Ascent Technologies Releases MiFID II Compliance Channel

Ascent Technologies Releases MiFID II Compliance Channel

Solution provides firms with targeted regulatory processes compliant with MiFID II requirements

December 11, 2017, Chicago—Ascent Technologies, a leading regulatory technology (regtech) company, announced the launch of their MiFID II compliance channel today.
The MiFID II channel incorporates all level 1 and level 2 directives, as well as structured regulatory text including guidance, opinions, and Q&As that provide nuanced compliance instructions. Through natural language processing (NLP) and machine learning, Ascent’s algorithms transform raw regulatory data into actionable items for compliance departments.

“MiFID II is the greatest regulatory compliance challenge facing the financial industry at the moment, and Ascent’s technology can ease that burden,” said Brian Clark, CEO and co-founder of Ascent.  “Depending on firm type, trading activity, and a variety of other variables, there is an overwhelming number of possible unique compliance processes: more than 144 quadrillion in fact.  Ascent’s MiFID channel provides a domain-specific, vertical AI pipeline that transforms the mass of regulatory data into actionable knowledge. Our Navigator platform then allows users to create comprehensive and accurate compliance systems that are precisely tailored to each firm’s unique regulatory requirements. It saves saving time and money while helping to simplify compliance.”

Ascent’s MiFID II channel adds the European Securities and Markets Authority (ESMA) to ten other regulatory channels, including the CFTC, CME, CBOT, NYMEX, CBOE, ICE, GIPS, NFA, JAC, and FINRA.

MiFID II is the update to Europe’s Markets in Financial Instruments Directive, which includes a wide range of regulatory requirements designed to enhance transparency, provide greater investor protection, and improve the functioning of financial markets.  Market participants are spending an estimated $2 billion to ensure compliance with MiFID II when it goes into effect on January 3, 2018.

Ascent Technologies is a RegTech firm that helps customers simplify and automate their regulatory compliance programs. Its IntaaS (Intelligence-as-a-Service) platform uses a proprietary vertical AI and automation processes to convert the rules and documents of a regulatory body into units of intelligence that are distributed to customers online. Ascent’s RegTech products allow customers to automate their regulatory compliance function by helping them to identify, monitor, and manage their regulatory obligations, saving time and money and reducing regulatory risks. Learn more at

An introduction to Intelligence-as-a-Service

Creating “Intelligence”

As machine analytics improve and artificial intelligence begins to automate basic routine processes, businesses have begun automating work that was traditionally completed by humans. The rise of artificial intelligence is a function of two variables that predated it: quantity of data and analytics processing. The creation of technical architectures to capture and process large quantities of data (“big data”) shifted companies forward as they grappled with the complexity and quantity available; as a result, many were unable to draw insights. The explosion of analytics processing (machine learning, deep learning, heuristics) and analysis of the resulting information move the world forward once again.

If properly organized data creates information, and properly organized information creates knowledge, then the application of that knowledge to a specific use case (i.e. a customer’s facts) can create intelligence. It is this merging of big data and analytics that is fueling the artificial intelligence boom we see today.

The Commercial Implications of Intelligence

The Intelligence ecosystem is beginning to build steam. Articles from prominent venture capitalists like Jerry Chen in “The New Moats” (pointing to “Systems of Intelligence” being the “Next Defensible Business Model”) and Phillip Stauffer in “Dawn of the Ultimate Unfair Competitive Advantage” (“data and intelligence is the ultimate unfair competitive advantage for at least the coming 10 years”) describe the commercial implications of intelligence. Businesses will transform what was the traditional arena of service workers into “streams” of information. Similar to market data, or even television content, each is an ongoing stream of information presented in real-time, targeted to a specific customer.

While the key to creating intelligence includes merging domain expertise with technology, the downstream impact is still the same: firms must focus their sales and execution strategies on players in the market who would benefit from increased automation. Larger customers are typically the first adopters, followed-by cost-conscious middle-market competitors and then mass adoption. However, this need not be the case: because of the targeted nature of the offering, systems offering domain-specific intelligence will be available to customers of all sizes.

The outstanding question, then, is how to deliver this “intelligence” stream in an effective way for customers? The answer is “Intelligence-as-a-Service”, or IntaaS.

What is Intelligence as a Service?

Intelligence-as-a-Service* is the combination of automated domain expertise (using machine learning, analytics, and artificial intelligence) with traditional SaaS delivery platforms. Imagine if your CRM came pre-filled with customer information, or your taxes were pre-loaded and completed in your tax software: this is the promise of IntaaS. Software-as-a-Service reduced the marginal cost of delivering a unit to a customer to near zero. IntaaS reduces the marginal cost of both creating and delivering a unit to a customer to near zero.

Ascent’s Use Case

The distinction between businesses that utilize a standardized application of deep-learning models and our approach is centered on the variability of data. In systems where the inputs are consistent and standard, and the output is identical (think self-driving cars), deep learning works extraordinarily well. Even in those systems, miniscule failure rates are extraordinarily risky. In systems where the inputs are dynamic and the outputs are targeted (n=1), deep learning must be supplemented by domain expertise and domain-specific technology (think regulation and law).

This is what we have done at Ascent. By combining our knowledge of regulation and rules with data science and technology, we built automated processes that allow us to (1) create regulatory intelligence channels and (2) feed those streams through our SaaS delivery platform. Users have the ability to access a variety of regulatory channels and construct a custom regulatory ecosystem for their firm. The results are remarkable.

We can build intelligent regulatory processes in seconds, produce compliance manuals in a moment’s notice, and create compliance reports that used to take months in a matter of minutes. This is the power of Intelligence-as-a-Service: it will act as a digital backbone for any firm – at any time, from any place – with consistent, accurate information. It will allow humans to concentrate on unique and bespoke tasks, rather than organizational efficiency, unlocking employee creativity and encouraging problem-solving that computers are (as of now) unable to solve.

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Understanding the Recent Reg AT Update

Very few topics have been as volatile or highly debated as Regulation AT, the CFTC’s proposal for regulating algorithmic trading.


On November 25, 2016, the CFTC released an update to the proposed rulemaking, which it released to the public for comment.  A brief summary of the updates is listed below:

  1. The Commission proposed moving the regulatory structure from a three-tier system composed of the AT Person, FCM, and Contract Market (Exchange), to a modified two-tier system in which the AT Person could delegate its pre-trade risk controls to its FCM.
  2. The Commission proposed expanding the scope of the regulation to cover all electronic trading, not just narrowly construed algorithmic trading for AT Person purposes.
  3. The Commission proposed allowing the Contract Market, FCM, or AT Person the ability to set pre-trade risk controls, creating more flexibility in the regulatory process.
  4. The Commission proposed registering certain market participants that are not already registered with the Commission, who would become “New Floor Traders” subject to a volume threshold.
  5. In lieu of an annual compliance reports, the Commission proposed that AT Persons and FCMs would instead be required to retain certain records and submit an annual certification and that contract markets would be required to establish programs for evaluating FCMs’ and AT Persons’ compliance with Regulation AT.
  6. The Commission proposed updates to its algorithmic trading source code preservation requirements, whereby the Commission would only have access to these records by a special call approved by the Commission itself (not staff), and indicated that the provision was not intended to be used for routine submission.
  7. The Commission made a variety of changes to defined terms either included in the original proposed rulemaking or as a result of responses from market participants.
  8. The Commission proposed the inclusion of additional rules focused specifically on DCMs’ trade matching platforms and self-trade prevention tools.
  9. The Commission proposed revising a series of defined terms throughout the regulation.

Notably, the introduction of a Volume Threshold Test for registration under Reg AT and clarification regarding source code retention were intended to directly address market participants’ concerns.  The Volume Threshold Test, included in 17 CFR 1.3(x)(2), indicates an “aggregate average daily volume of at least 20,000 contracts” and also requires the person to include volume from “other persons controlling, controlled by or under common control with such person.”

With respect to the retention of algorithmic trading source code, the metadata included in the Commission’s new Notice of Proposed Rulemaking specifically notes that proposed section “1.81(a)(vi) did not require the transfer of all source code to the Commission or other third party for centralized storage,” nor did it require the provision of source code “to the Commission on a regular basis.”

While these updates will likely satiate the concerns of some market participants, further changes are expected given the impact of the new Administration of the U.S. Federal Government.  The dissent of Commissioner Giancarlo’s statement on the subject may provide some guidance, where he highlighted that the Commission’s adoption of this rule would be a “giant stumble backwards in undoing Americans’ legal and Constitutional rights.”

Unfortunately for those of us managing risk, it seems this issue is far from settled.  We know how challenging it can be to navigate an ever-changing set of rules and regulations.  That’s why we’ve created easy-to-use tools that track regulatory changes in real time—because the more you know, the less you have to worry.

If You Have 10,000 Regulations . . .

“If you have 10,000 regulations, you destroy all respect for the law.”  —Winston Churchill

One thing is certain:  The growth in the number of laws and regulations is staggering, and this growth—not only in depth of rules, but breadth of rules—tends to overlook the notion that their purpose is to curb bad behavior.  A nearly 100-year-old distinction in antitrust law offers us two useful types of legal doctrine to analyze the types of rules: (1) Per Se, and (2) Rule of Reason. The former is the equivalent of a strict ban, while the latter allows for carve-outs and exceptions. (It’s no wonder that larger corporations prefer the latter: they have the scale necessary to compete.)

Rules and regulations that are constructed with more carve-outs create an implicit regulatory tax with which only larger institutions can compete.  These discrepancies aren’t always intentional by regulators—and occasionally, they’re even the result of good intentions.  Regulation is a necessary component of a properly functioning market:  it outlines the boundaries of the sandbox so that all parties are properly informed.  Moving towards per se methods of regulation more clearly defines the boundaries, but this isn’t the direction that the rules have been heading.

Our contemporary problem isn’t necessarily a result of the level of complexity or style, but rather the frightening depth of regulation that often detaches the rules from the purpose or very law itself.  Instead of writing clean, easy-to-understand rules and regulations and updating them as necessary, regulators turn to burying changes and new components in interpretations.  While this may be a byproduct of lengthy and difficult legislative processes, those processes exist to protect the market from over-burdensome regulation and over-active regulators.

A prime example is the Volcker Rule.  Largely expected to re-install the structural protections that were part of the 37-page Glass-Steagall Act (which prohibited commercial banks from engaging in the investment business), the Volcker rule comprised more than 900 pages when published.  This creates barriers to entering the market and hurts financial institutions by significantly increasing their costs. Instead of the rule of reason approach with various carve-outs, exceptions, and confusion, we should adopt the per se approach to writing rules: simple, clear, bright-line regulation.

Another example comes from our own experience reviewing a variety of regulations and—importantly—their sources.  In 2016, we found Enforcement Actions from a self-regulatory organization that cited the Q&A section from guidance documents as the authority on which they are prosecuting.  Now, there are five ways a corporation could get fined, as illustrated below.  Isn’t it anti-ethical to prosecute any party, in any format, on anything other than a published rule?


Clearly, the inefficiency and improper usage of non-rules adds significant cost.  So how do we fix this?

  • First, regulators should be upfront about the rules and include all relevant information in the (simple!) rule itself. This means placing all liability in the rules themselves, not buried in Q&A and interpretive notices.
  • Second, regulators should proactively add and subtract rules to address market harm and consolidate existing rules wherever possible.
  • Third, it is imperative that regulators write cleaner and more complete rules, rather than carving out exceptions that even the biggest players struggle to navigate.
  • Finally, and most importantly, regulators should embrace the per se style of drafting: clear rules and structures should be preferred over difficult, dense, and exception-riddled rules that only cause confusion, unintentional violations, and costs for all parties.

Until these changes take place, companies will continue to struggle with compliance—and this struggle drives Ascent’s mission.  We know that technology can help to equalize the playing field, and we’re working to build solutions that simplify compliance so you can get back to business.

On the Quantitative Value of Diversity

Ascent recently had the privilege of showing our support of ChickTech, a women-in-tech nonprofit that empowers women to stay in tech and encourage girls to join.  As a sponsor of the Career Fair at the organization’s ACT-W Conference in Chicago, we had the opportunity to meet many bright, highly skilled, and generally awesome women and girls in tech, and we were reminded of the importance of diversity—especially in the technology world.

The startup and tech communities are notorious for their lack of diversity—so much so that this serves as common fodder for countless blog posts, articles, seminars, and speeches—and many who rightfully assert that diversity is important for the social fabric of a business often contrast it with the economics of success.  After all, root capitalism is irreverent to notions of fairness for anything except price.  How, then, do we reconcile these two juxtaposed ideologies?


The answer, surprisingly, is highlighted quite accurately both by nature and in the unique fabric of the American melting pot.  In nature, biodiversity plays a key role in allowing for competitive and naturally selective outcomes.  Diseases or predators that target specific variations of species are incapable of targeting those that develop sufficient biodiversity (in essence, natural selection).  This diversity creates an optimal outcome for the species:  namely, survival.  The introduction of gene-editing techniques and the risks of removing specific gene sequences without knowing what they could eventually protect against creates risks of bio-homogeneity—and, as grandma would say, the bigger they are, the harder they fall. Biodiversity protects species from extinction and ensures optimal outcomes given conditions out of our control.

A society, whether ancient or contemporary, is an amalgamation of the various people within it.  The definition of a society is “the aggregate of people living together in a more or less ordered community.”  The definition of a data system refers to an “organized collection of symbols and symbol-manipulating operations.”  Each is a unique structure made up of components that create a complex, interwoven equation that depends on each variable’s difference to generate an optimal outcome.

The melting pot that is America encourages the same social diversity.  Race, religion, creed, sexual orientation, age, national origin, and many more, create a culturally rich group of individuals.  The frustrations we endure, the iterative challenges to “getting it right” regarding an egalitarian society, the discussions, the failures, the successes, and the progress are equally as chaotic as the alignment of any set of diverse data points coming together to form a trendline.  The very struggle to create such a world is what defines the breadth of those who are able to live comfortably in it.  America’s struggle to ensure equal access (not equal outcomes) is the equivalent of the cogs grinding in a massively productive machine.

It may seem inconsequential to compare such grandiose notions of diversity to that of a commercial enterprise.  Nonetheless, the “firm” has cemented itself in American society as the single most effective means of wealth creation and efficient method of resource allocation ever conceived.

Turning to economics, the three main sources of production (or wealth creation) are land, capital, and labor.  Land is a constant, and capital (cash) is homogeneous.  What, then, separates different types of wealth creation?

The answer, quite simply, is labor (made-up by the people in it).  Ergo:

Production (Supply) = Land + Capital*B1 + Labor*B2.

Production (or supply) coupled with market demand produces, in efficient markets, proper prices. Nonetheless, supply and demand only determine price for a market of defined size.  When identifying a market, we must look at the attributes of the demand constituents to ensure they fit the needs of the customers in that market.  And, of course, each customer’s needs are made up by the idiosyncratic experiences, interests, and commercial behaviors of its participants.

If we want to design a product for the largest possible market (thereby maximizing market fit), we must appeal to the broadest group of people in said market.  As a result, one should consider the makeup of the market and expand the potential pool of buyers before analyzing supply-demand behavior.

In other words, the more a company understands or represents its customers in a given market, the larger the potential revenue.  As capital and land are homogeneous, the only alterable variable is labor.

What this encourages, then, is a labor curve that is diversified by experiences.  These come from creed, race, religion, gender, geography, and a litany of other attributes of each of the employees.  The more diverse the labor, the more optimal the labor trendline, and the larger the potential product-market fit.

In summation:  Diversity is good for people, and for businesses. It’s not only the socially conscious thing to encourage, it’s also your fiduciary duty.