The MiFIDII Countdown: Systematic Internalisers and Kx Technology

24 Apr 2017 | , , , , ,
Share on:

By Michael Gorman


In my meetings with dozens of financial institutions in North America and Europe over the past six months, I have observed that after a slow start, the pace of MiFID II impact assessments, particularly among Tier 1 banks, has increased. These organizations have recognized the potential negative impact of non-compliance and are now looking to implement third-party solutions, like the Data-as-a-Service platform offered by Kx.

One of the more impactful changes from MiFID I to MiFID II has emerged as the Systematic Internaliser rules. In a move to improve price transparency, the criteria for Systematic Internalisers has become much more stringent, and at the same time, the new rules require collection and analyses of vastly greater amounts of multi-asset data from across all trading desks.

In this article I address some of the key issues that are emerging as financial institutions begin to take the first steps toward addressing the challenges of the MiFID II Systematic Internaliser rules.

Systematic Internaliser Definition and Assessment

The concept of a Systematic Internaliser is not new. The framers of MiFID I saw great potential in the regime—but it never appeared. As of this writing, eleven investment firms are currently registered as Systematic Internalisers under MiFID I. By January 3, 2018, this number will shoot through the roof, as many, many more firms will find themselves within the new Systematic Internaliser thresholds in equities and, with MiFID II’s expansion, non-equities.

Numbers won’t be the only change. The regime will become more complex and place additional regulatory responsibilities on Systematic Internalisers, particularly in trade transparency. Those responsibilities in turn will create implementation challenges, particularly when it comes to IT change.

Armed with impact assessments and business requirements, firms are now delving into the systems changes necessary, and the types of different datasets included. A ‘look under the bonnet’ has shocked many firms, which didn’t realize the level of IT changes and the new categories of data they will need to collect and analyze. As the ‘go-live’ date approaches, it’s time to convert that shock into action.

Monitoring Systematic Internaliser Status

Given the increased transparency requirements of being a Systematic Internaliser, many firms naturally have decided for commercial and business reasons not to become a Systematic Internaliser in particular instruments. The consequences of inadvertently becoming a Systematic Internaliser are unforgiving: pre-trade transparency under Article 18 and more frequent post-trade reporting requirements. What’s more, the European Union-wide trading data is not static: the number of trades in a financial instrument, for example, can change quarterly, affecting the calculation of ‘frequent and systematic.’

In terms of granularity, firms will need to monitor activity per the most granular sub-classes provided in RTS 2. For fixed-income, this is particularly interesting and challenging. The European Securities and Markets Authority (ESMA) recently said in a Q&A:

“[W]here an investment  firm  passes  the  relevant  thresholds  in  a  bond  it  will  be  considered  to  be  a systematic internaliser in all bonds belonging to the same class of bonds according to table 2.2. of Annex III of RTS 2 issued by the same entity, or by any entity within the same group.”

For firms who don’t want to be a Systematic Internaliser, they must have a means for measuring exposure to all bonds within a particular class issued by the same entity or group. In the case of  corporate bonds, linking all companies in large, global multinational firms can be quite challenging. Consider Fiat Chrysler Automobiles NV whose two main subsidiaries oversee dozens of smaller companies, many of whom issue bonds. Good reference data management will be a critical part of any Systematic Internaliser monitoring solution.

A dynamic Systematic Internaliser monitoring solution, like the one offered by Kx, that incorporates multiple thresholds (i.e., the ‘actual’ and the ‘warning’ levels) and can provide real-time update on the firm’s status instrument-by-instrument, will be required. These tools must provide the insight that allows trading desks to adhere to their firm’s commercial decisions. And, these solution shouldn’t be siloed, but extensible and flexible enough to cover multiple uses like Systematic Internaliser monitoring as well as transparency and Best Execution reporting.

Capturing Manual Responses to Requests for Quotes

Systematic Internalisers are obliged to make public ‘firm quotes’ when they are asked for a quote and agree to provide one, as long as a liquid market exists for that instrument and potentially subject to exceptions in MiFIR Article 18. For trading desks that use voice/chat for quote generation, up until now, all quotes were not likely captured. Under MiFID II this will require a change in desk procedures and workflow. It will also mean that firms will need new mechanisms to capture data. A number of options are available, the most prevalent of which is a simple GUI which allows sales staff to manually enter quote details. Ideally, this GUI will be linked to the CRM system to reduce the number of fields requiring manual input. In all of our engagements, we have seen this issue arise again and again.

Consolidating Branch Data for Assessment

The capital markets have been uncertain about which level in an organization that the Systematic Internaliser assessments need to occur: branch or legal entity? In its latest Q&A, ESMA has clarified that the assessment occurs at the legal entity level because MiFIR refers to ‘investment firms.’ For firms operating multiple branches in the European Union, they will need to consolidate the branch-level data for purposes of determining Systematic Internaliser calculations, and, for that matter, Systematic Internaliser monitoring. This sounds simple, but we have witnessed difficulty given different IT systems deployed and differences regionally. It might not be an issue, but it is certainly worth exploring earlier rather than later.


A lot of ink has been spilt over the new Systematic Internaliser regime—including its effect on liquidity in certain markets, as well as the merit in achieving greater transparency in the price formation process. At this point, commentary must give way to implementation and overcoming the IT and data challenges the new, sharper regime brings.

The appeal of a strategic data solution to comply with Systematic Internaliser rules, as well as Best Execution rules and the rest of the new MiFID II standards, is that a centralized data capture and analytics platform, like the Kx Data Refinery solution, creates far-reaching efficiencies. It provides a complete suite of tools for managing data from ingestion through to consumption by multiple parties in a consistent, controlled manner. And having one centralized data repository eases time-consuming data and information governance issues, saving financial institutions time and money.


To complement Kx Systems’ product solutions, the company is supported by First Derivative’s Regulatory & Compliance consulting practice, which combines subject-matter expertise, strong product knowledge, and data & technology DNA to offer a range of consulting services that can accelerate implementation for MiFID II and MiFIR. Read more here.



Kx Product Insights: Inter-Trading Alert

5 Dec 2018 | , , ,

by Aidan O’Neill Kx has a broad list of products and solutions built on the time-series database platform kdb+ that capitalize on its high-performance capabilities when analyzing very large datasets. Kx for Surveillance is a robust platform widely used by financial institutions for monitoring trades for regulatory compliance. The Surveillance platform instantly detects known trading […]

Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

The Exploration of Space Weather at NASA FDL with kdb+

4 Dec 2018 | , , , ,

Our society is dependent on GNSS services for navigation in everyday life, so it is critically important to know when signal disruptions might occur. Physical models have struggled to predict astronomic scintillation events. One method for making predictions is to use machine learning (ML) techniques. This article describes how kdb+ and embedPy were used in the ML application.