7 Complex Problems Apache Kafka Solves in Finance

Financial platforms rarely fail because of missing features. They fail because their systems cannot scale, evolve, or react fast enough to reality. This article breaks down seven complex, real-world problems that FinTechs encounter as they scale. See how these challenges manifest in real financial systems and how Apache Kafka can help you build a faster, more resilient, and more scalable business.

1. Delays in transaction processing

In finance, a “transaction” (card payment, bank transfer, ATM withdrawal) is not a single atomic step. It is usually a sequence of events that must pass through multiple independent systems:

• authorization (is the account or limit sufficient),
• ledger posting,
• balance and transaction history updates,
• customer notifications (push / SMS / email),
• reporting and monitoring.

When these components do not operate in real time, delays occur, resulting in:

• degraded customer experience,
• loss of trust in the financial institution,
• increased load on customer support.

It's easy to imagine, as I'm sure we've all been in a situation where we weren't sure if our payment had gone through. Just think back to how that made us feel.

Causes of transaction delays

The root causes of transaction processing delays usually fall into three categories:

1. Batch-based data processing instead of streaming – Many organizations still rely on periodic or overnight batch processing. While this may work for basic reporting, it is insufficient for latency-critical operations such as payments.
2. Excessive point-to-point integrations (System A → System B → System C) – Direct system-to-system communication creates tight coupling, bottlenecks, hidden queues, and makes it difficult to identify where delays originate. Even a minor slowdown in one component can cascade across the entire chain.
3. Uneven system performance – Systems scale differently and often fail to keep up during peak traffic periods such as Black Friday, holidays, or payroll days.

Moreover, when every system is directly connected to every other system, a change in one can break many others. Development slows down, risk increases, and operational costs grow – leading to organizational fear of change.

How Apache Kafka reduces delays in transaction processing

Apache Kafka transforms batch-oriented data flows into real-time event streams. This means, that instead of waiting for aggregated processing, Kafka acts as a live event channel:

• a transaction is emitted as an event,
• the information becomes immediately available to downstream systems.

This removes situations where certain systems only reflect updates after a delay.

One of Kafka’s key strengths is its ability to fan out the same event to multiple consumers in parallel.

A single TransactionCompleted event can simultaneously be consumed by:

• the mobile application (balance and history),
• notification services,
• fraud monitoring,
• CRM systems,
• reporting and analytics.

All of this happens without building dozens of fragile integrations, significantly improving system reliability and uptime.

2. Data Inconsistency Across Financial Systems

As mentioned before, financial platforms never consist of a single system. Payments, ledgers, AML, fraud detection, reporting, and customer notifications are usually built and scaled independently. Each of them stores its own copy of the data or a fragment thereof. The problem arises when these copies cease to be consistent with each other at the same point in time. Inconsistent data can lead to:

• incorrect limits,
• incorrect fees,
• delayed card blocks,
• incorrect credit decisions.

Kafka’s role in providing consistency across financial systems

Apache Kafka does not magically fix data – it changes how data is distributed across the organization.

Kafka acts as a single source of events. Every significant event (e.g. transaction, status change) is first written to Kafka. Systems do not communicate directly with each other; instead, they consume the same ordered event stream from a single place.

Kafka guarantees event ordering and retains events for a configurable period of time, allowing systems to catch up after downtime or delays. So, when scaling the platform, for example, introducing a new CRM system, no additional point-to-point integrations are required. The new system simply replays the event history directly from Kafka.

Read also: FinTech Software Development Services. How to Find the Right Partner?

3. Fraud Detection Too Slow

Fraud in finance is rarely a single even; it is a sequence of behaviors that together appear suspicious, such as:

• unusual location,
• unusual amount,
• rapid transaction velocity,
• behavior deviating from a user’s normal pattern.

For example, a customer makes a card payment in London, followed by another one in Seoul three minutes later.

Reaction time is critical. The difference between seconds and minutes often determines whether money is lost or protected.

Slow fraud detection has a significant impact on financial institutions:

• direct financial losses,
• increased operational costs,
• regulatory risk,
• reputational damage.

Why is fraud detection often too slow?

There are many contributing factors, but the most common ones are:

1. Delayed data delivery to fraud systems – In many organizations, transactions are first booked and only analyzed later. Often in batches. As a result, fraud attempts are detected after the damage has already occurred.
2. Signals scattered across multiple systems – Fraud indicators live in different places: transaction systems, application logins, device changes, geolocation data, customer history. If these signals do not converge at the same time, the risk picture remains incomplete.
3. Lack of real-time event history – Fraud systems need context: what happened moments ago and in what sequence. If data is overwritten or aggregated too early, behavioral patterns become difficult to detect.

How Apache Kafka enables faster fraud detection

Apache Kafka does not detect fraud itself but it enables fraud detection to happen quickly and effectively. It captures every event (transaction, login, device change) immediately and deliveres it as part of a unified event stream. This allows fraud systems to see full behavioral context, detect patterns, and make better, faster decisions.

Another key advantage of Kafka is event retention. Events can be replayed, enabling teams to “go back in time” and analyze sequences. This is critical for:

• improving fraud rules,
• training machine learning models,
• investigating incidents.

4. Lack of full event history (for audit and compliance purposes)

In finance, every significant action must be reconstructable:

• what happened,
• when it happened,
• in what order,
• and which system or actor initiated it.

If systems only expose the final state instead of the full sequence of events leading to it, serious issues arise in a heavily regulated industry like finance. This creates problems during audits and regulatory inspections, increasing the risk of fines and sanctions. Without a complete event history, institutions also struggle to analyze incidents effectively.

From a customer perspective, this often results in:

• long and frustrating dispute or complaint processes,
• slow decision-making (e.g. loan approvals),
• decreased trust and satisfaction.

How Apache Kafka addresses audit and compliance requirements

As described in previous sections, Apache Kafka stores all events as an ordered sequence with precise timestamps and retains them for a configurable period of time. This enables incident analysis and historical replay. For example, it becomes possible to reconstruct the exact account state at any point in time; even if the current state has already changed.

A real-life example? Let’s imagine that a payment institution under PSD2 must prove:

• why a transaction was approved,
• which fraud signals were evaluated,
• what customer state existed at that moment.

The institution can easily access those information as Kafka topics store:

• TransactionInitiated
• RiskScoreCalculated
• SCAChallengeTriggered
• TransactionSettled

This way, Kafka significantly reduces regulatory risk, simplifies audits, lowers compliance costs, and improves overall service quality.

5. Handling Traffic Spikes Without System Collapse

Traffic in finance is rarely uniform. There are moments when the volume of events increases dramatically within a very short time frame.

Market openings, interest rate changes, or crypto market volatility can cause:

• sudden 10x–100x traffic spikes,
• API timeouts,
• cascading failures.

Systems designed for “normal” traffic often choke under these conditions. Synchronous architectures amplify failures instead of isolating them.

Inability to handle traffic spikes leads to service outages and unavailability. You know its business and customer impact very well.

How Apache Kafka handles traffic spikes

Kafka acts as a safety buffer. During sudden traffic surges, it accepts and persists events, decoupling ingestion from processing. Downstream systems consume data at safe, controlled rates without being overwhelmed. This way, systems are isolated from one another and do not block or wait on each other.

Most importantly, no data is lost; even under extreme load.

With Kafka in place, organizations do not need to scale every system to peak capacity. Scaling Kafka and a few critical consumers is often sufficient, while less critical systems can catch up later.

6. Migrating Legacy Core Banking Systems

Core banking systems are often the oldest, most critical, and most fragile components of a bank’s infrastructure. Many are decades old, developed by multiple teams over time, and tightly coupled to dozens of dependent systems, including:

• customer-facing applications,
• card systems,
• accounting,
• CRM,
• compliance,
• reporting,
• partner integrations.

Many banks and FinTechs remain locked into monolithic cores, COBOL-based systems, or tightly coupled relational models. Because product must remain continuously available for customers, legacy system modernization is extremely risky. Especially since a change in one place can disrupt multiple processes simultaneously.

Nevertheless, the consequences of maintaining an outdated system are significant:

• high operational risk,
• stalled innovation,
• high maintenance costs,
• customer frustration caused by slow product evolution.

How Apache Kafka enables core banking migration

Apache Kafka helps address this challenge by acting as a decoupling layer between legacy and modern systems. Connecting Kafka to legacy systems is rarely a problem. Mature client libraries exist for many technologies (Java, Kotlin, .NET, Python, Go, C/C++, and others). When that is not enough, Kafka can be accessed via standard HTTP using REST proxies.

Kafka receives events from the legacy core and distributes them to new systems, which can be built alongside the old core without modifying its internal logic. This enables gradual, incremental migration:

• some processes continue to run on the legacy core,
• others are handled by new systems,
• Kafka synchronizes them via shared event streams.

The result:

• reduced risk,
• easier testing,
• faster rollbacks.

During migration, both legacy and new systems can run in parallel, consuming the same events. This allows teams to compare outputs, detect discrepancies, and build confidence in the new core.

Last, but not least, Kafka preserves event history and data consistency, supporting auditability and regulatory compliance throughout the migration process.

The end result is decoupling new systems from the legacy core, enabling further modernization. Meanwhile, customers experience uninterrupted service and gradual quality improvements.

7. Real-Time Financial Analytics Without Overloading Production Databases

This is one of the most practical reasons why FinTechs and banks adopt Apache Kafka.

Running real-time analytics directly on production databases leads to:

• degraded transaction performance,
• increased risk of outages,
• poor customer experience.

With Apache Kafka in place, analytical systems no longer query production databases. Instead, they consume data from event streams. Kafka acts as a safe replication and distribution layer for business events. When something happens – a transaction, balance update, or login – the system persists it to its production database as usual and simultaneously publishes an event to Kafka. Then, analytical systems connect directly to Kafka and process events in near real time, enabling fast business decisions without compromising system stability or customer experience.

Read also a case study: Building a resilient trading platform for a FinTech company

Conclusion

Apache Kafka is not popular in finance by coincidence. It is fundamentally aligned with the realities of financial systems.

Financial institutions operate in an environment where:

• events must be processed in real time,
• data consistency is critical,
• traffic patterns are unpredictable,
• failures are unacceptable,
• auditability and traceability are mandatory,
• systems must evolve without disrupting existing operations.

Kafka was essentially born for these conditions. Its event-driven model, durability guarantees, scalability, and decoupling capabilities make it a natural backbone for modern financial platforms. That is why it has become a default choice for banks, FinTechs, payment providers, and trading platforms worldwide – not as an experiment, but as core infrastructure.

At SoftwareMill, we provide Apache Kafka services on a daily basis, helping financial organizations design, build, and operate event-driven platforms that scale safely and reliably. As a Confluent Elite Partner, we support companies across the entire Kafka lifecycle: from architecture and implementation to optimization and long-term operation.

Reviewed by: Michał Matłoka