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Industry benchmarks for finance and accounting (F&A) teams show that three to five business days is the golden standard for closing timelines. Understandably, every finance leadership ahead of a closing quarter emphasises the need for speed, that today’s artificial intelligence (AI) can provide on scale. The counter is that speed could potentially lead to a drop in accuracy. Since both are correct, the tension between speed and accuracy has become an inherent feature of finance and account.
As agentic AI, generative AI, and other newer technologies continue to evolve in 2026, F&A personnel are increasingly pressurised to create a higher focus on scrutiny. With each passing year, regulators are tightening the grip on scrutiny while stakeholders expect real-time visibility and transparency. Being slow is a strategic disadvantage for enterprises looking to work efficiently and at scale. Under deadline stress, reconciliations are rushed and controls are bypassed. You can either be fast or compromise on accuracy.
The question finance leaders need to ask is not how to balance speed and accuracy. It is why the trade-off exists in the first place.
Sequential finance: The underlying constraint
The underlying architecture of F&A operations are built on a sequential model, which follows: data capture, reconciliations, adjustments, reviews, reporting. Each stage is dependent on its preceding counterpart, which was viable when financial data was sparse and when humans owned quality control mechanisms over siloed systems.
Sequential finance requires one function to wait for the previous to complete
However, waiting for one process to complete is no longer a bottleneck. Organisations now sit on vastly more data, processed across more systems, at far greater velocity than the sequential model was ever designed to handle. A model that’s dependent on data availability shouldn’t still be the underlying architecture of F&A.
Off-shoring was layered onto this same sequential model. The teams would do upstream processing and onshore teams do final reconciliations and review. However, when deadlines approached, the off-shoring team was “more hands for the crunch” rather than a structural fix.
Parallel finance: A fundamentally different operating model
Parallel finance shows promise to be a better alternative to sequential models. The concept is straightforward: instead of running processes sequentially, run them parallelly. After a transaction occurs, data capture, enrichment, and coding, all happen continuously.
The routine work, such as prepaid and accruals, are automated. Humans are now reviewing exceptions, which, for scrutiny and real-time visibility also happen continuously. Processing happening simultaneously, AI performing matching and anomaly detection for reconciliations controls the work spread across the period. It is a move towards orchestrated AI to deliver outcomes and build enterprise-level value propositions.
Parallel finance wherein all processes work simultaneously
This model also allows organisations to distribute work more effectively across time zones, specialisations, and delivery models that mix in-house capability with external partners where depth or scale is needed. Here, the external partners become part of a 24×5 production line: they handle continuous reconciliations, standard entries, and exception triage while onshore focuses on judgment calls, stakeholder communication, and scenario analysis.
Reducing dependence on period-end intensities
In a sequential model, most of the work happens during period-end in constricting sprints. Parallel finance dismantles the entire concept of period-end sprints. Since reconciliations run continuously, intercompany eliminations happen as they occur, and accounts can be managed routinely, period-end date marks the end of a process. What used an event in sequential finance becomes completely insignificant in Parallel Finance,
For leaders, this changes both the economics and the risk profile of the function. Headcount surges for peak periods become less necessary. The dependency on a handful of senior reviewers during the ‘closing window’ is reduced. Most importantly, off-shore teams can play a much richer role in reconciliations and data hygiene, so the onshore team can experience a more analytical month-end.
Also read: Guaranteed Australian CX with offshore operations
Implications for finance leaders
The speed-accuracy trade-off persisted because the model demanded it. Fixing the architecture solves both speed and accuracy aspects in F&A. With AI assuming a larger role in financial operations, leaders must approach this as a F&A transformation. It begins with asking the right questions: where does human expertise add value? Based on this, redesigning workflows because operating under the assumption that finance will work like it has always worked is flawed.
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