Banking APIs live under constant tension. They must be open enough to support partners and innovation, strict enough to meet regulatory expectations, and resilient enough to withstand abuse—whether accidental or malicious. Most API abuse incidents don’t start with sophisticated attacks. They begin with leaked keys, stale credentials, misconfigured clients, or overly permissive access that slowly turns into systemic risk. Preventing this requires more than rate limiting or firewalls. It requires intentional design around secrets, identity, and lifecycle management. 

This article explores engineering patterns that protect banking APIs from abuse while preserving partner SLAs and regulatory trust, with a focus on secrets management and key rotation as first-class architectural concerns. 

API Abuse Is Usually an Identity Problem 

When APIs are abused, the root cause is rarely raw traffic volume. More often, it’s weak identity control: a compromised API key, an over-scoped token, or credentials that outlive their original purpose. In banking platforms, API identity is tightly coupled to legal agreements, risk profiles, and regulatory accountability. Every request must be attributable to a specific partner, application, and permission set. Secrets are the foundation of that attribution. 

Treating secrets as static configuration is one of the most common—and dangerous—mistakes. 

Secrets as Living Credentials, Not Static Values 

API keys, client secrets, and certificates should be treated as living credentials with a lifecycle, not as values generated once and forgotten. 

be traceable to a partner and application 

Static, long-lived secrets increase blast radius. When compromised, they force emergency rotations that often break integrations and violate SLAs. Designing for rotation from day one avoids this trap. 

Designing for Key Rotation Without Partner Pain 

Key rotation is often discussed as a security best practice, but in banking it is also a partner experience problem. Poorly designed rotation breaks integrations, triggers incidents, and erodes trust. A common pattern is overlapping credentials. New keys are issued before old ones expire, allowing partners to update configurations safely. Both credentials remain valid for a controlled grace period. 

This approach requires backend systems to support multiple active credentials per client and to track usage precisely. The added complexity is worth it: rotation becomes routine instead of disruptive. 

Separating Authentication From Authorization 

Another critical pattern is separating who a client is from what they are allowed to do. Authentication answers identity. Authorization defines permissions. When these are tightly coupled—embedded directly in static API keys—changes become risky and slow. Modern banking APIs increasingly rely on token-based models where short-lived access tokens are derived from longer-lived credentials. Permissions are evaluated dynamically, allowing fine-grained control without regenerating secrets. 

This separation makes abuse containment far more precise. If a specific permission is abused, it can be revoked without invalidating the entire integration. 

Secrets Management as Infrastructure, Not Configuration 

Secrets should never be treated as application configuration files or environment variables managed manually. In regulated environments, secrets management must be centralized, audited, and automated. A proper secrets management layer ensures that: secrets are encrypted at rest and in transit, access is logged and monitored, rotation can be automated, secrets are never exposed to developers or logs. 

More importantly, secrets management systems integrate with runtime identity, allowing credentials to be issued, rotated, and revoked based on policy rather than manual processes. 

Limiting Blast Radius Through Scoped Credentials 

One of the most effective abuse prevention techniques is credential scoping. Instead of issuing one master key per partner, platforms issue multiple credentials scoped by: 

This ensures that if a credential is leaked, its impact is limited. Abuse can be isolated and contained without suspending an entire partner. 

Scoped credentials also align well with regulatory expectations around least privilege and proportional access. 

Monitoring Credential Behavior, Not Just Traffic 

Traditional API monitoring focuses on volume: requests per second, error rates, latency. Abuse detection requires a different lens. You need to understand how credentials behave over time. Sudden changes in usage patterns, access to unusual endpoints, or activity outside expected windows often indicate misuse long before rate limits are hit. This behavioral monitoring complements rate limiting and provides early warning signals without penalizing legitimate traffic. 

Emergency Revocation Without System-Wide Impact 

Despite best efforts, credentials will eventually be compromised. The difference between a minor incident and a major outage is how quickly and safely the platform can respond. 

Emergency revocation must be: immediate, targeted, reversible if needed. 

Platforms designed around static secrets often resort to blunt measures—disabling entire partners or environments. Platforms designed around dynamic credentials can revoke a single token or permission with minimal collateral damage. 

This capability is not optional in banking. Regulators expect it. 

Secrets management and key rotation are deeply tied to compliance. Auditors don’t just want to know that rotation exists—they want evidence. 

Engineering systems should be able to show: when keys were issued and rotated, who initiated the change, which credentials were active at any point in time, how access was restricted during incidents. When secrets are managed centrally and programmatically, this evidence is generated automatically. When they are managed manually, it is reconstructed painfully—if at all. 

Protecting APIs Without Punishing Partners 

A recurring theme in API security is unintended friction. Overly aggressive controls can punish well-behaved partners while failing to stop real abuse. 

The goal is proportional protection: strong defaults, adaptive controls, and predictable behavior. Partners should understand how credentials are managed, how rotation works, and how issues will be handled. Clear communication, documentation, and transparency are as important as cryptography. 

How OceanoBe Approaches API Protection in Financial Platforms 

At OceanoBe, we help banks and fintechs design API platforms where security, partner experience, and compliance reinforce each other. 

key rotation strategies that preserve SLAs 

We focus on engineering solutions that scale operationally, not just technically. 

Protecting banking APIs from abuse is not about adding more layers of defense. It’s about designing identity, secrets, and lifecycle management as core platform capabilities. When secrets are treated as living credentials, rotation becomes routine, abuse becomes containable, and compliance becomes demonstrable. 

In financial platforms, the strongest security systems are the ones that fail gracefully, recover quickly, and never surprise their partners. 

Engineering Patterns for Protecting Banking APIs from Abuse

Protect banking APIs from abuse using secrets management and key rotation—without breaking partner SLAs or regulatory compliance.

Secrets management and key rotation without breaking partner SLAs or compliance 

Open Banking APIs sit at an uncomfortable intersection. They must be open enough to encourage innovation, predictable enough for third-party developers, and strict enough to protect core banking systems. Rate limiting and throttling are where these tensions surface most clearly. Get them wrong, and you either expose critical systems to abuse or frustrate legitimate partners with opaque failures. Get them right, and you create a stable, fair, and scalable foundation for an ecosystem you don’t fully control. 

Here we explore how to design rate limiting and throttling for Open Banking APIs, not just implement them. 

Why Rate Limiting in Open Banking Is Different 

Rate limiting in a closed internal system is largely a technical problem. In Open Banking, it becomes a contractual and regulatory concern. 

Third-party providers depend on predictable access to APIs. Regulators expect non-discriminatory treatment. Banks must guarantee availability while protecting downstream systems that were never designed for bursty, external traffic. This means rate limiting is not just about numbers. It’s about fairness, transparency, and system resilience. 

Start by Defining What You’re Protecting 

Before choosing algorithms or tools, it’s important to understand what rate limiting is meant to protect. In Open Banking platforms, the usual bottlenecks are not the APIs themselves. They are downstream dependencies: core banking systems, payment engines, risk services, or legacy databases with strict throughput limits. 

Effective rate limiting acknowledges these constraints explicitly. Instead of applying arbitrary global limits, limits should be derived from real system capacity and known failure modes. This also means different endpoints deserve different treatment. A balance inquiry is not the same as a payment initiation, and your limits should reflect that. 

Consumers Are Not Equal — And That’s Okay 

One of the biggest mistakes in Open Banking implementations is assuming all API consumers should be treated identically. In practice, consumers vary widely. Some are high-volume, regulated TPPs. Others are small fintechs still in development. Some operate in multiple regions; others target niche use cases. 

Designing rate limits per consumer, per application, or per client credential allows you to reflect this reality without violating regulatory principles. The key is consistency and transparency: similar consumers should receive similar limits, and differences should be justifiable. 

This is where policy-driven rate limiting becomes more powerful than static thresholds. 

Rate Limiting vs. Throttling: A Useful Distinction 

Although often used interchangeably, rate limiting and throttling serve slightly different purposes. Rate limiting defines how much traffic a consumer is allowed to send over a given period. Throttling defines what happens when that limit is approached or exceeded. In Open Banking, hard rejections are sometimes necessary, but they should be a last resort. Well-designed systems use throttling to slow clients down gradually, giving them time to react and adapt. 

This approach improves overall stability and leads to better developer experiences. 

Token buckets, leaky buckets, sliding windows—there’s no shortage of algorithms. The important part is understanding their behavioral impact. For Open Banking APIs, burst tolerance matters. Third-party apps often perform short bursts of activity triggered by user actions. A rigid per-second limit can penalize legitimate use cases. 

Sliding window or token bucket approaches tend to work better because they allow controlled bursts while still enforcing long-term limits. Whatever strategy you choose, it should be documented and observable. If developers don’t understand why they are being limited, they will assume the platform is unreliable. 

Rate Limits Are Part of the API Contract 

In Open Banking, rate limits are not an internal detail. They are part of the API’s public behavior and, in many cases, part of regulatory compliance. 

Limits should be communicated clearly through documentation and, ideally, through response headers that expose remaining quota and reset times. This allows clients to build adaptive logic instead of hardcoding assumptions. Opaque limits lead to retry storms, support tickets, and broken integrations. Transparent limits build trust.

Handling Spikes Without Punishing Everyone 

One of the hardest problems in Open Banking is handling spikes caused by a single misbehaving or buggy client. Global limits often fail here. One consumer can exhaust shared capacity and degrade the experience for everyone else. Isolating consumers through per-client limits, combined with circuit breakers downstream, prevents localized issues from becoming systemic incidents. This isolation is a core principle of resilient Open Banking platforms. 

Rate limiting without observability is guesswork. You need visibility into: 

whether limits align with real system capacity 

These insights should feed back into platform design. Limits are not static. They evolve as systems scale, partners mature, and usage patterns change. 

In regulated environments, this observability also supports auditability. You can explain not only what happened, but why. 

Failing Well Matters More Than Failing Fast 

When limits are exceeded, how the system fails matters. Clear error responses, consistent status codes, and meaningful error messages help third-party developers recover gracefully. Silent timeouts or generic 500s do not. A well-designed Open Banking platform treats rate limiting as a communication mechanism, not just a protective barrier.  

Perhaps the most important lesson is this: rate limiting is never “done”. As Open Banking ecosystems grow, new use cases emerge. Traffic patterns shift. Regulatory expectations evolve. Systems that worked at launch may become bottlenecks later. Designing rate limiting as a configurable, observable, and evolvable system ensures the platform can adapt without disruptive changes. 

In Open Banking, rate limiting and throttling are where technical architecture meets ecosystem design. They protect systems, shape developer experience, and signal how much trust a bank places in its partners. Treat them as first-class design problems, not as defaults inherited from an API gateway. The most successful Open Banking platforms are not the ones with the highest limits—but the ones with limits that make sense, are clearly communicated, and evolve with the ecosystem they support. 

Designing API Rate Limiting and Throttling for Open Banking

Design API rate limiting and throttling for Open Banking to balance fairness, resilience, and developer experience at scale.

As fintech platforms grow, complexity rarely comes from business logic alone. It emerges from the surrounding ecosystem: inconsistent environments, bespoke CI/CD pipelines, fragmented observability, and security practices that vary from team to team. Over time, these inconsistencies slow delivery, increase operational risk, and make compliance harder to prove. 

Internal Developer Platforms (IDPs) have emerged as a response to this problem. Rather than adding another layer of tooling, IDPs aim to standardize how software is built, deployed, observed, and secured, while still allowing product teams to move fast. 

In regulated fintech and banking environments, IDPs are not a luxury. They are becoming a prerequisite for sustainable engineering at scale. 

Why Fintech Teams Are Turning to Platform Engineering 

Most fintech organizations reach a tipping point. Early on, teams move quickly by assembling their own stacks and pipelines. As the number of services grows, this autonomy turns into fragmentation. Onboarding new engineers becomes slow, environments drift, and security controls are applied unevenly. Platform engineering introduces a deliberate separation of concerns. Instead of every team solving the same infrastructure problems differently, a dedicated platform team provides a golden path for delivery. Product teams focus on business logic, while the platform handles the repetitive, high-risk aspects of software delivery. 

In banking, this separation also aligns well with regulatory expectations around control, consistency, and auditability. 

What an Internal Developer Platform Really Is 

An IDP is not a single tool or product. It is a curated set of capabilities exposed through self-service interfaces. From the developer’s perspective, it should feel like an internal product designed to make their work easier. At its core, an IDP standardizes how services are created, deployed, and operated. It provides opinionated defaults for infrastructure, CI/CD, observability, and security—while still allowing teams to extend or override behavior where justified. 

The success of an IDP depends less on technology choices and more on how well it abstracts complexity without hiding essential details. 

Standardizing Environments Without Killing Flexibility 

One of the biggest wins of an IDP is environment consistency. Fintech teams often struggle with subtle differences between development, testing, and production environments. These differences are a frequent source of incidents and failed releases. An IDP addresses this by defining environments declaratively, typically using Infrastructure as Code. Services are provisioned using the same patterns across all stages, reducing surprises during promotion to production. 

Importantly, standardization does not mean rigidity. Well-designed platforms allow teams to opt into additional capabilities or request deviations through controlled mechanisms, preserving flexibility while avoiding chaos. 

In many organizations, CI/CD pipelines evolve organically. Each team builds its own pipeline, leading to duplication, inconsistent quality gates, and uneven security coverage. An IDP elevates CI/CD to a shared platform capability. Pipelines are defined once, reviewed centrally, and reused across services. This ensures that every deployment follows the same standards for testing, scanning, approval, and rollback. 

For regulated environments, this consistency is critical. Auditors can inspect a single delivery model instead of dozens of bespoke pipelines. 

As systems become more distributed, observability becomes a foundational requirement rather than an operational afterthought. Without consistent logging, metrics, and tracing, teams struggle to diagnose issues or prove system behavior during audits. IDPs typically bake observability into the platform itself. Services deployed through the platform automatically emit standardized telemetry, follow naming conventions, and integrate with shared monitoring tools. 

This approach reduces the cognitive load on product teams and ensures that operational visibility is consistent across the organization. 

Security in fintech is often perceived as a blocker because it is applied late or inconsistently. Platform engineering flips this dynamic by embedding security controls directly into the developer workflow. Through an IDP, security policies become defaults: hardened container images, network isolation, secret management, and runtime policies are applied automatically. Developers don’t need to think about every control explicitly—they inherit them by using the platform. 

This model supports a shift-left approach while maintaining strong guardrails in production. 

One of the biggest cultural challenges in adopting an IDP is finding the right balance between autonomy and control. Too much control, and teams feel constrained. Too much autonomy, and the platform loses its value. Successful IDPs treat developers as customers. They provide clear documentation, sensible defaults, and fast feedback loops. Deviations from the standard path are possible, but intentional and visible. 

In banking environments, this balance is especially important. Teams need freedom to innovate, but within boundaries that protect the institution. 

The Role of Platform Teams in Regulated Environments 

Platform teams in fintech are not just infrastructure specialists. They act as enablers of compliance, resilience, and delivery speed. Their responsibilities often include translating regulatory requirements into technical standards, maintaining reusable templates, and continuously improving the platform based on developer feedback. This role requires both technical depth and strong collaboration with security, compliance, and product stakeholders. 

How OceanoBe Helps Build and Evolve IDPs 

At OceanoBe, we work with banks and fintechs that are adopting platform engineering to scale safely. Our teams help design and implement Internal Developer Platforms tailored to regulated environments. 

defining platform architecture and golden paths 

standardizing CI/CD pipelines and delivery workflows 

embedding observability and security by default 

integrating Kubernetes, cloud services, and IaC 

evolving platforms incrementally without disrupting teams

Our focus is on building platforms that engineers actually want to use—because adoption is what makes standardization effective. 

IDPs as the Foundation of Sustainable Fintech Engineering 

Internal Developer Platforms are not about adding bureaucracy or slowing teams down. When designed well, they do the opposite. They remove friction, reduce risk, and allow engineers to focus on what matters most. In fintech and banking, where reliability and compliance are non-negotiable, IDPs provide a way to scale engineering without losing control. They turn best practices into defaults and make good behavior the easiest path. 

As financial platforms continue to grow in complexity, platform engineering—and the IDPs it produces—will increasingly define which organizations can innovate safely at scale. 

Building Internal Developer Platforms (IDPs) for Fintech Engineering Teams

Fintechs and banks build Internal Developer Platforms to standardize CI/CD, observability, security, and delivery at scale

How platform engineering standardizes delivery without slowing innovation 

Feature flags are often associated with speed: faster releases, incremental rollouts, and rapid experimentation. In consumer tech, they are used liberally—sometimes casually. In banking and fintech, however, the same approach can quickly collide with regulatory expectations around stability, traceability, and control. Yet feature flags are not inherently incompatible with regulation. When designed and operated correctly, they can become one of the most powerful risk-mitigation tools in a bank’s engineering toolkit. 

This article explores how feature flags can be operationalized safely in regulated banking environments—supporting gradual rollout, controlled experimentation, and resilience, while remaining compliant by design. 

Why Banks Need Feature Flags, Even If They’re Wary of Them 

Banking systems are expected to change slowly, predictably, and safely. At the same time, product expectations are rising. Customers demand continuous improvement, regulators expect rapid remediation of issues, and engineering teams are under pressure to deliver without downtime. Feature flags help reconcile these forces by decoupling deployment from release. Code can be deployed to production without being activated, allowing teams to: 

enable or disable functionality instantly 

respond to incidents without emergency rollbacks 

In regulated environments, this control is not a luxury—it is a safeguard. 

The Compliance Risks of Naive Feature Flag Usage 

Despite their benefits, feature flags introduce real risks if implemented without discipline. Uncontrolled flags can lead to: 

undocumented behavior changes in production 

difficulty proving which logic was active at a given time 

“hidden code paths” that bypass testing or approval 

From a regulatory standpoint, these issues are unacceptable. Auditors expect deterministic behavior, clear change management, and traceability across environments. 

The challenge is not whether to use feature flags, but how to govern them. 

Feature Flags as Controlled Change Mechanisms 

In regulated banking platforms, feature flags should be treated as operational controls, not developer shortcuts. Each flag represents a potential change in system behavior and must therefore: be documented, ave a clear owne, be scoped explicitly (who, where, when), have a defined lifecycle.

When framed this way, feature flags align naturally with change management and operational risk processes. 

Gradual Rollouts Without Compliance Blind Spots 

One of the most valuable uses of feature flags in banking is gradual rollout. Instead of enabling a new capability for all users at once, teams can activate it incrementally—by customer segment, region, account type, or internal users first. From a compliance perspective, this is often safer than “big bang” releases. Issues can be detected early, and exposure remains limited. 

However, rollout logic must be deterministic and auditable. Randomized or opaque targeting can raise questions during audits. Many banks therefore restrict experimentation to controlled cohorts and prohibit uncontrolled A/B testing on regulated flows such as payments or authentication. 

A/B Testing Under Regulatory Constraints 

A/B testing is not forbidden in banking—but it must be applied carefully. Testing UI changes or non-critical UX flows is usually acceptable. Testing core financial logic, pricing, or risk decisions is far more sensitive. Regulators expect equal treatment, transparency, and explainability. 

A/B testing must be explicitly approved for each use case 

variants must be documented and traceable 

test duration and exit criteria must be defined upfront 

Feature flags can support this safely—but only with strong governance. 

One of the most compliance-friendly uses of feature flags is as kill switches. When incidents occur—unexpected behavior, external dependency failures, regulatory concerns—being able to disable a feature instantly without redeploying code can prevent outages and limit exposure. 

From an operational risk perspective, this is a major advantage. Kill switches reduce mean time to mitigation and provide a controlled response mechanism that auditors generally view favorably—provided it is documented and tested. 

Auditability and Traceability of Flag States 

A critical requirement in regulated environments is the ability to answer a simple but difficult question: What logic was active at this exact moment in time? 

Feature flag systems must therefore provide: 

correlation with deployments and incidents 

Without this, flags become invisible variables that undermine trust in the system. 

In well-designed platforms, flag state is treated as part of the system’s observable state—just like configuration or versioning. 

Separating Runtime Flags from Configuration Drift 

Another common pitfall is using feature flags as a substitute for proper configuration management. Feature flags are designed to control whether a capability is enabled or disabled, not to continuously alter how core business logic behaves. When flags start influencing decision paths, calculations, or risk logic in unpredictable ways, systems become harder to reason about, test, and audit. 

In regulated banking environments, it is essential to maintain clear boundaries between feature flags, configuration, and data. Flags should gate functionality, while configuration and data should drive behavior deterministically. This separation reduces complexity, limits unintended side effects, and preserves the predictability that regulators and auditors expect from financial systems. 

One of the biggest long-term risks is flag sprawl. Flags that are never removed accumulate technical debt and increase cognitive load. In mature banking platforms, feature flags have a lifecycle: creation with a clear purpose> controlled rollout> stabilization> removal once the feature is fully adopted.

Removing flags is not optional—it is part of maintaining a clean, auditable codebase. 

Embedding Feature Flags into CI/CD and Governance 

To remain compliant, feature flag usage must be visible across delivery pipelines. This often includes: 

flag definitions reviewed alongside code 

automated checks for undocumented or expired flags 

integration with change management workflows 

environment-specific restrictions (e.g. stricter rules in production) 

When feature flags are embedded into CI/CD and governance processes, they become an enabler of safe delivery rather than a source of risk. 

How OceanoBe Helps Teams Use Feature Flags Safely 

At OceanoBe, we help banks and fintechs implement feature flag strategies that balance agility with regulatory discipline. Our teams support: 

feature flag architecture and governance models 

integration with CI/CD and audit pipelines 

safe rollout strategies for payments, identity, and risk systems 

kill-switch design and incident response workflows 

cleanup and lifecycle management of flags 

We treat feature flags as part of operational architecture, not just tooling. 

Feature Flags Are a Control System, Not a Shortcut 

In regulated banking environments, feature flags must earn their place. Used carelessly, they introduce opacity and risk. Used correctly, they become one of the safest ways to evolve complex systems. The key is discipline: clear ownership, strict governance, and full observability. 

When operationalized properly, feature flags do not undermine compliance—they strengthen it, enabling controlled change in a world where standing still is no longer an option. 

Operationalizing Feature Flags in Regulated Banking Environments 

Banks can use feature flags for safe rollouts, experimentation, and risk mitigation—without compromising compliance or auditability. 

How to roll out safely, experiment responsibly, and mitigate risk—without violating compliance 

Strong Customer Authentication (SCA) under PSD2 is often discussed as a regulatory requirement or a UX challenge. In reality, it is an engineering and testing problem—especially in fintech platforms built on event-driven architectures. SCA flows span multiple systems: authentication services, tokenization engines, payment processors, notification channels, and third-party identity providers. Ensuring that these components work together reliably, securely, and compliantly requires more than unit tests or mocked integrations. 

This article explores how to design and test PSD2-compliant SCA flows using Testcontainers, Kafka Streams, and event-driven integration testing, with a focus on realism, traceability, and repeatability. 

Modern fintech platforms rarely implement SCA as a synchronous, single-step API call. Instead, SCA is orchestrated as a multi-stage process: 

A payment or sensitive action is initiated 

A risk evaluation determines whether SCA is required 

A challenge is triggered (OTP, push, biometric confirmation) 

The user responds via a separate channel 

The result is validated and correlated back to the original transaction 

Each of these steps often emits and consumes events. Kafka (or similar platforms) is commonly used to coordinate these flows asynchronously, ensuring scalability and resilience. From a testing perspective, this means SCA cannot be validated reliably with isolated service tests. It must be tested as a distributed, event-driven workflow. 

PSD2 SCA Requirements That Impact Testing 

PSD2 introduces concrete constraints that must be verifiable through tests: 

Authentication must use at least two independent factors 

Authentication results must be cryptographically bound to the transaction 

Exemptions must be applied deterministically and auditable 

These are not abstract requirements—they translate into specific system behaviors that must be validated under realistic conditions. 

In many fintech platforms, SCA is implemented using tokenization combined with 2FA. Instead of exposing sensitive transaction data directly, the system generates a short-lived authentication token representing the transaction context. This token is used across services and channels, binding the user’s authentication action to a specific payment or operation. The advantage of this approach is clear: tokens are easier to trace, revoke, expire, and audit—key properties for PSD2 compliance. 

Testing must verify not only that tokens are generated and validated correctly, but that they behave correctly across retries, failures, and concurrent flows. 

Why Testcontainers Matter for Fintech QA 

Mocking Kafka, databases, or identity providers hides many of the issues that occur in production. Timing, serialization, consumer lag, and message ordering are all critical in SCA flows—and none of them are visible with mocks. 

Testcontainers allow teams to spin up real dependencies as part of integration tests: 

This creates an environment that closely mirrors production, while still running deterministically in CI pipelines. 

For regulated environments, this realism is essential. Test results must be repeatable, explainable, and defensible. 

A typical event-driven SCA flow might look like this: 

A risk service consumes it and emits ScaRequired or ScaExempted 

If required, an SCA token is generated and an ScaChallengeIssued event is emitted 

The user response triggers an ScaCompleted event 

The payment engine consumes the result and proceeds or rejects 

Each transition is an opportunity for failure—and therefore a test case. 

Testing Kafka Streams with Real Dependencies 

Kafka Streams is often used to implement decision logic and stateful processing in SCA pipelines. Testing these components requires validating not only output events, but also state transitions. Using Testcontainers, teams can: 

Testing PSD2-Compliant Strong Customer Authentication with Event-Driven Architectures 

Test PSD2-compliant SCA using Testcontainers, Kafka Streams, and event-driven integration testing in fintech platforms

Test PSD2-compliant SCA using Testcontainers, Kafka Streams, and event-driven integration testing in fintech platforms. 

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