In June 2024, the US Commodity Futures Trading Commission fined a leading global commodities company $55 million for manipulating a US fuel oil benchmark, trading on nonpublic information misappropriated from a Mexican trading entity, and impeding employees' communications with regulators.
The case is relevant to a broader challenge facing commodity trading firms: surveillance cannot always be understood from the financial trade alone. The CFTC’s findings connected physical fuel oil activity, derivatives positions and benchmark trading. Seeing those relationships requires data from systems that were often designed for different purposes.
For firms trading across Asia’s increasingly complex venue landscape, that raises a question that goes beyond whether a surveillance platform is in place: can trade capture, position monitoring and surveillance see the same underlying activity?
Every new venue creates a maintenance obligation
Adding an Asian venue is rarely just another connectivity project. Each exchange brings its own products, rulebooks, data structures, reporting requirements and position-management conventions.
HKEX, for example, requires position aggregation across omnibus accounts. TOCOM has its own reporting and aggregation processes through JPX-CUBE. SGX requires firms to identify irregular activity involving traders or customers acting in concert. SHFE, DCE and other venues introduce further market-specific requirements.
Every one of those integrations becomes a permanent maintenance obligation, built by whoever had rulebook knowledge on that venue at the time. Six venues mean six separate points of failure, since rulebook knowledge from one venue does not carry over to the next.
The real regulatory exposure sits in lead time, not cycle time
When a risk analyst flags a fix that needs making, two clocks start: cycle time, how long the fix takes once a developer picks it up, and lead time, how long it sits in a queue before anyone picks it up at all. Elite teams hit cycle time under a day. Lead time, industry wide, runs to weeks, according to the DevOps Research and Assessment (DORA) benchmarking.
Lead time is what a regulator measures after the fact, the gap between your team spotting a problem and it actually getting fixed, and that gap is precisely what cost the previously mentioned commodity trading firm $55 million. Sixty three percent of developers say leadership does not understand what drives that delay, up from 44 percent the year before, according to Atlassian.[1]
Large enterprises run an average of 367 separate applications competing for the same developer time, according to Forrester.[2] Firms spend an average of 30 percent of IT budgets simply servicing debt like this, and a 2025 review of 47,000 applications found roughly a third of enterprise code too rigid to change without breaking something else, according to Protiviti and CAST research.[3]
What actually closes the gap?
More developers will not close it. They just sit behind the same queue, working the same backlog, split across the same six or seven venue specific connectors, no matter how many people you add to work through it. What closes it is one data model underneath trade capture, position monitoring, surveillance, and whatever else needs it, so a fix gets configured once, by the person who spotted the problem, rather than rebuilt separately for each system while waiting on an engineering queue. Gartner forecasts 80 percent of low code platform users will be business technologists outside IT by 2026, up from 60 percent in 2021, and the firms getting fined this cycle are the ones that have not made that move yet.[4]
A handful of architectural choices determine how much of it actually gets closed. Which exchanges a platform certifies directly against matters more than whether it holds certification at all: one certified against ICE and CME owns its own connector logic, while one routing through a generic aggregator inherits whatever that aggregator maintains, a live problem the moment TOCOM’s JPX-CUBE or HKEX’s aggregation regime needs a genuinely custom build.
Alert generation and report configuration are often built on the same codebase, forcing a report filter change and a new surveillance rule to compete for the same release window even though neither depends on the other, when separated properly a compliance officer’s fix stops waiting behind a risk analyst’s. Alert scoring based on machine learning tends to get sold on false positive reduction, but its more useful function on a fragmented multi venue stack is sequencing, determining which fix gets built first when several are queued at once. No-code configuration only delivers what it promises if it sits in front of the person who owns the outcome, not behind a UI that still requires an engineer to ship every change underneath it. And order book depth works the same way: two or three levels tells you something happened, but ten levels is what lets a firm actually reconstruct how, rather than just flag that it did.
BroadPeak already runs this way: certified directly against ICE and CME rather than inheriting an aggregator’s connector, alert generation decoupled from report configuration by design, ML based scoring instead of static thresholds, ten levels of order book depth, no-code configuration, and trade, order, and physical data on one model instead of separate systems checked against each other
None of this makes fragmentation disappear by itself, but it changes how much of the fix a compliance or risk team can make directly, without waiting on an engineering queue for changes that never needed one.
The venue count will only grow
The number of Asian exchanges, products and market structures available to commodity trading firms will continue to increase. The strategic question is therefore not whether to connect the next venue. That is inevitable. It is whether every new venue adds another standalone integration to maintain, or strengthens a common post trade data foundation.
BroadPeak takes the latter approach. Certified connectivity to 100+ global exchanges, brokers, E/CTRMs and trading systems brings trade and order data into a common platform. Trade capture, position limits and surveillance can then operate on the same underlying data rather than maintaining separate integrations and representations.
The objective is not to eliminate complexity from Asian markets. It is to stop that complexity being multiplied inside the post trade architecture.
Questions senior teams should be asking
- How many separate integrations maintain the same underlying trading data?
- When an exchange changes its data or reporting requirements, how many systems need to be updated?
- Do trade capture, position monitoring and surveillance operate on the same data?
- Can surveillance correlate orders, executions and bilateral or physical exposure?
- How much of the current post trade architecture is a permanent maintenance obligation created by an earlier point solution?
- When the next Asian venue is added, does it require another standalone integration or extend an existing data foundation?
- The cost of short term infrastructure thinking is rarely visible when the system is first implemented.
