BMLL, the
independent provider of historical order book data and analytics, is joining
forces with Features Analytics. The deal gives the company that hunts down market manipulation and abuse an access
to BMLL’s layers of market data order book records spanning global equities,
ETFs, futures and US equity options. The aim is to build a new class of
surveillance benchmarking products.
The core
idea is blunt: most firms running trade surveillance today don’t really know
how well their systems are performing. They set parameters, watch alerts pile
up, and spend significant time chasing false positives. BMLL and Features
Analytics are betting that a data-rich benchmarking layer can change that.
A New Way to Measure
Whether Surveillance Actually Works
The
collaboration centers on giving financial institutions the ability to measure
their own surveillance stacks, not just in absolute terms, but against changing
market conditions and competing solutions. Firms would be able to run
like-for-like comparisons of market abuse detection rates across different
systems using the same underlying order book reconstruction, producing
documentation that regulators can follow and challenge if needed.
That last
point matters. Regulators increasingly want to see not just that a firm flagged
suspicious activity, but how it reached that conclusion.
Explainability has become a compliance requirement in many jurisdictions, and
rebuilding the exact state of an order book at a given moment is the cleanest
way to produce that kind of evidence trail.
“Market
integrity and surveillance are a natural application layer on top of
high-quality historical order book data,” said Paul Humphrey, CEO of BMLL. “This
partnership reflects our focus on enabling sophisticated workflows on top of
BMLL data, now extending beyond market quality into market integrity and
surveillance benchmarking use cases.”
It’s a
challenge that extends beyond equities. LSEG recently
launched a cross-venue market abuse detection platform targeting MiFID instruments
and FX, combining client trade data with public feeds and news to cut false
positives.
False Positives Remain a
Costly Problem for Compliance Teams
Legacy
surveillance systems are known for generating enormous volumes of alerts, a
large proportion of which turn out to be noise. Manually reviewing those alerts
ties up compliance teams and drives up operational costs – a persistent
headache for banks, brokers and exchanges.
Features
Analytics has positioned eyeDES as a solution specifically designed to reduce
that burden, claiming its algorithms can cut false positives by more than 90%
compared to rule-based systems.
“Our
mission is to help financial institutions stay ahead of regulatory requirements
with our unique eyeDES AI technology and tools that deliver measurable coverage
and accurate detection of market abuse,” Cristina Soviany, PhD, CEO and
co-founder of Features Analytics, said.
The company
traces the platform’s AI methodology back to cancer detection research, which
required similar precision in distinguishing genuine signals from background
noise.
BMLL has
been steadily broadening its partnerships in recent months, from cutting ETF
spreads by 16% in a joint test with Ultumus to giving Saudi
institutional investors Python-native access to historical order book data via
Wamid.
BMLL’s Activate Program
Lowers the Entry Barrier for Partners
The
partnership was structured through BMLL’s Activate: Data Credits Program, which
lets qualified partners build and test products on BMLL’s order book
infrastructure with no upfront data license costs. Features Analytics received
a credit allowance covering access to BMLL’s Python research sandbox and Data
Feed, with a clear path to full commercial deployment once the build phase
wraps up.
It’s a
model BMLL has been refining as it broadens the ecosystem of products built on
top of its data. In early 2025, BMLL and Pico
launched an end-to-end solution combining real-time and historical data to help quantitative
analysts move from research to production, while a partnership
with Ultumus cut ETF spreads by 16% in initial tests by combining ETF index
data with granular order book analytics.
More
recently, BMLL launched
its Trades Plus execution analytics dataset, built with direct client input to simplify
transaction cost analysis and best execution workflows.
This article was written by Damian Chmiel at www.financemagnates.com.
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