How does infrastructure affect fintech app performance?

You've built a fintech app with impressive features: instant payment processing, AI-powered fraud detection with real-time pattern recognition, millisecond-level risk scoring, and AI-driven personalization. Your product roadmap looks solid and your features check every box.

When you hit production scale, cascading infrastructure failures can emerge. Fraud detection times out during peak traffic. In failure scenarios, "instant" payments can degrade to multi-second delays instead of the expected millisecond response times. Dashboards show stale data. Regulators ask questions. Your engineering team scrambles to fix what turns out to be a core infrastructure problem, not a feature bug. This pattern appears across fintech companies: great features built on infrastructure that can't support them at scale.

Fintech apps face mounting pressures from multiple directions. Regulators expect tested operational resilience. Transaction processing must complete within strict timeframes—SEPA sets a 10-second maximum for instant payments. Fraud detection systems need to score risk in tens of milliseconds, sometimes even microseconds.

This guide covers the essential infrastructure capabilities fintech apps need, why features fail without proper infrastructure, and how to build systems that scale with your product.

Essential features & infrastructure fintech apps need to scale

Modern fintech apps need specific technical capabilities to remain competitive, and these capabilities are increasingly becoming table stakes for operating in regulated financial services.

Real-time transaction processing

Real-time transaction processing sits at the foundation for most modern fintech apps, but legacy batch processing systems struggle to meet current expectations. Internal processing often targets millisecond-level latency, while scheme-level execution timelines are typically measured in seconds (such as SEPA Instant Credit Transfer's 10-second maximum).

AI-powered fraud detection

AI-powered fraud detection runs as a continuous pipeline that ingests transaction events, enriches context, evaluates risk with machine learning models, and executes decisions within milliseconds. Many production systems use layered pipelines—combining rules, ML models, and behavioral features—to balance speed and accuracy.

Instant payment infrastructure

Instant payment infrastructure has moved from experimental to increasingly mandatory in many markets. The SEPA Instant Credit Transfer framework requires a maximum execution timeline of 10 seconds, while FedNow transaction volumes have grown rapidly since launch, reaching approximately 1.3 million transactions in Q1 2025, up from approximately 915,000 in Q4 2024.

Cross-border payments in many corridors now settle in minutes instead of days, a dramatic improvement in settlement velocity over legacy systems. Technical implementations typically provide real-time settlement, Request for Pay integration, and real-time visibility into transaction flows.

Advanced authentication systems

Advanced authentication systems extend beyond traditional passwords to multi-modal biometric approaches. Modern biometric systems support FIDO-compliant protocols and passwordless authentication standards while balancing security with user convenience.

Full compliance frameworks

Full compliance frameworks need infrastructure supporting PCI DSS v4.x (future-dated requirements effective March 31, 2025), SOC 2, GDPR, DORA (Digital Operational Resilience Act), NIS2 (Network and Information Security Directive 2), and the EU AI Act.

Why features & infrastructure must work together

Infrastructure failures can create cascading problems that make even well-designed features unusable. The failure patterns are often predictable: database bottlenecks cause connection pool exhaustion and lock contention, distributed architectures multiply latency across microservice hops, and replication lag creates consistency problems that violate financial accuracy requirements.

Consider what happens when a database starts handling thousands of queries per second during peak load. Query latency can spike from single-digit milliseconds to multiple seconds. At that point, real-time notifications become delayed notifications, instant transfers stall, and users see stale or incorrect account balances. The features may work well at the application layer, but the infrastructure underneath can't keep pace.

Trading platforms face similar challenges during market volatility when write volume spikes. High-frequency updates to the same records create "hot partitions" and lock contention, which can make core features unavailable precisely when users need them most. Microservice architectures compound the problem: a single user action requiring communication between 8-12 services can see latency multiply from under 100ms to several seconds during peak traffic.

The core tension is this: core ledger and balance paths in financial services apps often require strong consistency guarantees that many horizontally scalable architectures struggle to provide, though other workloads like analytics and personalization can work with weaker consistency. Choosing between consistency and scalability often creates infrastructure tradeoffs that surface as feature failures when load increases.

Infrastructure that powers fintech at scale

Many real-time financial apps need infrastructure that can process transactions in low-millisecond or sub-millisecond ranges while maintaining strict consistency guarantees, especially for latency-sensitive paths like fraud checks and balance updates. Traditional databases that provide ACID guarantees often struggle to scale horizontally, while NoSQL databases that scale horizontally may sacrifice the consistency fintech apps require.

Why in-memory architecture matters

In-memory databases store data in RAM rather than on disk, enabling sub-millisecond response times that disk-based systems struggle to match. This speed difference often matters for fraud detection, where models need to evaluate transaction risk and return decisions before settlement occurs.

For fraud detection workloads, vector similarity search can help in specific workflows like pattern similarity and entity resolution. You can compare transactions in an embedding space to identify similar patterns in historical data within tight real-time decisioning windows. Redis provides vector similarity search alongside traditional data operations in a single infrastructure layer.

High availability & disaster recovery

Financial services require infrastructure that stays available during failures. Multi-region database replication provides the disaster recovery capabilities regulators increasingly expect, with frameworks like DORA emphasizing operational resilience and tested continuity plans.

Redis Cloud offers a 99.999% monthly uptime SLA for Active-Active deployments, achieved via multi-region replication, so a regional outage doesn't take down your payment processing or fraud detection systems.

Cost-effective scaling with tiered storage

Pure in-memory architectures can become expensive at scale. Tiered storage architectures address this by combining RAM for frequently accessed data with SSD for less active data. Redis Flex uses tiered storage (RAM + SSD) to reduce caching costs by up to 80% in some workloads. For teams managing terabytes of financial data, Redis Flex delivers in-memory performance characteristics for active workloads at reduced infrastructure costs.

Simplified compliance through consolidation

Vendor sprawl can complicate compliance. Managing separate systems for caching, vector search, streaming, and time-series data often means multiple audit trails, integration points, and vendor relationships to maintain.

Unified platforms that consolidate these workloads can help simplify compliance posture. Fewer vendors often mean fewer audit trails and integration points to manage for teams dealing with PCI DSS, SOC 2, and other regulatory requirements.

Building fintech infrastructure that scales with your features

Your fintech app's features are only as good as the infrastructure supporting them. Real-time fraud detection loses much of its value if your database can't return results in under 100 milliseconds. Instant payments struggle to deliver on their promise if your system degrades during peak traffic. AI-powered personalization fails when inference takes longer than users will wait.

Infrastructure decisions compound over time

The infrastructure decisions you make early compound as you scale. Organizations that underestimate infrastructure requirements often face expensive retrofitting investments when they hit production scale. Those that design for real-time performance, horizontal scalability, and strict consistency from day one can avoid the costly architectural rework that many fintech companies undergo during growth phases.

When evaluating infrastructure, consider these benchmarks as reference points for production-grade performance:

• Throughput: Production systems handling financial workloads often need to sustain very high operation rates during peak load, especially for high-traffic payment and trading platforms
• Latency: Sub-millisecond response times for in-memory operations (not end-to-end transaction execution)
• Availability: 99.999% uptime for payment-critical systems, typically achieved through Active-Active multi-region replication
• Scalability: Linear scaling as transaction volume grows, without degradation under load

Infrastructure that meets these benchmarks today positions your team to handle tomorrow's transaction volumes without architectural rewrites.

Evaluating the economics

When building the business case for real-time infrastructure, consider factors like infrastructure consolidation (replacing multiple point solutions), reduced downtime costs, and engineering productivity gains from simpler architectures. Teams that can quantify these benefits typically find the economic case supports the technical requirements.

Start building faster fintech apps

Fintech app infrastructure determines how well your features work under production load. The patterns are commonly seen across the industry: database bottlenecks create cascading failures, distributed architectures multiply latency, and consistency problems surface as financial discrepancies.

As instant payments become mandatory in more markets and fraud detection latency requirements tighten, the gap between infrastructure that scales and infrastructure that struggles is likely to widen. Organizations investing in real-time infrastructure now position themselves to meet regulatory requirements and customer expectations as they evolve.

In-memory architectures help alleviate latency bottlenecks that make it harder to balance strict consistency requirements with horizontal scalability, especially for real-time fintech workloads. Sub-millisecond latency supports fraud detection decisioning, multi-region replication provides the availability regulators expect, and tiered storage makes the economics work at scale.

Redis provides the real-time infrastructure for these workloads, with the ability to achieve sub-millisecond latency for in-memory operations, 99.999% monthly uptime SLA for Active-Active Redis Cloud deployments, and built-in vector search capabilities for modern fraud detection pipelines.

Try Redis Cloud free to test these capabilities with your workload, or talk to our team about production deployments for financial services.