For decades, payments have been defined by delay. Settlement windows, batch processing, cut-off times, and overnight reconciliations were accepted as normal constraints of financial systems. That era is ending. 

By 2026, real-time payments will no longer be an exception—they will be the default expectation. With SEPA Instant expanding across Europe, FedNow gaining traction in the US, and PIX-like models influencing global payment design, banks are under growing pressure to modernize their infrastructure to operate at real-time speed. This shift is not just about faster payments. It fundamentally reshapes how transaction processing, risk management, observability, and system reliability must be engineered. 

From Scheduled Processing to Always-On Payments 

Traditional payment infrastructures were built around batch jobs and predictable windows. Systems assumed downtime, deferred settlement, and asynchronous reconciliation. Real-time rails break those assumptions entirely. 

Instant payments operate 24/7, require irrevocable settlement within seconds, and leave no room for post-processing fixes. A payment either succeeds or fails immediately—while the customer is still waiting. 

This reality forces banks to rethink core architectural principles: 

systems must be always available, not “mostly available” 

latency becomes a product feature, not just a technical metric 

failure handling must be automatic and deterministic 

reconciliation must be continuous, not end-of-day 

SEPA Instant, FedNow, and the Global Push Toward Immediacy 

SEPA Instant continues to expand in scope and volume, with increasing transaction limits and regulatory pressure pushing adoption beyond early movers. FedNow accelerates a similar transformation in the US, while PIX has already demonstrated how real-time rails can become the dominant payment method almost overnight. 

What these initiatives share is not just speed, but architectural demand. They require banks and PSPs to process high volumes of payments with: strict SLAs measured in seconds, immediate liquidity checks, synchronous confirmations, real-time fraud screening, instant customer notifications.

Legacy cores were never designed for this operating model. 

Many banks attempt to “wrap” legacy cores with adapters to support instant rails. While this can work short term, it often creates fragile systems where real-time traffic stresses components designed for batch workloads. Modernization does not necessarily mean replacing the core overnight. Instead, successful institutions introduce real-time transaction layers alongside existing systems. These layers: 

This approach allows banks to evolve incrementally while meeting real-time expectations. 

Event-Driven Architecture as the New Default 

Instant payments naturally align with event-driven architectures. Every transaction becomes an event that triggers multiple reactions: balance updates, notifications, fraud checks, ledger entries, and reporting. 

Kafka and similar platforms play a central role by: 

> absorbing spikes in transaction volume 

> supporting real-time analytics and monitoring 

Instead of tightly coupled synchronous flows, modern payment platforms rely on streams that allow systems to react independently—without compromising consistency. 

Redesigning High-Throughput Transaction Layers 

Real-time rails expose performance bottlenecks quickly. Databases, network calls, and synchronous dependencies all come under scrutiny. 

High-throughput payment layers are designed around: in-memory processing for critical paths, asynchronous persistence where possible, partitioned data models aligned with Kafka topics, aggressive use of idempotency keys, back-pressure and circuit-breaking mechanisms. 

The goal is not just speed, but predictability under load. A system that processes 1,000 transactions per second reliably is far more valuable than one that peaks at 10,000 and collapses under stress. 

Real-Time Fraud and Compliance Cannot Be an Afterthought 

Instant payments are irrevocable, which means fraud detection and compliance checks must happen before authorization—not after settlement. This forces fraud engines, AML checks, and risk scoring into the real-time path. Streaming architectures make this possible by maintaining live risk profiles and evaluating transactions as events arrive. 

Instead of blocking flows with synchronous calls to multiple systems, risk signals are aggregated continuously, allowing decisions to be made within strict latency budgets. 

Observability in a World Without Downtime 

When payments run 24/7, traditional monitoring is insufficient. Banks must know immediately when latency creeps up, error rates spike, or downstream systems fall behind. 

Modern instant payment platforms rely on: distributed tracing across transaction flows, real-time metrics for throughput and latency, structured, correlated logging, automated alerts tied to SLAs. Observability becomes a core capability—not just an operational tool—supporting reliability, compliance, and customer trust. 

The shift to instant payments changes how teams build and operate systems. Engineering practices evolve toward: 

performance engineering as a first-class concern 

chaos and resilience testing under real-time conditions 

tighter collaboration between backend, data, and platform teams 

CI/CD pipelines optimized for zero-downtime deployments 

architectural decisions driven by latency and failure modes 

Payments engineering becomes a discipline of precision, not just correctness.

How OceanoBe Helps Banks Prepare for the Real-Time Era 

OceanoBe works with banks and fintechs navigating this transition—from batch-based systems to real-time, event-driven payment platforms. 

> design scalable instant payment architectures 

> implement Kafka-based transaction pipelines 

> modernize cores with real-time transaction layers 

> optimize Java-based payment engines for throughput and latency 

> embed observability and resilience into payment flows 

> ensure compliance without slowing down authorizations 

We focus on engineering payment systems that are fast, reliable, and ready for what comes next. 

By 2026, customers will no longer ask whether a payment is instant—they will assume it is. Banks that delay modernization risk falling behind not because of product features, but because their infrastructure cannot meet real-time expectations. 

The shift to instant payments is not a regulatory checkbox. It is a transformation of how financial systems are designed, built, and operated. 

Those who invest now—in event-driven architectures, high-performance transaction layers, and scalable real-time infrastructure—will define the next era of payments. 

The Year of Real-Time Everything 

Why instant payments are no longer a feature—but the foundation of modern financial infrastructure 

Instant payments are becoming the default. Learn how banks must modernize cores, adopt event-driven systems, and scale for real-time transactions

The 2026 Shift to Instant Payments as the Default 

2025 was not a year of incremental change in fintech engineering—it was a year of consolidation and acceleration. Many technologies that had been experimental or aspirational finally became operationally critical. Banks and fintechs were no longer asking if they should modernize, but how fast they could do so without breaking compliance, reliability, or trust. 

Across banking, payments, and digital financial infrastructure, engineering teams were forced to adapt to new realities: faster payment rails, stricter regulations, higher availability expectations, and growing system complexity. The result was a clear shift in architecture, tooling, and delivery practices. 

Let's look back at the most impactful engineering trends of 2025, how they reshaped financial systems, and what they revealed about the direction the industry is heading. 

From Experimentation to Industrial-Grade AI Engineering 

AI moved decisively from experimentation to production responsibility in 2025. What changed was not just model quality, but how AI was engineered into systems. 

Engineering teams increasingly adopted AI-assisted development for testing, refactoring, documentation, and operational insights. At the same time, AI-driven components—risk scoring, fraud detection, document understanding—were embedded directly into critical workflows. 

AI components became first-class services, not sidecar experiments 

auditability and explainability became mandatory design requirements 

AI pipelines were integrated into CI/CD and monitoring stacks 

For regulated environments, this meant stronger MLOps foundations: versioning, traceability, rollback strategies, and strict separation between model logic and business decisions. 

Real-Time Payments as a Baseline, Not a Feature 

Instant payments reached a tipping point in 2025. What was once a differentiator became a baseline expectation. 

This shift exposed weaknesses in legacy architectures. Batch processing, synchronous dependencies, and tightly coupled systems struggled to meet the latency and availability requirements of modern payment rails. 

> introducing event-driven architectures to decouple transaction flows 

> redesigning payment processing around idempotency and retries 

> using streaming platforms like Kafka to absorb spikes and ensure ordering 

The result was a broader realization: payments are no longer just transactional systems—they are real-time distributed systems, requiring the same engineering discipline as large-scale platforms. 

Event-Driven Architectures Became the Default 

In 2025, event-driven architecture moved from “modern option” to default pattern for scalable financial systems. Banks and fintechs increasingly used events to: decouple core systems from integrations, enable real-time analytics and personalization, support replayability and auditability, isolate failures and reduce blast radius.

This shift also changed team structures. Services became smaller, more focused, and independently deployable. The role of Kafka and similar platforms expanded from messaging to system backbone. 

Event-driven thinking influenced everything—from fraud detection and compliance reporting to lifestyle banking and embedded finance platforms. 

Observability Matured with OpenTelemetry 

As systems grew more distributed, observability could no longer be an afterthought. 2025 marked the broad adoption of OpenTelemetry as a unifying standard for logs, metrics, and traces. 

end-to-end visibility across microservices and event streams 

consistent correlation of transactions across systems 

faster root cause analysis for production incidents 

For financial institutions, observability also became a compliance tool. Traceability, audit trails, and operational transparency were no longer manual reporting tasks—they were built into the platform itself. 

Serverless and Knative Found Their Place 

Serverless platforms matured significantly in 2025, especially in regulated environments where predictability and control are critical. Knative, in particular, emerged as a pragmatic bridge between Kubernetes and serverless execution. Teams used it for: event-driven workloads, spiky, unpredictable traffic, background processing and integrations. Rather than replacing Kubernetes, serverless complemented it. Hybrid models became common, with core systems running on Kubernetes and peripheral workloads handled by Knative-based services. 

Regulation as an Architectural Driver: PSD3 and SEPA 2.0 

Regulatory change in 2025 was not just legal—it was deeply technical. PSD3 and SEPA 2.0 readiness pushed engineering teams to: 

redesign integration layers for flexibility 

improve exception handling and reconciliation flows 

strengthen auditability and data lineage 

Compliance increasingly shaped architecture decisions early, rather than being addressed as a final validation step. 

What Changed in How Teams Build Software 

Beyond technology choices, 2025 reshaped how fintech teams operate. 

There was a clear move toward: smaller, senior-heavy teams, stronger focus on architecture and testability, tighter collaboration between engineering, compliance, and product, delivery pipelines designed for continuous change, not big releases. The cost of poor architectural decisions became too high in a real-time, regulated world. 

Looking ahead, the trajectory is clear. AI will become more embedded, explainable, and regulated. Event-driven platforms will expand into analytics, identity, and personalization. Real-time risk and fraud systems will dominate backend complexity. Regulation will continue to drive architectural discipline. 

Technical partners will be chosen for execution, not just advice. 

Throughout 2025, OceanoBe worked alongside banks and fintechs navigating these shifts—from modernizing payment architectures and building event-driven platforms to embedding observability, AI, and compliance into core systems. 

Our experience this year reinforced one truth: successful fintech engineering is no longer about adopting tools—it’s about designing systems that can evolve continuously under pressure. 

As the industry moves into 2026, the organizations that thrive will be those that treat engineering excellence as a strategic capability, not a delivery function. 

The State of Fintech Engineering 2025 

A deep dive into the key fintech engineering trends of 2025—from AI and real-time payments to event-driven architectures and PSD3 readiness.

2025 Technology Shifts Reshaped Banking, Payments, and Digital Infrastructure 

Digital identity is now the backbone of modern financial services. From onboarding and payments to account recovery and high-risk transactions, trust must be established continuously, not just once. Banks and fintechs rarely rely on a single identity or compliance provider anymore. Instead, they orchestrate multiple eKYC, AML, identity verification, and Strong Customer Authentication (SCA) services across different geographies, risk levels, and use cases. 

While this improves coverage and resilience, it introduces a significant engineering challenge: how to orchestrate complex identity flows reliably, securely, and audibly at scale. 

This article explores the architectural patterns behind modern digital identity platforms and how banks can design systems that handle variability, fallbacks, and regulatory scrutiny without breaking user experience. 

Why Identity Has Become an Orchestration Problem 

No single provider solves identity verification perfectly. Coverage varies by country, document type, biometric quality, and regulatory interpretation. As a result, most institutions integrate multiple providers for: 

The challenge is no longer how to integrate a provider, but how to coordinate many of them within a single, coherent flow. 

Each onboarding or authentication journey becomes a dynamic process rather than a linear checklist. 

Engineering eKYC and AML as Modular Flows 

Modern identity platforms treat verification steps as composable building blocks rather than hard-coded sequences. A typical onboarding journey might involve document capture, biometric checks, AML screening, and risk scoring—but the exact order and providers used may vary. 

Architecturally, this is best modeled as a workflow, not a service call. Workflow engines or orchestration layers coordinate: which provider to call, what data to pass, how to evaluate results, when to retry, fallback, or escalate, how to log decisions for audit. This approach allows identity flows to adapt to new regulations, providers, or fraud patterns without rewriting core logic. 

Strong Customer Authentication as a State Machine 

SCA is often implemented too rigidly, resulting in poor user experience or unnecessary friction. In reality, SCA should be risk-driven and contextual. 

From an engineering perspective, SCA flows behave like state machines. Depending on transaction risk, user behavior, and regulatory requirements, the system may: allow frictionless authentication, trigger OTP or push-based verification, request biometric confirmation, escalate to manual review. Designing SCA as an orchestrated flow allows platforms to introduce progressive authentication instead of blanket enforcement. 

Handling Fallbacks, Exceptions, and Provider Failures 

Identity systems must expect failure—not as an exception, but as a normal condition. Providers can be unavailable, responses can be inconclusive, documents can be unreadable, and users can abandon flows. 

Robust orchestration layers handle this by: 

defining fallback providers per verification step 

supporting partial success and manual review paths 

preserving state across sessions and devices 

This ensures onboarding and authentication remain resilient even when individual components fail. 

Identity and authentication systems operate under intense regulatory scrutiny. Every decision—automatic or manual—must be explainable after the fact. 

Event-driven orchestration helps here. Each step emits structured events that record: inputs and outputs, provider decisions, timestamps and correlation IDs, risk evaluations, final outcomes. This creates an immutable audit trail that supports compliance reviews, dispute resolution, and internal investigations—without impacting live flows. 

Decoupling Identity from Core Banking Systems 

One common architectural mistake is embedding identity logic directly into core services. This creates tight coupling, slows change, and increases risk. 

Instead, modern platforms isolate identity orchestration into a dedicated layer that communicates via APIs and events. Core systems consume decisions, not raw identity signals. 

This separation enables: independent scaling of identity workloads, faster provider onboarding, safer experimentation with new verification methods, clearer security boundaries.

Security and Privacy Across Identity Flows 

Because identity data is highly sensitive, orchestration platforms must enforce strict security controls: 

Privacy is not a feature—it is an architectural constraint that shapes how identity systems are designed. 

How OceanoBe Helps Build Identity Orchestration Platforms 

OceanoBe works with banks and fintechs to engineer identity and trust platforms that scale across providers, regulations, and markets. 

event-driven audit and monitoring pipelines 

secure data handling for sensitive identity information 

We focus on turning fragmented identity integrations into cohesive, reliable platforms that support growth without compromising compliance. 

Conclusion: Trust Is an Engineered Capability 

Digital identity, eKYC, and SCA are no longer isolated checks—they are continuous, orchestrated processes that define customer trust. Banks that succeed treat identity as a platform capability, not a provider integration. With the right orchestration patterns, workflow engines, and secure integration layers, institutions can deliver compliant, resilient, and user-friendly identity journeys—ready for the next generation of digital finance. 

Digital Identity, eKYC, and Strong Customer Authentication 

The engineering challenge behind trust, compliance, and seamless customer journeys 

banks and fintechs orchestrate eKYC, digital identity, and SCA flows across multiple providers with auditability and resilience

Orchestrating Flows Across Multiple Providers 

Fraud has evolved, and today’s attackers don’t just exploit stolen cards or compromised accounts—they build synthetic identities, blending real and fabricated data to bypass traditional controls. These identities behave “normally” for weeks or months before committing fraud at scale, making detection far more complex than simple rule checks. 

For banks, PSPs, and fintech platforms, this reality has one clear implication: fraud detection must be real-time, adaptive, and deeply integrated into transaction flows. Decisions need to happen in milliseconds, often before authorization is approved, while maintaining accuracy and minimizing false positives. 

This article explores how modern streaming architectures enable real-time fraud defence, combining rule-based logic, machine learning, and event-driven systems—without compromising latency or customer experience. 

Why Synthetic Identity Fraud Changes the Game 

Synthetic identity fraud sits at the intersection of data quality, behavior analysis, and long-term monitoring. Fraudsters may create an identity that passes KYC checks, gradually builds credit history, and interacts with the system like a legitimate customer. Traditional batch-based fraud systems struggle here. By the time patterns are detected, the damage is already done. What’s needed instead is continuous risk assessment, where every event—transactional or behavioral—updates a real-time risk profile. 

This requires architectures capable of processing high-volume streams, correlating signals across domains, and responding instantly. 

Event Streaming as the Backbone of Real-Time Fraud Defence 

At the core of modern fraud platforms is event streaming. Every relevant signal becomes an event: card authorizations, account changes, device fingerprints, login attempts, velocity spikes, merchant category changes, or unusual spending patterns. Streaming platforms like Kafka allow these events to be ingested once and consumed by multiple downstream components in parallel. Risk engines, analytics services, alerting systems, and audit pipelines can all react independently without coupling fraud logic directly to transactional systems. 

This decoupling is critical. It ensures that fraud detection scales horizontally and evolves independently, while payment and authorization flows remain fast and resilient. 

Designing Low-Latency Streaming Pipelines 

In fraud detection, latency is non-negotiable. Decisions often need to be made within tens of milliseconds, especially for card payments or instant transfers. 

ingestion of transaction and behavioral events, 

enrichment with contextual data (user profile, device history, merchant risk), 

evaluation by rule-based and ML-based components, 

emission of a risk decision back into the authorization flow. 

To keep latency low, these pipelines are designed to operate in memory, avoid synchronous calls to external systems, and minimize cross-service chatter. Stateful stream processing—using tools like Kafka Streams or Flink—allows risk context to be maintained locally, avoiding expensive database lookups during authorization. 

Combining Rule-Based and ML-Based Risk Engines 

Effective fraud defence is never purely rules or purely machine learning. It’s a layered approach. 

Rule-based engines provide determinism and explainability. They enforce hard constraints such as velocity limits, country mismatches, or impossible travel scenarios. These rules are fast, predictable, and easy to audit, making them essential in regulated environments. 

Machine learning models add adaptability. They detect subtle behavioral anomalies, evolving fraud patterns, and correlations that static rules cannot capture. In streaming architectures, models are typically used for real-time scoring, producing risk probabilities that complement rule evaluations. 

The key architectural principle is orchestration. Rules and models operate side by side, contributing signals to a unified decision rather than competing for control. 

Synthetic identity fraud is often revealed through behavior over time, not a single transaction. Streaming architectures enable the continuous construction of risk profiles by aggregating events across sessions, devices, and channels. 

Because this state is maintained within the streaming layer, updates happen instantly, and decisions always reflect the most recent activity—without querying multiple databases during authorization. 

Latency, Accuracy, and Customer Experience 

One of the hardest challenges in fraud detection is balancing security with user experience. Excessive checks increase friction; overly aggressive models drive false positives. Streaming architectures help strike this balance by enabling progressive risk evaluation. Low-risk transactions pass through instantly. Medium-risk flows may trigger step-up authentication. High-risk events are blocked or flagged for review—all within the same event-driven framework. 

Because decisions are data-driven and contextual, platforms can remain both secure and customer-friendly. 

Observability and Auditability in Fraud Systems 

Fraud systems operate under intense scrutiny. Every decision must be explainable, traceable, and auditable. 

Event-driven fraud platforms naturally support this by: 

supporting regulatory audits without impacting live traffic. 

Strong observability is not just an operational benefit—it’s a regulatory requirement in financial services. 

How OceanoBe Helps Build Real-Time Fraud Platforms 

Designing and operating real-time fraud defence systems requires deep expertise in distributed systems, data engineering, and financial risk. 

OceanoBe helps banks and fintechs: design low-latency streaming architectures, implement Kafka-based fraud pipelines, integrate rule engines and ML scoring services,  build scalable risk profiles for synthetic identity detection, ensure observability, auditability, and compliance, optimize performance for high-throughput authorization flows. 

Our teams work at the intersection of real-time data, high-performance backends, and regulated environments, helping organizations stay ahead of increasingly sophisticated fraud. 

Modern fraud—especially synthetic identity fraud—cannot be addressed with batch jobs or siloed systems. It requires real-time, event-driven architectures that continuously assess risk, adapt to new patterns, and deliver decisions in milliseconds. 

Banks and fintechs that invest in streaming-first fraud platforms gain more than protection—they gain agility, insight, and trust. With the right architecture and the right technology partner, real-time fraud defence becomes a strategic capability, not just a control function. 

Real-Time Fraud Defence 

How modern fintech and banking platforms detect fraud in milliseconds—without slowing down payments

real-time streaming architectures detect synthetic identity fraud and monitor transactions with low latency and high accuracy

Designing Streaming Architectures for Synthetic Identity and Transaction Monitoring 

Digital banking is undergoing a fundamental shift. Customers no longer see their bank as a standalone destination for checking balances or executing transactions. Instead, they expect financial services to appear seamlessly within their everyday activities—shopping online, commuting, managing subscriptions, or interacting with digital ecosystems. 

This expectation defines Lifestyle Banking. It represents a move away from static online banking toward platforms that are adaptive, contextual, and real-time. Achieving this transformation requires more than UI improvements; it demands a new architectural foundation built around events, data, and decoupled services. 

Why Static Digital Banking Is Reaching Its Limits 

Traditional digital banking platforms are largely reactive. They respond to explicit user actions and expose precomputed data, often based on batch processing or nightly updates. Personalization, where it exists, is typically rule-based and slow to adapt to changing behavior. 

Lifestyle banking reverses this logic. Instead of waiting for customers to request information, the platform continuously observes what is happening and responds accordingly. A card payment, a location change, a recurring charge, or a change in usage pattern can all become signals that trigger financial insights, offers, or guidance. 

To support this model, banks must process and react to events as they occur—not hours later. 

Event Streaming as the Foundation of Real-Time Banking 

At the heart of lifestyle banking lies event-driven architecture. Every meaningful interaction becomes an event that is published once and consumed many times. Payments, app interactions, consent updates, merchant activity, and partner integrations all flow through an event streaming platform such as Kafka. 

This approach allows banks to decouple systems cleanly. Core banking platforms focus on correctness and durability, while downstream services independently consume events to power analytics, personalization, notifications, and partner integrations. 

Event streaming also enables replayability and traceability, which are essential for regulated environments. Historical events can be reprocessed to fix logic, generate new insights, or support audits—without touching production systems. 

Hyper-personalization depends on understanding customer behavior in real time. Instead of grouping users into static segments, modern platforms analyze streams of events continuously, enriching them with context such as merchant data, location, historical behavior, or risk indicators. These enriched signals feed personalization engines that can decide instantly whether to surface an insight, trigger an offer, or initiate a workflow. For example, a spike in subscription payments might trigger a spending alert, while a travel-related transaction could prompt insurance or FX recommendations. 

Crucially, these analytics pipelines are architected separately from transactional systems. This separation allows them to scale independently and evolve rapidly without compromising core banking stability. 

Microservices Powering Personalized Journeys 

Lifestyle banking platforms are composed of many small, focused services rather than a single monolithic application. Each microservice is responsible for a specific capability and reacts to events flowing through the system. Typical services include real-time insights, contextual offers, rewards engines, budgeting assistants, and notification services. Because these services are loosely coupled, teams can experiment, deploy, and iterate independently. New journeys can be introduced without redeploying the entire platform. 

This modularity is key to innovation at scale, allowing banks to continuously refine customer experiences while maintaining operational resilience. 

Extending Beyond Banking into Daily Life 

A defining characteristic of lifestyle banking is its integration with non-banking ecosystems. Modern platforms interact seamlessly with mobility services, e-commerce marketplaces, travel providers, and super-apps. From a technical perspective, this requires secure APIs, tenant-aware routing, event subscriptions, and strong governance controls. Event-driven integration allows banks to respond to external signals just as they do to internal ones, creating unified journeys across multiple domains without tight coupling. 

As personalization becomes more sophisticated, data sensitivity increases. Lifestyle banking platforms must embed privacy and security directly into their architecture. 

Key principles include: consent-aware event processing, fine-grained access control and tenant isolation, encryption and pseudonymization across pipelines, full observability and auditability. Event-driven systems support these requirements by centralizing policy enforcement at ingestion and processing layers, ensuring consistent application of rules across all services. 

Building ‘Lifestyle Banking’ Platforms 

How banks evolve from static digital channels to real-time, context-aware financial experiences 

Event-driven architectures enable lifestyle banking platforms with real-time personalization, secure integrations, and scalable data pipelines.

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