Markets Technology Awards 2025: Untangling the knots

Vendors jockeying for position in this year’s MTAs, as banks and regulators take aim at counterparty blind spots

All is not well in the world of counterparty credit risk management. If it were, the collapse of Archegos would not have inflicted more than $10 billion in losses on a handful of banks, leading indirectly to the collapse of Credit Suisse. The Bank of England would not have had to step in to support UK pension funds in September 2022, following a spike in gilt yields. And the London Metal Exchange wouldn’t have felt compelled to cancel $12 billion in nickel trades in response to a sudden price surge.

The response, from regulators and industry, is still taking shape. In April, the Basel Committee on Banking Supervision published a set of guidelines designed to address counterparty credit blind spots. Banks themselves recognise there is still much work to be done, and have begun reviewing their internal controls and processes.

Unfortunately, industry and regulators do not fully agree on the best way forward. Meanwhile, vendors – as is often the case – are caught between the rock of regulatory expectations and the hard place of shifting industry practice.

A number of challenges – and some opportunities – were highlighted in many of the counterparty credit-related submissions Risk.net received as part of this year’s Markets Technology Awards.

We need to move away from the simplified view that just increasing margin or capital held will be enough to deal with this risk

Stuart Smith, Acadia

“There have been many counterparty credit risk events over the past 25 years – from the failure of Long Term Capital Management in 1998, to the collapse of Archegos a few years ago – and the characteristics of each of these incidents has been very distinct,” says Rafik Mrabet, managing director at Cumulus9 and former head of risk at exchange and clearing group, Ice. “As much as banks want to enhance their counterparty risk management, it remains challenging for them to predict how their clients might be impacted by idiosyncratic market events, because of the multitude of asset classes or markets that they need to focus on.”

Mrabet’s comments hint at two core problems banks face.

First, they don’t always have as much data on their counterparties as they would like. Buy-side entities are not subject to the same disclosure requirements as banks – and can be particularly opaque in terms of leverage and concentrations – making it hard for their dealers to build up a full picture of counterparty credit risk.

Second, many large banks have historically operated in silos. Bringing the different strands of the business together in order to form a holistic view of total risk exposure, is proving harder than institutions had hoped.

There is a lot to do.

“The trajectory of risk management has been towards simpler models, driven by the move of capital models to new standardised approaches. This may now be at a turning point where firms are re-engaging with financially sophisticated models. While simplified models might be okay to capture risk at the 99th percentile, the tail risk can be much bigger and capturing that can be key to avoiding the worst loss events,” says Stuart Smith, co-head of business development at Acadia, an LSEG business.

Knowing your counterparty

Banks should have a good understanding of key assumptions made about a counterparty’s risk profile when establishing a relationship with them. This is one of the starting points for the Basel Committee guidelines – and it’s a principle banks broadly agree on.

But it’s harder than it sounds. Disclosure standards vary significantly between counterparties, and some entities, such as hedge funds, are required to divulge very little about their positions when trading with banks. For this reason, banks have pushed back against the Basel guidelines.

They are not alone. Almost unanimously, technology vendors agree the lack of adequate disclosure is a key obstacle to addressing deficiencies in counterparty risk modelling. However, there are differences of opinion on how the problem should best be resolved.

There will always be someone that is prepared to give more leverage for less disclosure, and this is a slippery slope

Alberto Micucci, S&P Global Market Intelligence

Mrabet believes the risk team needs to have the strength and authority to stand up to the front office, and limit business with counterparties that are not being open about their positions.

“They need to be able to say: if we have a counterparty that wants to work with us, and we are going to provide them with the services they need to trade the market, then they must maintain a certain level of transparency, otherwise we will impose strict limits on how much risk they can take on,” says Mrabet.

There is a competitive dynamic here: counterparties that are denied headroom at one bank will simply go elsewhere, as Archegos did.

“There will always be someone that is prepared to give more leverage for less disclosure, and this is a slippery slope, potentially leading to moral hazard,” says Alberto Micucci, director of financial risk analytics for S&P Global Market Intelligence. “At the same time, should regulators intervene and force greater disclosure for hedge funds or family offices? This could risk killing off an entire business by significantly reducing its profitability. It’s a very difficult problem to solve for.”

Some vendors believe they might have a role to play in finding a solution to this.

“Maybe a service provider could help them, where maybe we collect some sort of information from hedge funds and then we disclose something that is not private, maybe a level of concentration to banks that are particularly exposed to certain risks,” says Micucci.

Working together, regulators, banks and vendors may be able to “create a consensus where everyone is willing to share something”, he adds.

Breaking down the silos

The silo problem – a particular challenge for the larger banks – was raised consistently in MTAs pitches. Different pockets of counterparty risk often sit in different divisions of the bank, making it difficult to quickly aggregate exposure to a single client, or to understand how different clients might themselves share interconnected exposure. Organisations accept this needs to be remedied, but there is no quick fix. In many cases, breaking down the silos requires an overhaul of both technology and organisational structure.

“Of course we should be looking at risk holistically across the bank. A classic area that people often can’t do is to aggregate across the trading book risk and the banking book risk that they have with a counterparty. This can be a big problem with banks if they neglect to understand the total risk they have with a single counterparty,” says Smith at Acadia.

You could have two different businesses within the same group that are not allowed to disclose to each other because they are separate legal entities

John Pucciarelli, Acadia

For many banks this is still a work in progress – despite the Basel Committee’s separate and long-standing push on risk data aggregation.

“Large banks often have multiple complex systems, including, a mix of vendor systems and in-house systems, with a lot of plumbing between them. In those cases trying to do basic things like aligning the legal hierarchies is not a trivial exercise at all,” says Smith.

This is a legal issue as well, as John Pucciarelli, client director for strategic clients at Acadia, points out: “This is a question of how your trading books are legally structured. You could have two different businesses within the same group that may not be allowed to disclose to each other, because they are separate legal entities.”

Focus on PFE

The collapse of Archegos has also prompted a fresh look at the limitations of potential future exposure (PFE) calculations. In its guidelines, the Basel Committee noted a number of common gaps in PFE analysis – relating, for example to leverage, concentration and wrong-way risks. Banks are now looking for ways to make the calculation both more comprehensive and more granular.

“PFE modelling is a pre-trade decision support tool. Having a very strong PFE model is increasingly being viewed as giving a competitive edge. People want to use good PFE models to determine whether they should fight for a particular deal or walk away,” says Udi Sela, senior vice-president, product and field marketing for Numerix.

For Dmitry Pugachevsky, director of research for Quantifi, it is important to have PFE calculations as closely integrated as possible with the valuation of derivatives contracts.

Sometimes people get too lost in the weeds of risk modelling

Rafik Mrabet, Cumulus9

“XVA and PFE should be thought about holistically – the more holistic the better. When the XVA desk puts on a hedge, it makes sense to try to understand how this affects PFE,” says Pugachevsky.

He notes that closer incorporation of PFE into the valuation methodologies of banks may require significant changes to internal processes and controls, which could create political issues within some institutions.

“But from my point of view, as a provider of software and as a person who regularly speaks with traders and credit officers, it seems like there are a lot of potential synergies there, which would definitely be beneficial to the functioning of a bank,” says Pugachevsky.

Fundamentally overhauling the way in which counterparty credit risk is computed and managed will, for many organisations, be extremely challenging. Enter the stress test.

“Sometimes people get too lost in the weeds of risk modelling,” says Mrabet from Cumulus9. “The beauty of stress testing is that it can be as simple as picking particular events from the past and cross-referencing them – then you have something based on historical price movements.”

It can be that simple, but Mrabet recommends applying a range of different scenarios, and using them for different purposes. “You can have stress scenarios that you look at on a daily basis, and perhaps take decisions to restrict the risk exposure of certain clients based on the potential for large stress losses. Then you could have some scenarios that look at really implausible losses, which may prompt a risk manager to dig deeper to understand why an account is so sensitive to a particular scenario, and possibly prompt a conversation with the client,” he says.

 

Markets Technology Awards 2025: roll of honour

In total, there are 27 awards this year, with entries invited for a further six categories. There were either too few entries in the missing categories, or no compelling entrant.

The winners

Traded risk technology

Market risk management product of the year: SS&C Algorithmics

Market liquidity risk product of the year: Bloomberg

Counterparty risk product of the year: Numerix

XVA product of the year: S&P Global Market Intelligence

 

Front office regulation

FRTB-IMA product of the year: Murex

FRTB-SA product of the year: ActiveViam

Regulatory reporting product of the year: Regnology

 

Pricing/trading technology

Pricing and analytics: commodities: Quantifi

Pricing and analytics: equities: Finastra

Pricing and analytics: fixed income, currencies, credit: Quantifi

Pricing and analytics: structured products/cross-asset: Numerix

Trading systems: commodities: Orchestrade

Trading systems: structured products/cross-asset: Murex

 

Buy-side technology

Buy-side market risk management product of the year: Orchestrade

EMS provider of the year: 360T

Best execution product of the year: Tradefeedr

Buy-side ALM product of the year: Moody’s Investors Service

 

Data and other specialist categories

Market data vendor of the year: S&P Global Market Intelligence

Risk data repository and data management product of the year: Axoni

Electronic trading support product of the year: TransFICC

Best vendor for system support and implementation: Murex

 

Back-office categories

Clearing house support product of the year: FIA Tech

CCP risk/margin risk product of the year: LCH

Collateral management and optimisation product of the year: LSEG Post Trade

 

Innovation categories

Best user interface innovation: ActiveViam

Best use of machine learning/AI: CompatibL

Best use of cloud: ActiveViam

 

Methodology

Technology vendors were invited to pitch in 33 categories by answering a standard set of questions within a maximum word count. More than 135 submissions were received, resulting in over 89 shortlisted entries across the categories.

A panel of 10 judges – consisting of practitioners, analysts, and Risk.net editorial staff – reviewed the shortlisted entries, with judges recusing themselves from categories or entries where they had a conflict of interest or no direct experience.

The judges individually scored and commented on the shortlisted entrants before meeting in June to review the scores and – after discussion – make final decisions on the winners.

In all, 27 awards were granted this year. Awards were not granted if a category had not attracted enough entrants or if the judging panel was not convinced by any of the pitches.

 

The judges

Sidhartha Dash, chief researcher, Chartis Research

Murray Steel, chief operating officer, Qube Research and Technologies

Stan Yakoff, professor of law, Fordham University

Max Gokhman, head of MosaiQ investment strategy, Franklin Templeton Investment Solutions

Ahimsa Gounden, manager, market risk for insurers and financial market infrastructures, Reserve Bank of South Africa

David Germain, group chief information officer, QBE Insurance

Sudipto De, head of investment risk, Principal Asset Management

Navin Sharma, head of credit and market risk and HIMCO chief risk officer, The Hartford

Blake Evans Pritchard, Risk Technology Awards manager

Duncan Wood, editorial director, Risk.net