For compliance teams at asset management firms, allocation review has quietly become one of the most thankless tasks in the working day. Hours are consumed wading through exception reports, only to find that the overwhelming majority of alerts carry no genuine risk.
According to ACA Group, the signal-to-noise ratio is broken, and the behaviours that regulators most want to see addressed are precisely those that current frameworks are least equipped to surface.
ACA Group recently discussed why ‘cherry picking season never ends’ and why allocation surveillance must evolve.
This is not a new problem. Conflicts of interest stemming from trade allocations have been a sustained priority for regulators including the Financial Conduct Authority and the Securities and Exchange Commission for years. Firms managing multiple funds, particularly those with different fee structures or exposure to illiquid instruments, continue to face scrutiny that shows no sign of easing. What is striking, however, is not the regulatory pressure itself, but how modestly allocation surveillance has evolved in response to it.
Despite being a well-understood risk category, allocation analysis remains one of the least mature components of many trade surveillance frameworks, a situation that is becoming increasingly difficult to defend.
The familiar controls that no longer cut it
Most firms continue to rely on the same core controls they have used for years: daily allocation exception reports drawn from order management systems, manual reviews of deviations from expected outcomes, and periodic sampling exercises that absorb significant time and resource. These approaches are well-intentioned, but they are fundamentally misaligned with how allocation risk actually manifests.
Single-trade exception reviews generate substantial operational noise. Partial fills, trade rotations, timing effects, and minimum notional constraints routinely trigger alerts that, on closer inspection, amount to nothing. They are operational realities, not behavioural warning signs. The inevitable result is that compliance teams spend disproportionate time investigating non-issues, genuine patterns become buried beneath false positives, and surveillance functions slide into a reactive rather than insight-led mode.
The real question is not whether any individual trade was perfectly allocated on a given day. It is whether allocation behaviour over time has systematically favoured certain accounts, funds, or strategies at the expense of others.
Allocation is behaviour, not just process
Allocation decisions are exercises in professional judgement, and judgement repeated over time leaves patterns. The real risk is not the isolated imperfect allocation; it is the persistent skew. Certain accounts consistently receive better fills. Less favourable outcomes are routinely directed elsewhere. Results diverge across comparable mandates with no clear or documented rationale.
These are behavioural signals, and they cannot be identified through snapshot reviews or isolated exception checks. Detecting them requires longitudinal analysis across extended time periods, cross-account and peer group comparison, and the ability to connect allocation outcomes directly to financial impact, including profit and loss. Without that analytical depth, firms risk missing exactly the conduct issues that regulators are actively seeking to uncover.
The structural gap in surveillance frameworks
Over the past decade, firms have invested heavily in modernising their surveillance infrastructure. Market manipulation detection, insider dealing controls, and communications monitoring have all advanced considerably. Allocation testing, by contrast, has been left behind. It frequently sits outside core surveillance platforms, persists within manual or semi-automated workflows, and lacks consistent, data-driven methodology.
The result is a defensibility gap, a meaningful distance between what firms believe they are monitoring and what they can clearly evidence and justify under regulatory challenge. As expectations continue to rise, that gap is becoming harder to sustain.
From exceptions to risk-based insight
Closing it requires a shift in approach. Allocation oversight must move beyond exception-driven review and towards a risk-based, analytics-led model that focuses on behaviour rather than process adherence. That means analysing allocation behaviour over time rather than as isolated events, measuring dispersion and deviation within defined peer groups, identifying persistent outliers and emerging trends, and linking allocation outcomes to financial impact.
This approach reduces noise, sharpens accuracy, and aligns surveillance activity with genuine regulatory risk. Most importantly, it redirects the judgement of skilled compliance professionals to where it adds the most value.
RegTech makes the shift achievable
The current RegTech landscape makes this evolution practical in a way it was not previously. Modern surveillance platforms can operationalise allocation analysis and embed it directly within broader market abuse surveillance frameworks, combining pattern detection, peer benchmarking, and statistical analysis within a single workflow.
Initial public offering allocation monitoring offers a useful illustration of what this looks like in practice. Allocations are typically expected to follow a pro-rata distribution. A more advanced analytical approach can calculate expected allocations based on each account’s proportion of net asset value within a peer group, compare those expectations against actual allocations across a block trade or series of trades, assess deviations over time rather than in isolation, and identify accounts that are persistently advantaged or disadvantaged. The focus shifts from any single imperfect allocation to sustained patterns that warrant investigation, a distinction that is critical to detecting conduct risk that would otherwise remain hidden.
A control that can no longer be treated as peripheral
Allocation analysis sits at the intersection of fair treatment, fiduciary duty, and market integrity. Firms that continue to depend on manual, exception-heavy processes risk misallocating compliance resources, overlooking genuine behavioural issues, and struggling to defend their oversight approach when regulators come knocking.
Those that embed allocation analysis within their core surveillance framework gain a meaningful advantage: reduced noise, earlier identification of risk, and a demonstrably modern, data-led approach to oversight. Cherry picking season never ends. But with the right surveillance strategy, it becomes considerably easier to spot.
Read the full ACA Group post here.
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