Batch processing is one of the most accepted constraints in financial services technology. Overnight jobs that aggregate transactions, update customer profiles, refresh risk scores, and flag suspicious activity have been the operational default for decades. Everyone knows the limitations. Everyone has learned to work around them.
The problem is that "working around" batch is getting more expensive every year — and the costs extend far beyond the obvious ones.
The obvious cost is latency. Fraud that happened at 2 PM is not flagged until the overnight batch runs. A customer who changed their behavior pattern in the morning does not have their profile updated until the next day. A risk signal that crosses a threshold at noon is not visible to the risk team until tomorrow.
The less obvious cost is complexity. Most institutions that need real-time capability in any area — fraud detection is the most common starting point — have built a parallel streaming infrastructure alongside their batch systems. They are now maintaining two separate data architectures: one for batch, one for streaming. Two sets of pipelines. Two sets of transformations. Two sets of monitoring. Two sources of truth that have to be reconciled against each other.
This parallel infrastructure is not a bridge to the future. It is a permanent cost center that grows more expensive and more fragile with every use case that requires real-time data.
The True Cost Structure
When we work with financial institutions on their data architecture, the cost of batch processing shows up in four layers. Most institutions are aware of the first. Few have quantified the other three.
Layer 1: Direct fraud and risk losses.
This is the number everyone knows. Global payment fraud losses are projected to exceed $40 billion by 2027. Institutions with real-time detection systems have demonstrated up to 60% reduction in fraud losses compared to batch-based systems. The math is straightforward — every hour of detection latency is measurable in losses.
Layer 2: Dual infrastructure cost.
Maintaining parallel batch and streaming systems is not twice the cost of maintaining one system — it is often more, because the reconciliation layer between them introduces its own complexity. Data engineers spend significant time ensuring that the batch and streaming versions of the same data agree with each other. When they do not, debugging the discrepancy pulls senior engineering time away from higher-value work.
Layer 3: Customer experience degradation.
This is harder to quantify but increasingly consequential. Customers who interact with a financial institution through multiple channels — mobile, web, branch, call center — expect continuity. When the customer profile updates overnight rather than in real time, every channel is working with slightly stale information. The result is not catastrophic in any single interaction, but it compounds into an experience that feels disjointed compared to what fintechs and digital-native competitors deliver. Research suggests that every 100 milliseconds of transaction latency reduces conversion by as much as 7%. In customer-facing decisioning — credit offers, product recommendations, fraud alerts — the experience gap between real-time and batch is not measured in milliseconds. It is measured in hours.
Layer 4: Opportunity cost on the AI roadmap.
Many of the most valuable AI use cases in financial services — real-time credit decisioning, dynamic pricing, behavioral fraud scoring, next-best-action engines — require real-time data as an input. When the data architecture is batch-first, these use cases are either impossible or require expensive workarounds that limit their effectiveness. The batch architecture becomes a ceiling on the institution's AI ambitions, even if the models themselves are sophisticated.
The Convergence Path
The architectural answer is not to replace batch with streaming. It is to converge them.
Modern data platforms allow batch and streaming workloads to run on the same pipeline, against the same data, with the same transformations and the same governance. This is not a theoretical capability — it is a production-ready architecture that eliminates the dual infrastructure problem entirely.
In practice, this means that the same pipeline that processes end-of-day aggregation also processes real-time transaction events. The same transformation logic that creates a daily risk summary also updates a live risk score. There is one source of truth, not two. One pipeline to maintain, not two. One set of governance controls, not two.
The migration does not have to happen overnight. Institutions can identify the highest-value real-time use cases — typically fraud detection, customer profile updates, and risk monitoring — and converge those first, while running less time-sensitive batch workloads on the same platform without modification. The architecture supports both modes natively. The transition is incremental, not disruptive.
We led this transition for a capital markets client that moved their fraud detection from overnight batch to real-time streaming. The results were significant: false positive rates dropped by more than 50%, and the system caught fraudulent transactions that the previous architecture would not have flagged until the following business day. Equally important, the engineering team that had been maintaining the parallel infrastructure was freed to work on the next set of real-time use cases rather than keeping two systems in sync.
The Decision Point
Batch processing is not going to stop working. The overnight jobs will continue to run. The reports will continue to generate. Nothing will break dramatically on any given day.
That is precisely what makes this decision difficult. There is no crisis that forces the move. There is only the compounding cost of not making it — fraud losses that are preventable, customer experiences that fall further behind every quarter, engineering capacity consumed by parallel infrastructure, and an AI roadmap that is structurally constrained by a data architecture designed for a different era.
The institutions that have already made this shift are operating in a fundamentally different competitive position. The question for those that have not is how long the compounding cost is acceptable.
This is one of six structural shifts we break down in our Financial Services Data & AI Modernization Playbook — the full architecture framework we use with every financial services client.