchant, ensuring all balance-impacting operations for an entity reside within a single partition. This prevents race conditions and double-spend scenarios. 

Distributed transactions slow down systems and create failure scenarios that are difficult to reconcile. Instead, platforms aim to design sharding keys such that most operations logically stay in one shard. 

For events involving multiple entities—such as marketplace payouts—systems use patterns like: 

This avoids distributed locks while remaining consistent. 

Some merchants or accounts generate huge volumes (e.g., global marketplace platforms). For them, advanced strategies include: 

Sub-sharding based on secondary identifiers 

Event stream partitioning in Kafka or Pulsar 

No single technique solves all scenarios, but combining sharding at the DB level with partition-aware streaming infrastructure provides stability. 

Kafka and Sharded Architectures: A Strong Pairing 

Many modern payments systems use Kafka to absorb write pressure before data reaches databases. Kafka partitions map naturally to sharding keys. 

// Ensuring payment events always land in the correct Kafka partition 

int partition = Math.abs(merchantId.hashCode() % numPartitions); 

producer.send(new ProducerRecord<>("payments", partition, merchantId, paymentEvent)); 

 This guarantees ordering by merchant (or account), which is essential for ledger consistency. The consuming service then writes events to the correct database shard. 

This pattern: preserves ordering guarantees, avoids cross-shard writes, enables replay and backfill, and makes scaling the pipeline easier. 

Migrating to a Sharded Architecture Without Disruption 

Banks and payment processors rarely get to start from a clean slate. Most evolve from monolithic databases that served well during the early years. Migration requires careful planning. 

Introduce an event streaming backbone (Kafka) to decouple writes. 

Define sharding keys based on domain behavior and access patterns. 

Move non-critical services first to validate partitioning logic. 

Adopt dual-write or dual-read strategies with feature flags. 

Progressively migrate traffic to new shards. 

Decommission legacy tables only after reconciliation. 

This controlled, phased evolution avoids outages and ensures regulatory reporting remains intact. 

How OceanoBe Helps Scale Payments Platforms 

As a technology partner with deep experience in banking, core payment flows, and event-driven architectures, OceanoBe supports clients in: 

selecting the right partitioning and sharding strategy, 

implementing Kafka-based routing and event modeling, 

designing consistency models for ledgers and balances, 

migrating safely from monolithic to distributed data stores, 

ensuring full observability and operational transparency. 

Scaling financial systems is more than a technical exercise—it requires deep knowledge of compliance, reconciliation, and transactional guarantees. Our senior engineers bring the experience necessary to help platforms grow without compromising correctness. 

Data partitioning and sharding are essential for any payments platform reaching scale, but adopting them requires more than just distributing data. The architecture must preserve transaction ordering, ensure consistency, avoid cross-shard contention, and support auditability. 

When done well, sharding transforms a payment system from a bottleneck-prone monolith into a horizontally scalable, resilient foundation that can sustain growth for years. 

Data Partitioning and Sharding for Scalable Payments Platforms 

Learn how data partitioning and sharding enable scalable, consistent, and high-performance payment systems as transaction volumes grow. 

Techniques for distributing data in large-scale payment systems to balance performance and consistency

In fintech, every product, no matter how innovative, eventually comes down to one thing: integration. Integrating with banks, PSPs, KYC providers, credit bureaus, fraud engines, open banking aggregators, and internal microservices. These connections define how fast you can scale, how reliable your system is, and how painful compliance requirements will be once regulators enter the conversation. 

Many early-stage startups overlook one foundational truth: the quality of your API design from day one determines the speed of every future integration. When early endpoints are rushed, inconsistently structured, or built without a long-term view, fragmentation creeps in. What starts as an MVP approach becomes a permanent architecture—one that is expensive to fix and increasingly fragile as the business grows. 

A strong API strategy isn’t just a technical choice. It is a business decision that influences your ability to onboard partners, pass audits, and avoid multi-month refactoring projects. 

APIs are more than interfaces—they are the external contract through which your product communicates with the world. Poor design affects everything downstream: partner onboarding slows, customers experience inconsistent behaviours, and development teams accumulate technical debt that becomes difficult to unwind. 

In fintech, the stakes are even higher. APIs must support strict transaction guarantees, clear audit trails, predictable performance under load, and privacy constraints that must hold up under regulatory review. If these expectations are not embedded early, teams end up reworking core flows just when the product should be scaling. 

The most common pitfalls for early-stage teams appear deceptively minor at first. For example, a simple decision to structure payloads differently across services can later force teams to maintain dozens of mapping layers. Another typical issue is skipping a versioning strategy—once external partners rely on your endpoints, even the smallest change can become a breaking change. 

Some young fintechs also place business logic inside controllers, creating APIs that cannot evolve cleanly. Others forget about idempotency in write operations, leading to duplicate charges, stuck onboarding flows or inconsistent customer states—issues that become expensive to diagnose in production. 

Perhaps the most costly mistake is ignoring compliance expectations until late in the journey. Regulators require clear timestamps, correlation IDs, audit metadata, and strict handling of sensitive data. Adding these fields retroactively through every service and integration chain usually results in a full redesign—not a patch. 

Early shortcuts lead to delayed partnerships, long QA cycles, and, often, the need to rebuild entire flows when scaling becomes necessary. 

A future-proof API strategy begins long before writing the first controller. Start with a clear domain model—one that encodes business concepts consistently across services. A well-structured domain becomes a shared language between engineers, product teams, and external partners. 

From there, consistency is key. Standardizing how errors, timestamps, money fields, identifiers, and metadata are represented ensures that all services speak the same dialect. Even seemingly small choices—how you structure pagination, how you wrap responses, how you propagate correlation IDs—shape the maintainability of your system. 

Some considerations, such as idempotency, must be treated as non-negotiable. In payments or lending flows, retries are inevitable. Designing your APIs with idempotency keys and replay-safe logic prevents the nightmare scenario of duplicate transactions or corrupted states. 

A strong versioning model helps your system evolve without breaking consumers. Even if you expose only a single version initially, preparing for version isolation avoids painful retrofits. 

Security and compliance should be integrated into API design, not layered on afterward. Audit trails, field masking, permission checks, and context propagation are most effective when they are foundational rather than reactive add-ons. 

Finally, observability should be baked into your contract. If every request carries traceable, structured context, debugging distributed systems becomes dramatically easier—especially in event-driven architectures. 

Integration Layers That Evolve With Your Product 

Modern fintech stacks benefit greatly from a robust integration layer. Java and Spring Boot offer mature support for contract-driven development using OpenAPI specifications, while Kafka enables asynchronous communication patterns, fault isolation, and controlled retries. 

A well-designed API layer supported by automated tests (including Testcontainers-based integration tests) makes it easier to ensure that new changes do not break existing contracts. As the system grows more complex, this foundation becomes a major advantage. 

partner integrations accelerate rather than slow down, 

testing and monitoring remain predictable, 

regulatory requirements are easier to satisfy, 

backend logic stays cleanly separated from API concerns, 

and rework is minimized even as the product evolves. 

In other words, the API becomes a stabilizing force rather than a source of technical debt. 

Why Technical Partners Make a Difference 

Many startups do not have the resources or experience to design compliance-ready APIs from day one. A seasoned engineering partner like OceanoBe brings domain knowledge from banking, payments, PSD2/SCA flows, and large-scale integration programs. 

This expertise helps founders avoid early missteps that later become multi-month rebuilds. Partners can establish schema standards, enforce idempotency patterns, design observability layers, and validate security and regulatory expectations—long before these issues become blockers. 

Working with an experienced technical integrator allows startups to scale confidently, knowing their API foundation will support growth rather than constrain it. 

Your API Strategy Defines Your Growth Trajectory 

In fintech, APIs are the product. They determine your speed of integration, your compliance posture, and your ability to onboard partners without friction. Poor early API decisions compound quickly, but thoughtful design—rooted in domain modeling, consistency, security, and observability—pays off for years. 

By planning your API strategy early, you save time, reduce cost, strengthen operational reliability, and build an architecture that can scale from MVP to enterprise-grade. It is one of the most strategic investments a fintech startup can make. 

The Cost of Integration 

Why smart API design choices in early stages save months of rework and compliance trouble later 

How to Plan Your API Strategy Early 

European payments are in the middle of a transformation. With SEPA Instant adoption rising, PSD3 and the Payment Services Regulation reshaping compliance requirements, and SEPA 2.0 promising improved interoperability and richer transactional data, financial institutions are being pushed to modernize faster than ever before. 

Yet the reality inside most banks and PSPs is far from “cloud native.” Core payment engines often run on decades-old architectures designed for batch processing and constrained by strict SLAs, monolithic data models, and regulatory expectations that make rapid change difficult. Rewriting these systems is usually not an option—it’s too risky, too disruptive and too expensive. 

The challenge becomes clear: How do you adopt SEPA 2.0 without destabilizing the systems that already process millions of payments every day? The answer lies in reframing modernization as a phased, event-driven evolution instead of a disruptive overhaul. 

Why SEPA 2.0 Demands a New Approach to Architecture 

SEPA 2.0 is not simply a message format upgrade. It shifts the industry toward real-time interactions, richer ISO 20022 semantics, and a level of transparency that is difficult for legacy systems to support. Traditional cores—whether COBOL-based, PL/SQL-heavy, or built on early-generation Java stacks—were never designed to validate complex XML structures in real time, enrich messages dynamically, or coordinate instant payments under strict fault tolerance. 

This is why the most successful SEPA migrations today rely on a modern integration layer that gradually absorbs responsibilities from the core. Firms preserve stability while adding new capabilities on the edges, where they can evolve safely and incrementally. 

A Phased Modernization Journey Instead of a Big Bang Rewrite 

The transition to SEPA 2.0 becomes far less daunting when approached as a series of controlled architectural expansions rather than a replacement of the core platform. Many institutions begin by introducing an event-driven integration layer, often built on Kafka. This layer becomes the buffer between the old world and the new one. Legacy systems continue producing batch files or synchronous messages, while Kafka takes over ingestion, fan-out, retries, and transformation into SEPA 2.0 formats. 

Once this layer is in place, banks can start building specialized transformation services using Java and Spring Boot. These services validate incoming messages against ISO 20022 schemas, enrich them with customer or risk data, and output clean, modernized payloads without requiring the core to understand SEPA 2.0 natively. The transformation logic becomes externalized, modular, and easy to iterate on. 

As payment complexity grows, most organizations add an orchestration layer on top—either a workflow engine like Camunda or Temporal, or a saga-based coordination model built with Kafka Streams. This orchestration layer coordinates the multi-step lifecycle of a payment: validation, fraud review, AML checks, liquidity management, posting, and callback notifications. The core remains the system of record, but the intelligence shifts into the distributed architecture around it. 

Over time, institutions can start moving real-time flows—such as SEPA Instant or Request-to-Pay—into the event-driven ecosystem entirely. The shift is gradual, reversible, and much safer than attempting a full migration in one step. 

How Java and Kafka Enable Modern SEPA Processing 

Java continues to be the dominant language for payments infrastructure because of its maturity, type safety, and strong ecosystem around ISO 20022 handling. In modernization programs, Java microservices become the building blocks for translating legacy formats into SEPA 2.0 messages, performing schema validation, and applying enrichment rules. 

A transformation service, for instance, may read from Kafka, validate the content against an XSD schema, and reconstruct a SEPA-compliant message enriched with additional risk or customer metadata. The logic remains outside the core, which means changes can be deployed continuously without affecting mainframe or legacy stability. 

Kafka, on the other hand, provides the backbone of the event-driven ecosystem. Its log-based design ensures that every payment step is recorded immutably—crucial for audit trails in regulated environments. Kafka Streams further unlocks real-time enrichment and routing capabilities. It can combine payment events with fraud scores, AML signals, or historical behavior models in milliseconds. It can run windowed aggregations to detect duplicate submissions or suspicious velocity patterns. And with exactly-once semantics, it avoids double posting—always a critical concern in payments. 

Together, Java microservices and Kafka-based event pipelines form a modernization layer flexible enough to keep up with SEPA 2.0 demands while keeping risk away from the core. 

Maintaining Compliance and Stability Throughout the Transition 

One of the biggest concerns for any payment modernization program is maintaining business continuity. Banks cannot interrupt posting, delay settlements, or break regulatory reporting during a transition. 

An event-driven approach makes this possible. Every transformation, enrichment, and routing decision can be logged, timestamped, and replayed. Backward compatibility with legacy flows is preserved via dual publishing strategies. Rollbacks become simpler because new services operate as overlays rather than replacements. 

In addition, the architecture naturally supports compliance obligations: each message carries correlation IDs, traceability metadata, and versioning markers that can be inspected for PSD3 audits or internal risk reviews. 

Why Technology Partners Matter in SEPA 2.0 Programs 

Modernizing payment rails is not just a matter of building new APIs. It requires navigating regulatory expectations, guaranteeing resiliency, designing streaming topologies, and integrating with legacy platforms that may not have been touched in years. 

Partners like OceanoBe bring experience in SEPA flows, ISO 20022 transformation, Kafka-based architectures, cloud-native deployments, observability, and end-to-end testing. They help banks and PSPs design migration paths, build integration layers, validate performance under load, and ensure that new services coexist gracefully with existing systems. With a phased, event-driven blueprint, the risk of modernization shrinks dramatically while the institution gains a future-proof payment infrastructure. 

SEPA 2.0 Is an Evolution, Not a Replacement 

Migrating to SEPA 2.0 does not mean dismantling a stable core system. It means building a more flexible, resilient architecture around it—one capable of supporting real-time flows, richer data models, and distributed compliance requirements. By embracing event-driven architectures, Java-based transformation services, and Kafka as the integration backbone, banks and PSPs can modernize steadily, safely, and with full confidence in regulatory alignment. 

If you want, I can now generate the meta description, keyword list, and LinkedIn promo post for this article as well. 

From SEPA to SEPA 2.0 

How banks and PSPs can evolve toward SEPA 2.0 while keeping their core platforms stable 

Banks can adopt SEPA 2.0 safely using event-driven architectures, Java microservices, and Kafka integration layers without disrupting legacy payment cores. 

Modernizing Payment Rails Without Breaking Legacy Systems 

Real-time data pipelines are now a core part of modern financial systems. Whether it’s transaction scoring, fraud detection, authorization routing, settlement reconciliation, or real-time customer analytics, the banking and payments ecosystem depends heavily on event-driven architectures. Kafka has become the backbone of these systems—offering durability, high throughput, and strong ordering guarantees. 

But while event-driven architectures solve many scalability and resilience challenges, they also introduce new testing complexities. Traditional unit tests rarely capture the behavior of real Kafka clusters, serialization boundaries, or stream topology correctness. Integration environments are often slow, inconsistent, or shared across teams. And mocking Kafka behavior tends to hide important failure modes. 

This is where Testcontainers shines. By running Kafka, schema registries, databases, and other dependencies inside lightweight, disposable containers, teams can test real topologies, real data flows, and real-time behavior—directly from the local machine or CI pipeline. 

Let's explore how to build deterministic, production-like event-driven tests using Kafka Streams + Testcontainers, ensuring consistency across builds and confidence in real-time financial pipelines. 

Why Event-Driven Testing Matters in Financial Systems 

For fintech and banking workloads, errors in streaming pipelines can cause real impact: duplicate transactions, incorrect fraud scores, delayed settlements, out-of-order events, inconsistent state stores, missed compliance or audit logs.

These are not hypothetical risks—real payment systems depend on correctness under load, across partitions, and during failures. 

Event-driven systems require us to test: 

error-handling flows (DLQs, retries, tombstones) 

serialization/deserialization boundaries 

Mocking a Kafka cluster doesn’t reveal these issues. Running a real cluster in containers does. 

Building a Local Kafka + Schema Registry Environment with Testcontainers 

Testcontainers allows you to spin up a Kafka broker in isolation for a test suite. You get clean environments per test run, no leftover topics, and no cross-test interference. 

Here’s a basic Testcontainers setup for Kafka in Java: 

public static KafkaContainer kafka = new KafkaContainer( 

        DockerImageName.parse("confluentinc/cp-kafka:7.4.0") 

With this running, your tests interact with a real Kafka broker, not mocks. 

For more complex fintech systems using Avro, JSON Schema, or Protobuf, you can also spin up a Schema Registry container: 

public static GenericContainer<?> schemaRegistry = 

    new GenericContainer<>("confluentinc/cp-schema-registry:7.4.0") 

        .withEnv("SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS", kafka.getBootstrapServers()) 

Now tests can validate schema evolution, compatibility, and serialization just like production. 

Testing Kafka Streams Topologies with Testcontainers 

Kafka Streams applications are powerful but easy to misconfigure. Topology tests ensure: 

messages flow through the correct branches 

windowing logic matches regulatory expectations 

Here’s an example Kafka Streams test using Testcontainers: 

Event-Driven Testing with Testcontainers and Kafka Streams

Hands-on guide to testing real-time data pipelines with containerized environments for consistency across builds

Test fintech pipelines reliably with Testcontainers and Kafka Streams; containerized environments ensure consistent, production-like event-driven testing.

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