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Mar 20
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5 Ways To Prepare Your Business for a Bear Market

By Blog

In the midst of these unprecedented times, one thing seems certain: one of the longest bull markets on record is now over.

We are in the midst of unprecedented times, filled with uncertainty. Markets are whipsawing daily, the health of the economy — both short- and long-term — is in serious question, and it is unclear what the next week will hold, let alone the next quarter.

We at Ascent won’t attempt to answer these unanswerable questions. Instead, we want to acknowledge a reality that daily seems to be more and more certain: One of the longest bull markets on record has now come to an end. 

It will take time for governments to untangle the global uncertainties that grow more daunting by the day. In the meantime, businesses don’t have to feel paralyzed by inaction. Here are five ways to prepare for the new market environment.

READ ARTICLE: Creating Confidence in an Uncertain World

 

#1: Separate the Must-Haves from the Nice-to-Haves

The most immediate reaction to a tighter environment is to prioritize essential expenses and cut non-essential ones. This vital step sets the stage for the processes and procedures that follow.

You’ll likely be looking across your business for ways to reduce costs, but as you do so, it’s imperative you don’t undermine the fundamental and functional aspects of your business. This of course means protecting revenue-generating areas of your business, but it also means protecting the teams that protect your business.

Maintaining a stellar client-service reputation won’t matter if your business is overrun by cybercriminals — or if a lapse in compliance leads to a debilitating regulatory fine.

As you manage costs on a slimmer budget, insulate the areas of your business that make it functional so that they can help support the parts that make it successful.

#2: Focus on Efficiencies

Even after eliminating “non-essential” items, your list of expenses may still seem too long. Rather than cutting into the essentials, though, look for ways to improve efficiencies across your business.

Where is managerial oversight slowing down processes and creating unnecessary costs? How might you leverage hidden expertise within one department to help that of another? Perhaps buried in your IT department is a project-manager-certificate holder who can help organize the roll-out of your latest asset management strategy. Or maybe a junior associate has discovered how to streamline a burdensome administrative task but hasn’t had the opportunity to share it with other teams.

Now is the time to draw on the deep strengths of your teams so that they can empower your business to do more with less.

#3: Leverage Technology

Doing more with less is, as with many things, easier said than done. While it might be possible to ask some team members to wear another hat or share skills, departments likely won’t have the time and resources right now to wholly reimagine processes. 

Instead, look for ways technology can help.

Talk with teams about which parts of their jobs are heavy on mundane, manual labor and could potentially use automated support. The recent explosion in machine learning capabilities has revolutionized how automation can support different job functions.

We at Ascent are of course strong believers in automation. By automating regulatory knowledge creation, we’ve seen firsthand how technology can reduce errors, drastically improve efficiencies, and free up internal experts to focus on more critical functions. Other automation tools can be similarly transformative for different departments.

#4: Prioritize Employee Well-Being

Employees are the pillars that hold a business up, and a bear market puts significant stress on those pillars. In their professional lives, they’ll likely be asked to take on more in a bear market, even if they already have full plates. And, at the same time, they’ll be bombarded with worrisome headlines adding stress to other areas of their life too.

So the mental and physical health of employees should be a top priority. Employees with a clear mind will undoubtedly be happier, less distracted, and — as a result — more productive. 

Often, as firms look to buckle down on costs and increase efficiencies, the focus is too much on the number of hours worked and the output gained. What should also be considered is the potential expense of that work to employee well being.

Creating an environment that prioritizes employees and their health and empowers them with stimulating work will create a supportive atmosphere during a challenging time — and, ultimately, boost productivity in the process.

#5: Take advantage of the opportunity

Here’s a quote from Sun Tzu’s The Art of War that’s as cliche as it is true: “In the midst of chaos, there is also opportunity.”

Financial firms are known for reminding clients during downturns that it’s here the real money can be made — if one can bear the pain.

The same is true for businesses. Some of the most successful companies were started during a recession. By trimming excesses, improving internal procedures, empowering staff, and leveraging automation, you can position your business to take advantage of the potent opportunities emerging rather than being stuck in a paralyzed state of inaction.

READ ARTICLE: How Ascent Simplifies Regulatory Change Management with Automation

 

Most importantly, listen to your Risk and Compliance Teams.

Risk and Compliance professionals often serve as your crisis response team. They help companies implement new practices, create business continuity plans, and adapt to new environments. 

In the midst of a bear market, their work becomes more vital than ever. A recession or global crisis doesn’t mean that the regulatory wheels stop turning. On the contrary, regulators will still be publishing rule updates to keep up with the changing environment. And internal departments will likely be moving quickly to take advantage of opportunities in the marketplace. The last thing a business needs during uncertain times is increased risk.

As the critical functions of Compliance and Risk teams ramp up, automation can help reduce their workloads as much as possible.

At Ascent, we leverage emerging technology to automate the routine aspects of regulatory compliance to reduce risks and costs. Tools like these help reduce time-consuming tasks like regulatory monitoring and channel those efforts into more vital parts of compliance.

At a time when it’s essential to eliminate as many roadblocks as possible, our solutions can help a firm feel empowered rather than constricted by the rule of law.

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Mar 16
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Ascent Raises $19.3 Million in Series B Funding Round to Expand Compliance Automation Solution

By Blog

“We’re excited to advance Ascent’s mission to reduce the cost of compliance and protect the rule of law.” —Brian Clark, Founder and CEO, Ascent

Ascent, an AI-driven solution that helps customers automate regulatory compliance, announced today the closing of its Series B funding round of $19.3 million. The round was led by Drive Capital and includes investments from global banks ING and Wells Fargo, Series A investor lead Alsop Louie, and Series A participant The University of Chicago. Now entering its fourth year of operations, Ascent will use this round to fuel the continued growth of its team, product, and brand awareness in the financial compliance space.

“Keeping up with regulation is mission-critical for businesses,” said Brian Clark, Founder and CEO of Ascent. “While digital transformation of the enterprise is happening everywhere, compliance has been largely left behind, which is unthinkable considering the risk involved in compliance work. We are thrilled to partner with our Series B investors to help customers achieve certainty in their compliance operations, and we’re excited to advance Ascent’s mission to reduce the cost of compliance and protect the rule of law.”

Ascent’s proprietary RegulationAI™ processes and analyses regulatory text, doing automatically what takes individual Compliance Officers, consultants, and lawyers hundreds of hours to accomplish manually. 

By delivering customers actual regulatory knowledge in the form of a dynamic obligations register—in other words, the obligations and regulatory changes that apply specifically to their business—Ascent dramatically reduces the mundane and error-prone manual efforts of regulatory research and analysis that permeate the industry today. 

Ascent is actively exploring partnerships with government, risk management, and compliance (GRC) solutions with the ultimate goal of helping its customers achieve end-to-end compliance.

“The ‘RegTech boom’ of the past few years is evidence of the pressing need for innovation in not only financial services, but in every regulated industry.” —Andy Jenks, Partner, Drive Capital

“The ‘RegTech boom’ of the past few years is evidence of the pressing need for innovation in not only financial services, but in every regulated industry,” said Andy Jenks, Partner at Drive Capital. “We’re proud to support the Ascent team as they enhance their solutions and continue pioneering in an industry hungry for progress.”

Built using sophisticated machine learning and natural language processing technologies, Ascent’s groundbreaking solution helps businesses mitigate their regulatory and reputational risk while often obtaining seven and eight figure compliance savings for its customers.

“ING is keen to support innovative and visionary firms, such as Ascent, which will play an essential role in shaping the industry’s future.” —Benoit Legrand, CEO of ING Ventures and Chief Innovation Officer, ING

Benoit Legrand, CEO of ING Ventures and Chief Innovation Officer of ING says: “As the regulatory environment becomes increasingly demanding, so is the pressure on firms to remain compliant. In order to keep up with this ever-changing landscape and help relieve the mounting strain on resources, the financial services sector is continuously looking for more automated, intelligent and cost-effective ways to manage compliance. ING is keen to support innovative and visionary firms, such as Ascent, which will play an essential role in shaping the industry’s future.”

Ascent announced last month that major U.K. regulations – namely the Financial Conduct Authority (FCA) and Prudential Regulation Authority (PRA) — are now available on the Ascent platform, accelerating the company’s expansion into the U.K. market. Ascent is currently engaged with the Global Financial Innovation Network (GFIN) in a cross-border pilot that seeks to analyse the similarities and differences of a firm’s obligations across jurisdictions. With a rapidly growing team based in Chicago, Ascent serves major financial institutions around the world, including in the U.S., U.K., Australia, and Asia. 

To learn more about Ascent or to request an interview, please reach out to Vanessa Yeh at vanessa@ascentregtech.com.

Mar 11
Love2

Ascent Wins 2020 FinTech Breakthrough Award for Best RegTech Startup

By Blog

“We’re honored to be included among the many top companies across the FinTech industry to be selected for this year’s FinTech Breakthrough Awards.” —Brian Clark, Founder and CEO, Ascent

Ascent, an AI-driven solution that helps customers automate regulatory compliance, announced today that it has won the 2020 FinTech Breakthrough Award for Best RegTech Startup. 

FinTech Breakthrough is an independent organization that recognizes the top companies, technologies, and products in the global FinTech market through its annual FinTech Breakthrough Awards program. This year’s awards, now in their fourth year, drew over 3,700 nominations.  

Brian Clark, Ascent Founder & CEO, commented, “We’re honored to be included among the many top companies across the FinTech industry to be selected for this year’s FinTech Breakthrough Awards. This win, and the other industry recognition that we continue to receive, is a direct reflection of our commitment to the needs of our customers and helping them achieve certainty in their compliance obligations.”

Using its proprietary RegulationAI™, Ascent processes and analyses regulatory text, doing automatically what takes individual Risk and Compliance officers hundreds of hours to accomplish manually. By delivering actual regulatory knowledge – the regulatory obligations and ongoing rule changes that apply specifically to their business – Ascent helps customers reduce their risk while saving significant time and money. 

In a recent project with global institution CommBank, Ascent used natural language processing (NLP) and AI technologies to interpret and convert over 200,000 words of regulation into a set of digital, easy-to-consume tasks customized for the bank. As a result, CommBank saved hundreds of hours of manual processing across the business.

Ascent has been rapidly gaining momentum since its founding in 2015. Since its inception, Ascent has grown 100% YOY, secured $26.7M in funding, and expanded to 50 full-time employees. Ascent has customers all over the world, from Tier 1 and Tier 2 banks and other financial institutions. Ascent is continually expanding its regulatory coverage in order to better serve its customers worldwide.

To learn more, request a meeting with our Sales team below.


Mar 10
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Creating Confidence in an Uncertain World

By Blog

The Compliance Conundrum

We live in an uncertain world. This is something Compliance and Risk teams know all too well. 

We often hear from our customers about the anxiety and chaos that uncertainty causes in the world of regulatory compliance — uncertainty in how rules are changing, uncertainty in what rules are important and likely to be enforced, uncertainty in whether they are tracking all the right obligations, uncertainty in whether their business is properly complying with rules. 

Unfortunately, reducing that uncertainty traditionally costs a lot of money. The only lever most companies have to pull is to hire more people — compliance officers, lawyers, consultants — to keep track of obligations. These costs don’t scale well and have, at best, unclear ROI.

At Ascent, our goal is to insulate our customers from some of that uncertainty that has traditionally plagued them. To do this, we are building the largest programmatically-accessible body of regulatory knowledge in the world, and we are building the tools to scale this knowledge set as fast as regulators change their information, all while maintaining the quality and accuracy required for our customers to succeed. 

But just like our customers, we too face our own uncertainty challenge: How can we be certain, especially when working with datasets that are far too large to be checked manually, that our information is correct? 

Rather than running from this problem, though, we embrace it — and use technology to help solve it. We design our tools and strategies in a way that treats uncertainty as a reality that we can manage. Everything — from our knowledge production processes and internal and external product decisions to the technology that powers our scale and the governance around our machine learning modeling — provides levers we can pull to more effectively manage quality and scale for our customers.

READ ARTICLE: What the Tech? Machine Learning Explained

 

Knowledge Risk Framework

The first tool we use to manage quality and scale in the face of uncertainty is a simple knowledge risk framework: for any given step of our knowledge production process, what is the accuracy our customers need to be successful, and what is the most efficient way of maintaining that accuracy given our portfolio of tools? 

For example, consider the technology that powers self-driving cars. The accuracy the technology requires varies depending on the action the car is completing. If the task is parallel parking without hitting a bumper, 95% accuracy is probably sufficient. If it’s turning left into oncoming traffic, accuracy will need to be much, much closer to 100%. 

One of the key capabilities of our solution is the ability to analyze regulatory text, extract the obligations from within it, and automatically determine which of those obligations apply to our customers’ business. Making sure that this process is complete and error free is absolutely critical for our customers. Missing an obligation is like messing up that left turn — it’s not an option. 

So for this process, we do not rely purely on machine learning models, which always have some error rate. Instead, we combine machine learning with domain expert review and internal tooling, allowing us to dramatically accelerate the rate at which we conduct this decomposition while maintaining extremely high quality. Think of it as having a human driver in that self-driving car to supervise left turns.

By taking this approach we have eliminated more than 80% of the effort it takes to do this step manually, while still achieving the same or better level of quality than a fully manual process. 

In another example that’s less critical than identifying obligations, we have a step at which we classify regulatory documents into different internally-defined categories to help our customers filter. Because we have many different ways for our customers to find the right documents, the accuracy requirement for this specific step is much lower, which means we can use a machine learning model exclusively and sample a small subset of predictions periodically to estimate our accuracy statistically. 

By applying this knowledge risk framework, we know that we’re spending our resources to eliminate uncertainty where it matters the most for our customers, while scaling the value we provide much more quickly than most customers can do themselves.

Probabilistic Predictions and Measured Uncertainty

We also use math and statistics as a way of managing quality in the face of uncertainty. Our solutions are powered by machine learning models — essentially, algorithms that are trained how to complete a task using large sets of data. We give our algorithms a task — for example, determine whether this line of text within this regulatory document is an obligation or is supporting text. Our algorithms reference the vast archives of regulatory text on which we’ve trained them to predict an answer to that prompt — what’s known as a prediction. 

Using probabilistic predictions, our machine learning models can give us a measurement of how “uncertain” they are about that prediction. Think of it like a Jeopardy! contestant labeling each answer with a score of how confident she is that she’s right. If the model consistently predicts a similar answer with a very high probability, we can interpret the model as being more certain that its prediction is correct for that data point. This gives us the opportunity to break up our predictions into different measurable confidence “tranches” with different accuracies at different levels of confidence. 

For example, if we decide as a business that our risk tolerance for a particular step is very low — that it’s a “left turn into oncoming traffic” kind of step and we need 99% accuracy —  we can choose a confidence threshold above which we consistently achieve 99% accuracy. Any predictions above that threshold can be fast-tracked efficiently, whereas any predictions below that threshold can go into a queue for further human review. 

Initially, this could require a fair amount of manual labor on our part. But the power of machine learning models is that they continue to learn. So as we accumulate more human-reviewed data, our models continue to improve and the size of our “high confidence tranche” increases, driving up our overall efficiency while maintaining our quality.

Correcting for Model Drift

Another source of uncertainty is one all predictive models must inevitably contend with: model drift.

Machine learning models use historical data to make predictions on new data. Sometimes the relationship between historical and new data is relatively static — for example, making a left turn now isn’t materially different than making a left turn five years ago. Other times it can be much more dynamic — like comparing sunscreen sales in August to those in December. As our regulatory scope continues to expand, a possible drift between patterns in historical data and patterns in new data is something we have to guard against.

To do this we rely on process, technology, and some clever sampling techniques. We have built and continue to invest in a modern machine learning infrastructure that makes it easy for our data scientists to monitor model performance, retrain models with new data, compare multiple models against each other, and quickly deploy the models that perform the best. We also maintain a stream of human labeling to compare against our model labeling, even for models that are performing well. This allows us to constantly collect quality metrics, identify error modes and drift, and generate additional training data. 

We’ve designed our internal tooling to take advantage of smart sampling techniques to apply our domain-expert labeling time to the most information-rich data points, so that if we label even a fraction of a percent of a dataset we can maximize the value of that ground truth information and propagate it across the broader dataset. All of these strategies increase our confidence that the models we have deployed are the best they can be with the resources we have; in other words, we are able to maximize the leverage of our data science and domain expert labor across the uncertainty-quality tradeoff.

Managing Enterprise Risk

Finally, as a business we also think about uncertainty from the perspective of enterprise risk and the internal control frameworks we have in place to manage that risk. Even as a growing startup, we have invested time and resources into building out a robust Model Risk Management framework, established on many of the same guiding principles that financial institutions follow when using quantitative models like credit risk models. 

We have well-documented processes for reducing risk during all stages of model development:

  • When we develop models, we follow documented policies around our development standards, testing procedures, and stakeholder review.
  • We validate our models by using independent teams within the company and human review of model outputs.
  • To help govern our modeling practice and overall data generation approach, we use a model inventory, follow a detailed change management process, and have clearly identified roles and responsibilities. 

These operational investments reduce the risk that we inadvertently let entropy creep into our production system and gives us comfort that our process is working correctly.

We live in an uncertain world

For a self-driving car to be a safe option, how safe does it need to be? 90% accident free? 95%? 100%?

Logically, the answer is it needs to be safer than the safest human driver. No self-driving car will ever be 100% accident free, but neither will human drivers.

The same holds for regulatory compliance. The size of regulatory data is too large, and the world changes too quickly, to ever know with 100% certainty every detail of every word across all regulators. Even if any business could afford to pay enough humans to read through every document multiple times, they wouldn’t be able to pay to rule out human-error. 

But by acknowledging that we live in an uncertain world and by using state-of-the-art technology, smart process and frameworks, and the power of machine learning, Ascent can help financial institutions navigate the world of regulatory compliance more quickly and efficiently — and with a lot more certainty.

READ MORE: Ascent’s RegulationAI™ – Why It’s Different

 

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Feb 18
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What is SupTech and How Will it Change Compliance?

By Blog, Featured

What is SupTech?

SupTech, short for supervisory technology, is the application of emerging technology to improve how supervisory agencies conduct supervision.

Regulatory technology — or, RegTech — is in the midst of a full-blown revolution, overhauling how financial service firms handle regulatory compliance. 

Asset managers are automating laborious processes like disclosure production through robotic process automation. Wealth managers are streamlining the tiresome process of know-your-customer data collection and suitability analysis through compliance management solutions. And firms of all sizes and shapes are now able to automate the burdensome work of regulatory change management through AI-powered knowledge automation solutions.

In short, the industry is in the throes of digital disruption. The advances in technology that have upended so many other industries are doing the same to regulatory compliance. And, to date, financial institutions have been the ones to bear the benefit of this.

But that’s beginning to change.

The same technologies that have launched the RegTech industry over the last few years are now propelling a similar rise in a sector very, very closely related to RegTech.

SupTech, short for supervisory technology, is the use of those same breakthrough technologies but by supervisory agencies to help support supervision. In essence, it’s leveraging the technologies of RegTech for regulators themselves.

READ MORE: What is RegTech?

SupTech Solutions for a Data-Driven World

SupTech benefits from a serendipitous coincidence. Both the work of supervisory agencies and the technologies that are fueling our current technological revolution are underpinned by the same thing: data.

Data — and specifically the ability to aggregate and analyze large sets of it — is what has fueled the deep learning revolution of the last decade. 

Neural networks can crunch the large data sets of online images to create image recognition software. Machine learning algorithms ingest massive troves of regulatory documents to create knowledge automation solutions. For industries built around big data, technology now offers a plethora of ways to reduce errors and improve efficiencies.

This perfectly coincides with the modern approach to financial regulation, which is built around big data. But today’s approach also manages data in a manual, time-intensive, and usually backward-looking manner. 

Consider, for example, the lengthy onsite inspections regulators regularly conduct as a means of collecting data, and the cumbersome analysis process which, when it results in supervisory action, is often focused on incidents that happened months or even years ago.

SupTech offers that possibility to fundamentally change this.

Imagine a scenario where regulators receive data feeds directly from the firms they are regulating. Rather than having to go out and collect the data, the data is funneled into their systems — and is then analyzed by machine learning and natural language processing technologies in order to flag suspicious transactions or behaviors.

This is the dream of SupTech, which is quickly becoming a reality. It is built around two aspects of financial supervision: data collection and data analytics.

SupTech Use Cases

READ MORE: What the Tech? Machine Learning Explained

 

Streamlining Data Collection

Historically, data collection for regulatory reporting has focused on using standardized reporting templates — a holdover from the days of paper-based reporting. While these templates help organize data uniformly, they can be costly to update, making it difficult to keep them current with the fast-paced change occurring across financial services.

Additionally, these templates can be extremely inefficient. Because of how heavily regulated financial services is, one transaction may have to be reported to multiple regulatory bodies, meaning multiple reports have to be completed and submitted by financial institutions and then also ingested and analyzed by regulatory bodies, creating inefficiencies for all parties involved.

As regulations have increased, regulators have been forced to step up the frequency and granularity of the data they ingest. It’s quickly become clear that standardized reporting templates aren’t up to the challenge.

SupTech providers are already creating solutions. One, pioneered by the Austrian regulator OeNB, is AuRep (Austrian Reporting Service GmbH) — a reporting platform that can be used by both supervised entities and supervisors. It allows banks and other financial firms to input their data into the system to seamlessly send it to the OeNB.

This allows for a much higher level of integration between parties, improving the speed at which regulators can receive data and the granularity and accuracy of that data. But this methodology — known as data-input — is just one way to improve on the standardized template process.

Other SupTech solutions are investigating data-pull processes, where data is sourced directly from an institutions operational system and then pulled into the supervisory platform. Alternatively, a real-time access approach would let supervisors “see” the data at will rather than only during reporting periods, allowing them to monitor and interact with data without a time delay.

Data-input, data-pull, and real-time access approaches would all rely on APIs, short for application program interfaces — a technology making waves in other sectors of financial services as well.

READ MORE: Open Banking: What It Is, Why It Matters, and How RegTech Can Help

 

Overhauling Data Analytics

Once regulators have collected these massive pools of raw, unformatted data, the next question is what do they do with it. While it can be a challenge for humans to sift through and make sense of large data sets like these, this is where big data tools like AI and machine learning really begin to shine.

Here are just a few of the ways SupTech solutions are tackling data analytics:

  • Supervisors can use machine learning tools to create a “risk score” for supervised entities. FINTRAC, the Financial Transactions and Reports Analysis Centre of Canada, has created one such score, evaluating the risk factors related to an institution’s profile, compliance history, reporting behavior, and more.
  • Supervisors can also use network analysis to assess an entity’s exposure to money laundering risk. DNB (De Nederlandsche Bank), for example, analyzes transactional data in order to detect whether related entities are sending funds to the same party through different financial institutions. 
  • A number of regulators, including ASIC (Australian Securities and Investments Commission), the Bank of Mexico, and the FCA (Financial Conduct Authority), are leveraging natural language processing technologies to audit the promotional materials, prospectuses, and financial advice documents that are produced by financial institutions.

Beyond Data: Other SupTech Solutions

Data collection and analytics aren’t the only domains of SupTech solutions.

The FCA and BSP (Bangko Sentral ng Pilipinas) in the Philippines are both working on implementing chatbots to interact with supervised entities more efficiently. The chatbots would be able to answer questions for the supervised entities and also provide regulators with a wealth of information about what kinds of concerns supervised entities had.

The FCA is also looking into machine-readable regulations, what it is calling Digital Regulatory Reporting. In a tech sprint, the FCA developed a trial system that translated reporting rules into machine-readable language — non-English text, standardized so it can automatically be read by a computer system. Once translated, machines could then process these rules to compare them against a firm’s policies and procedures. 

This and other efforts acknowledge the heavy burden of regulatory change management that’s plaguing financial institutions — and the ability of technology to help alleviate this process.

The Future of SupTech

SupTech is undeniably still in its early days. In recent research conducted by the Bank of International Settlements, only half of the participating regulators surveyed had or were developing SupTech strategies. And, of those strategies, less than a third were operational, with most still being in the experimental or developmental stages. 

As SupTech advances, it will undoubtedly find new ways to make the work of regulators more accurate and efficient, but it will have serious questions to consider as well.

For example, by interconnecting regulators and supervised entities, will SupTech create new avenues for cyberattacks? And if supervisory technologies make a mistake, what will the cascading effect of this be?

Even more importantly, how much automation is the right amount for regulators? When implementing RegTech solutions, many financial firms have found the solutions work best when augmenting the work of Risk and Compliance teams, not replace it. It is likely that, in work as complex as that carried out by supervisory agencies, the same will be true for SupTech solutions. It will take patience and practice, though, to find that precise balance.

What is undeniable is that the processes of supervisors are ripe for digital disruption, much as those of Risk and Compliance teams were. It will be exciting to see how SupTech solutions add value to regulatory agencies in the years to come — and how they change the regulatory landscape in the process.

READ ARTICLE: How Ascent Simplifies Regulatory Change Management with Automation

 

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Feb 10
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Simplifying FX Compliance with RegTech

By Blog, Featured

(7 min read)

Regulatory complexity has exploded in the dozen years since the Global Financial Crisis. Massive new regulations, from Dodd-Frank to EMIR to MiFID II, have been brought down on the financial markets with increasing frequency and severity. The SEC set records last year with the highest number of enforcement actions against public companies in a decade, and the CFTC recently signaled it plans to move more in-line with the SEC. 

The forex market has certainly felt the effects of these massive waves of regulatory change. Affected in areas as widespread as price transparency and order execution to trade reporting and business conduct rules, FX traders now live in a world where, for any given transaction, they risk higher non-compliance fines for the regulations they know about, and also risk not knowing about all of the regulations that may apply. 

But FX traders don’t have to feel stuck between a rock and a hard place. RegTech — or, regulatory technology — provides solutions to these exact issues.

In this article we’ll dive into what RegTech is and how its solutions can revolutionize regulatory compliance for FX firms.

What is RegTech?

In its simplest definition, RegTech is the application of technology to improve the way we manage regulatory compliance. RegTech companies are employing machine learning (ML), natural language processing (NLP), blockchain, AI, and other technologies, in an attempt to streamline compliance processes, increase efficiencies, and lower costs and risks.

The FX marketplace is no stranger to the transformative power of technology. After all, it was technology that expanded FX from the trading desks of the few to the smartphones of the many. 

Now, Technology has developed to the point where it can take over some of the more labor-intensive aspects of regulatory compliance to produce more accurate results and at a lower cost.

READ MORE: What is RegTech and Why Does it Matter?

 

How is RegTech changing the FX industry?

RegTech solutions can be segmented into three categories: point solutions, workflow management, and knowledge automation. Each group is already making a profound impact on FX.

Point Solutions

Point solutions solve one specific regulatory compliance need. While more limited in scope than workflow management and knowledge automation solutions, the right point solution can have a powerful impact on an FX firm’s processes.

Here are just a few point solutions that can help the FX market:

  • Electronic identity verification tools to streamline and automate laborious Know-Your-Customer procedures
  • Anti-money laundering tools that can automatically flag suspicious transactions and dubious trading behaviour at a scale and speed not possible for humans
  • Reporting solutions to streamline the heavy burden created by MiFID II transparency requirements
  • Voice-to-text translation technology which decipher complex trader jargon and convert it to text, creating a searchable database which ML algorithms can then crawl to identify problematic transactions or trends
  • Data aggregation tools to collect instant messaging, email, and phone call data in a single place in order to better monitor for market abuse and to help meet regulatory requirements

Workflow Management

Workflow management solutions — specifically, governance, risk management, and compliance (GRC) platforms — are intended to help solve operational risk management needs. This may mean improving communication between team members, creating a better audit trail, providing a platform to reconcile obligations against policies and procedures, etc.

At their most basic level, GRC platforms act as a container, much like a customer relationship manager (e.g., Salesforce, Oracle, etc.,). They are known for their extreme flexibility, allowing users to customize the experience to their needs, but the specific components of each GRC platform helps determine how it may address a firm’s individual operational risk management needs.

For example, some GRC platforms are built around one specific aspect of risk management, such as risk assessment. Others drill down one level further and are structured around one particular regulator. Some are known for improving collaboration across business functions — aligning IT, operations, legal, and others by providing access to the same data within one framework — while others specialize in the ability to integrate with existing systems and legacy data.

FX firms will have to evaluate the factors of each to determine which is right for their specific needs, but GRC platforms can bring Risk and Compliance teams out of the quagmire of Excel spreadsheets and into the modern era.

Knowledge Automation

Knowledge automation represents the next frontier of RegTech — as well as one of the most powerful manifestations of how technology can help regulatory compliance.

Knowledge automation solutions are positioned upstream of both workflow management and point solutions, situated right at the very beginning of the compliance process. They help solve one of the most complex and intractable challenges of compliance: regulatory change management.

How are FX traders supposed to keep up to date on the constant flow of new regulatory updates being released? When towering new regulations like GDPR are released onto the marketplace, how can FX firms assess which aspects relate to their business in a quick and accurate manner? In short, how can FX traders have confidence that they’re trading compliantly?

At large banks and enterprise firms, these questions are answered by employing a small army of compliance analysts, consultants and lawyers to collect and sift through the dense legalese of regulatory updates. But smaller firms, who usually can’t afford such a hit to their bottom line, are instead left with a few exhausted Risk and Compliance officers, pouring over documents day-in and day-out while traders put trades in with fingers crossed. 

Change management solutions are now finally able to leverage technology to help solve these challenges. 

Some of these solutions act as a news feed, aggregating all relevant regulatory updates, proposed rule changes, enforcement actions, and speeches in a single place. They automate the laborious and time consuming horizon scanning aspect of change management.

Recent advances in AI technology can take us beyond this, though. At Ascent, we’ve created RegulationAI™ — a true innovation in regulatory technology — which leverages neural networks to automate both the organizing and the sifting processes of change management.

Neural networks are deep learning systems that are taught how to complete a task by being fed large data sets. Our knowledge automation solution treats the vast trove of existing regulatory documentation as a giant data set, runs that data through our trained RegulationAI™, which is then able to automatically determine which obligations apply specifically to a business — automating the transformation from data to knowledge.

Knowledge automation represents the next frontier of RegTech — as well as one of the most powerful manifestations of how technology can help regulatory compliance.

READ ARTICLE: The Rise of Data Privacy Regulation and How RegTech Can Help

 

Meeting the unique needs of FX

FX firms have the opportunity to get ahead of their competition by embracing regulatory compliance.

There are two aspects of the FX marketplace that make RegTech of special importance to it.

One is the fact that, by its very nature, FX operates within a global marketplace. 

This means that FX traders can be subject to even more rules and regulations than, for example, an RIA focused only on domestic operations. Through AI and machine learning, RegTech has the ability to simplify the impact of multi-jurisdictional compliance.

The other is that FX operates within a decentralized marketplace.

Outside of the country-based regulators that oversee FX — like FCA and CFTC — there are two organizations that are globally-focused on  “governing” or “guiding” organizations within the FX trading marketplace: FX Global Code and BIS Markets Committee. Both organizations provide guiding principles instead of rules due to the decentralized nature of the FX marketplace. The lack of formality in this regulatory framework has led to a lack of adoption and enforceability.

This represents a competitive advantage for FX firms. One need only look passingly at the path of regulatory compliance to see that, in all likelihood, the decentralized marketplace of FX will only be burdened by more and more regulations in the near future. FX firms have the opportunity to get ahead of this — and ahead of their competition — by embracing regulatory compliance.

READ ARTICLE: “But Does RegTech Actually Work?” 3 Ways Financial Firms and RegTechs Can Bridge the Trust Gap

 

Stop drowning in regulation.

The superhuman rate of regulatory change and the ever-increasing penalties for non-compliance don’t have to be a stumbling block for your business. RegTech offers a way to feel empowered rather than restricted by the rule of law.

LEARN MORE: Click here to learn about Ascent Solutions

 

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Feb 04
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Easing Asset Management’s Regulatory Burden with RegTech

By Blog, Featured

(5 min read)

For any given trade, asset managers risk higher non-compliance fines for the regulations they know about, and also risk not knowing about all of the regulations that may apply.

The bombshell that was the Global Financial Crisis of 2007-2008 radically remade the financial services landscape. It brought the global economy to its knees, set the stage for the longest bull market on record, and ushered in a new era of regulatory oversight.

And it is perhaps this last point which could have the most lasting effect. Because while the global economy has mostly recovered, and while at some point even this bull market will meet its bear, the new burden of regulatory oversight has transformed almost all sectors of financial services.

Asset management has certainly not been immune. Those managers looking to simply raise capital, bring in clients, and put up strong risk-adjusted returns are instead shouldering an increasingly complex regulatory burden.

A few facts can illustrate the deep strain of this weight: Every seven minutes a new regulatory update goes into effect. Also: Last year, the SEC published more than 2,750 enforcement actions, including 95 against public companies — the highest number in a decade. 

In short, for any given trade, asset managers risk higher non-compliance fines for the regulations they know about, and also risk not knowing about all of the regulations that may apply. 

But while the challenges of increased regulatory complexity may seem intractable and insurmountable, a nascent industry is determined to provide solutions to exactly these issues — the regulatory technology industry, or, RegTech.

In this article we’ll dive into what RegTech is and examine how its solutions can help asset managers escape from under the increasingly heavy weight of regulatory compliance.

What is RegTech?

In its simplest definition, RegTech is the application of technology to improve the way we manage regulatory compliance. RegTech companies are employing machine learning (ML), natural language processing (NLP), blockchain, AI, and other technologies, in an attempt to streamline compliance processes, increase efficiencies, and lower costs and risks.

Initially, many RegTech providers focused on solutions relevant to retail and institutional banks, especially around anti-money laundering and fraud protection. But the technologies of RegTech have advanced enough in recent years — particularly as relates to ML, NLP, and AI — that automation can now meaningfully streamline the work of Compliance teams. For asset managers, the timing of this couldn’t be better, as new regulation rollouts like MiFID II and GDPR only further raise the stakes for trade and transaction reporting.

READ MORE: What is RegTech and Why Does it Matter?

 

Revolutionizing How Asset Managers Handle Compliance

As RegTech has blossomed over the last handful of years, a plethora of solutions have popped up to help with solving problems across the regulatory compliance landscape.

Some of these operate more like point solutions, solving one particular problem for asset managers. For example, one massive lift facing many asset management Compliance teams is the production of hundreds of disclosures that firms are required to produce throughout the year. RegTech solutions now exist that employ robotic process automation (RPA) to turn this laborious, manual process into an automated one.

Similarly, RegTech can help streamline investor onboarding, reducing the process down to minutes and making it fully digitized. And NLP can be applied to the onerous task of communications management, digitizing vast troves of telephone conversations so they can then be mined via machine learning in order to catch potential red flags.

But the truly revolutionary power of RegTech lies beyond these point solutions. In its most impactful form, RegTech offers ways to leverage the big data of regulatory compliance in order to significantly streamline labor intensive processes, such as determining which obligations apply to your business.

For example, imagine if every time another seven minutes ticked by and one of those new regulatory updates was introduced, you were able to know nearly instantaneously whether it applied to your business and how it might impact your policies and procedures. Imagine if you were able to approach a massive new regulation like GDPR and — rather than feeling that it would take hundreds of hours and a meaningful chunk of your bottom line to untangle what it meant for your company — you were able to see a complete list of your obligations in mere minutes.

This is the power of Ascent’s RegulationAI™, a true innovation in RegTech. Our technology leverages machine learning and natural language processing to automate the most tedious and error-prone parts of compliance. 

Based on your firm’s unique profile, Ascent automatically delivers the obligations and rule changes that are relevant to your business, cutting out significant white noise so that you can focus on a much narrower set of obligations. 

Ascent is faster and more comprehensive than humans alone, saving Risk and Compliance Officers hundreds of hours of manually researching, reading, and analyzing regulation so that they can instead focus on the more critical tasks.

READ ARTICLE: “But Does RegTech Actually Work?” 3 Ways Financial Firms and RegTechs Can Bridge the Trust Gap

 

Reduce the Weight of Your Regulatory Burden with Ascent.

In a world where the trend toward passive management has pushed fees ever downwards, asset managers have to operate as efficiently and effectively as possible. Leveraging RegTech solutions can help reign in the skyrocketing costs of compliance, meaningfully reduce the time Compliance teams are spending on laborious, manual tasks, and protect against the risks of human error in the process.

LEARN MORE: Click here to learn about Ascent Solutions

Jan 28
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The Rise of Data Privacy Regulation and How RegTech Can Help

By Blog, Featured

(7 min read)

If data is money, it’s often left sitting out in the open.

Ascent founder and CEO Brian Clark has a hypothetical question he often likes to ask new people when meeting them: If you were given a giant bag of money, what world problem would you solve? 

Homelessness, poverty, world hunger — there are ample crises to choose from. But what Brian’s really interested in is your answer to his second question: If that bag of money were then taken away, and you were instead given a giant bag of data, what problem do you solve now and how do you do it?

The implication, of course, is that ultimately the two bags equate to the same thing. They’re both resources. And as technology has revolutionized our ability to capture and analyze huge troughs of data, big data has in turn become an increasingly powerful resource and disrupted industry after industry.

And much of that disruption has come at a price.

Facebook, Equifax, Yahoo! — these are just a few of the massive data breaches that have happened over the last handful of years. As companies have collected more and more data, they have not always taken the proper precautions to protect that data. In the terms of our original analogy, if data is money, it’s often left sitting out in the open.

As a result, we have seen a number of large new data privacy regulations come into play recently, with many more on the horizon. Like all things related to big data, these regulations have been extremely hefty, sometimes to the point of seeming overwhelming. But we would argue that they don’t have to feel this way.

In this article, we dig deeper into the rise of data privacy regulation, examining the major new regulations that have recently come into play, the way these regulations are transforming the compliance function, and how RegTech can help transform them from overwhelming obstacles into exciting opportunities.

READ CASE STUDY: How a Global Top 50 Bank Secured Its GDPR Obligations Using Ascent

 

GDPR: The Game-Changer

The modern age of data privacy regulation was ushered in by four letters: GDPR. The first significant update to Europe’s data protection rules since the 1990s, GDPR (or, the General Data Protection Regulation) serves as both the core of Europe’s digital privacy legislation and as the benchmark the rest of the world began comparing their data privacy policies against.

First introduced in 2012 and then argued over until it was adopted in 2016, GDPR finally came into effect in May of 2018. The regulation was revolutionary for its emphasis on citizens’ rights. It was designed to give EU citizens control over their personal data, as exemplified by the eight rights for individuals within the regulation. These rights include giving EU citizens easier access to data companies hold about them, laying out fines for the failure to do so, and requiring companies to receive consent from individuals before collecting their data. 

There are many more details to the 99 articles in the regulation, but it’s these individual rights that caught a lot of public attention, both for the burden they placed on companies and the pop-up banners they created on our web browsers. 

GDPR came to seem so ubiquitous because its obligations applied not only to companies headquartered in the EU, but to any company gathering the personal data of an EU citizen. In the borderless age of the internet, this more or less meant any company with a website that tracked any information about its visitors

Of course, the EU wasn’t likely to chase down every mom-and-pop shop around the world that failed to comply with GDPR regulations. But the breadth and depth of the legislation acted as a standard-bearer, telling companies and countries it was time to update data privacy regulation for the twenty-first century. It would only be a matter of time until other countries followed suit.

CCPA: GDPR Hops Across the Pond

That most notably and recently happened in the US with the California Consumer Privacy Act (CCPA). The CCPA, which was just implemented at the beginning of this year, brought similar GDPR-like obligations to the US, including consumer rights related to the disclosure of personal information and requests for personal data. 

The CCPA affects a significant number of companies. It applies to businesses that either exceed a gross revenue of $25 million, gain 50% or more of their annual revenue by selling consumer’s personal information, or that buy, sell, receive, or share personal information of 50,000 or more consumer households.

Like GDPR, the CCPA is similarly focused on consumer rights, including a section known as data subject requests, which grants users the right to access or delete the personal information a company may have about them.

And — just as GDPR acted as the data privacy blueprint for the rest of the world — the CCPA is acting as the blueprint for the rest of the US. A number of other states are quickly catching up:

  • Washington State currently has a bill with requirements and fines drawn straight from the CCPA currently working its way through the state senate and house.
  • New York, in typical coastal one-up-manship, recently introduced an even more comprehensive bill into its state senate, which disregards the CCPA’s revenue requirement for covered entities.
  • Nevada actually implemented privacy legislation a few months before California, but its definition of “sale” resulted in a law that was narrower and more lenient on financial institutions.

The Changing Role of the Compliance Officer

The above litany of legislation, without any guiding federal framework, is a significant challenge for companies, especially those transacting business across the country. This patchwork of regulation means, for simplicity’s sake, companies often have to comply with the strictest requirements of any one regulation, even if it doesn’t necessarily apply to all the states where they are doing business. That is, of course, assuming companies and Compliance Officers can keep up-to-date on the waves of new regulation constantly being released and updated.

But in another light, these new data privacy regulations actually represent an opportunity for Compliance Officers. 

These regulations could help raise the visibility of the compliance role at companies, especially those that might have dismissed data privacy as not relevant to their day-to-day. That’s because compliantly following these privacy regulations is going to require companies to make real changes in their policies and procedures and in their corporate culture — all of which are crucial aspects of the compliance role. 

As companies update and overhaul internal procedures accordingly, Compliance teams will need to play an integral role in developing business processes to ensure that personal data is being managed compliantly.

But for Compliance teams to do that, they will somehow need to keep current with the massive amount of new regulations being rolled out and find a way to quickly and concisely understand how those regulations relate to their policies and procedures. Between the hefty laws already in place and the long list of those in process, this can seem like an insurmountable task.

Technology, though, provides a path forward.

READ ARTICLE: How Your Peers in Financial Services are Tackling 3 Big Compliance Issues

 

RegTech Offers the Key to Data Privacy Regulation

RegTech (Regulatory Technology) is an emerging industry of companies leveraging machine learning, natural language processing, blockchain, AI, and other technologies to solve the challenges of regulatory compliance. These technologies offer a way to leverage the big data of regulatory compliance to help solve the problems of data privacy regulation.

In a recent case study, one global Top 50 bank tried to identify its obligations under GDPR within one of its business units. The bank had a lack of clarity around which aspects of GDPR it was required to follow, and it attempted to solve this problem via a traditional solution: hiring a consulting firm.

The consulting firm, though, proved expensive and inaccurate. The firm missed a number of obligations and the bank was forced to hire a second consulting firm to correct those initial mistakes — adding duplicative costs. It was in the midst of this frustrating process — causing costly mistakes and creating continued regulatory uncertainty — that the bank decided to try a different approach.

The bank partnered with Ascent, an AI-powered compliance automation solution. At Ascent, our proprietary RegulationAI™ technology generates the obligations that apply to our customers, helping banks and other financial firms reduce risk and gain confidence in their compliance programs.

RegulationAI™ was able to generate a complete obligations register in mere minutes and at a 99% cost savings. This technology — a true innovation in RegTech — leverages machine learning and natural language processing to ingest hundreds of regulations and then rapidly determine which obligations apply to your business — with zero manual effort from you.

Rather than the time-consuming, expensive, and inaccurate results it had received before, the bank now had all its obligations in an easy-to-read digital format, produced with significantly lower risk of human error.

READ ARTICLE: How Ascent Simplifies Regulatory Change Management with Automation

 

Secure Your Obligations with Ascent.

The complexity of data privacy regulation is likely only going to increase in the future. But you don’t have to drown in regulation. Ascent can help you leverage technology to make this fast-paced world of digital disruption work for you.

LEARN MORE: Click here to learn about Ascent Solutions

 

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Jan 27
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How a Global Top 50 Bank Secured Its GDPR Obligations Using Ascent

By Blog

Case Study

A Global Top 50 Bank sought to identify its obligations under the Genderal Data Protection Regulation (GDPR) within one of its business units.

Our Customer at a Glance

  • $20B Annual Revenue
  • 30,000 Employees
  • 1,000+ Locations Worldwide
  • 300+ Regulating Bodies to Comply With

The Problem

Our customer faced the following hurdles:

  • Needed help determining which parts of GDPR were required for the business.
  • Initially hired a consulting firm to produce its GDPR requirements, but the firm missed a number of obligations.
  • Forced to hire a second consulting firm to correct initial mistakes, creating duplicative costs.
  • Ultimately dissatisfied with the rigamarole of multiple consultancies, missed obligations, and ongoing regulatory uncertainty.

Partnering with Ascent

Frustrated with their journey so far, the Bank partnered with Ascent, an AI-powered compliance automation solution. Ascent generates the obligations that apply to the customer, helping banks and other financial firms reduce risk and gain confidence in their compliance programs.

Using Ascent, the Bank was able to comprehensively identify its GDPR obligations at a fraction of the time and effort, kickstarting its path to compliance and better positioning the Bank to protect the privacy of its customers.

How the Global Bank Accelerated GDPR Compliance with Ascent

Before Ascent:

  • Hundreds of thousands of dollars in ongoing consulting fees
  • Countless hours and headaches, only to produce an incomplete register of GDPR obligations
  • Increased regulatory risk and error

After Ascent:

  • A mere fraction of the cost (99% savings)
  • Took just minutes to produce a complete and verified register of GDPR obligations
  • Thorough and easy-to-read digital format, produced with significantly lower risk of human error

We’re here to make compliance easier.

The road to compliance can be confusing and complex. Ascent makes it simpler with an AI-driven solution that generates the obligations that are relevant to your business. Ascent allows you to:

  • Reduce the risk-prone and costly impact of human error and missed obligations
  • Review a much narrower set of obligations, fast-tracking the tedious and manual process of regulatory research and analysis
  • Save a significant amount of time and money while reducing your regulatory and reputational risk

Modern challenges require modern tools. Interested in seeing how Ascent can help you stay ahead of regulations like GDPR?

Contact Us

Jan 20
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Open Banking: What It Is, Why It Matters, and How RegTech Can Help

By Blog, Featured

If open banking lives up to its promise, it could revolutionize modern banking and simultaneously usher in waves of new regulation and compliance changes.

Digital disruption is burning through almost every sector of our modern economy, creating exciting new opportunities while also unleashing chaos on long-established ways of doing business.

Banking is one of digital disruption’s latest beneficiaries, and one specific trend has been causing a lot of buzz: open banking. It’s a topic that, if it lives up to its promise, could revolutionize modern banking and simultaneously usher in waves of new regulation.

In this brief primer we’ll break down what open banking is, why it’s making so much noise, and how technology can help solve the compliance challenges technology has created.

What is Open Banking?

Broadly, open banking is a banking practice that gives users the ability to grant third-party financial service providers access to their financial data. 

The basics of open banking have been around for a few years now — the same principles can be found in budgeting tools like Mint or YNAB. But historically, apps like these have used a process known as “screen scraping” — where users give the budgeting app their bank username and password so the app can then “scrape” their financial information from the bank’s site. What is generating all the excitement around open banking now is the possibility to extract this information by instead using an API.

APIs (or, application programming interfaces) are a way for third-party providers to plug directly into an app or web service. So rather than giving Mint your bank username and password, you would instead grant it authorization to access your bank information, which Mint would then connect to directly through the API. 

So why is an API so much more powerful than screen scraping?

Because for one, you don’t have to share your username and password with third parties, whose cybersecurity protocol might not be robust as your bank’s. Also with an API, if the username or password is changed, the connection isn’t broken. And the process is significantly more efficient for the third party, who now has direct information to the data they want, rather than having to scrape it from another source, reformat it, and then ingest it.

But open banking’s most exciting opportunities extend far beyond budgeting apps.

What open banking really allows for is a more efficient and secure way to share financial data. 

When looked at from this perspective, the possibilities start to become industry-shaking opportunities. Here are just a few examples:

  • The labor-heavy process of getting a loan, currently requiring the lendee to pass off reams of financial statements and information to a lender, who then has to ingest those materials, could become significantly easier for all parties involved. An API would allow lenders to have more efficient access to up-to-the-minute information with much less work from the lendee, and would allow lendees to only share the information relevant to the lender.
  • Aggregation tools are already making money management a much simpler, more cohesive process. Existing solutions allow investors to get a truly holistic view of their investment portfolio, even if assets are custodied at different institutions. And emerging solutions are revolutionizing how investment advisors interact with custodians, how they analyze client data, and how they present to clients.
  • The complicated payment system that exists today is also starting to be streamlined. APIs are now connecting developers with payment systems, and it could soon become possible to make payments directly out of a bank account rather than needing an acquirer to process payments via a credit card company. This would limit the number of times user data needs to be shared and reduce costs for both vendors and customers.
  • Accounting solutions for both businesses and consumers are emerging that would make the process more efficient and less costly. Businesses will be able to benefit from bookkeeping applications that can plug directly into their payments feed and consumers could see a cheaper and easier tax-preparation process.

The great promise of open banking is that it liberates your data from being held solely at one financial institution in order to make it available to companies of your choosing. Ultimately, it will take some time before the benefits of this are truly understood and realized.

Regulating the Open Banking Revolution

As these benefits start to come to light, though, they will not be without risks. For example, direct access to user data, even if theoretically more secure than current practices, is an unsettling idea. And digital disruption within any industry can be chaotic, as rules and best practices become upended, outdated, and replaced. That’s why the open banking revolution is certain to be accompanied by new regulations designed to help protect consumers. 

The European market already offers a preview of those regulations — and the challenges that come with them.

The second Payment Services Directive (PSD2) was rolled out in Europe by the Competition and Markets Authority as a way to spur more innovation and competition in the banking sector. In recognition of the opportunities presented by open banking, PSD2 required enterprise banks to make their data available in a secure, standardized form, so that third-party providers (TPPs) could plug into and leverage that data through APIs.

Banks were given until March 2019 to provide TPPs with a simulated bank environment where they could test their APIs before they became fully operational in September of that same year. And yet over 40% of the European banks missed the deadline.

This is just one example of how, even in the early days of open banking, a significant number of large banks are struggling to meet the demands of the associated regulations. As the effects of open banking are more widely felt, and as wider-reaching regulation accompanies them, the workload on banks and financial firms is sure to only increase. 

New technology, though, can help solve the same problems that new technology has created.

RegTech: Open Banking’s Best Friend

Just as advances in technology are upending the banking industry, they’re also revolutionizing the world of regulatory compliance. RegTech (Regulatory Technology) companies are leveraging machine learning, natural language processing, blockchain, AI, and more to solve the problems of regulatory compliance.

RegTech solutions will be crucial both to new open banking companies looking to quickly get off the ground and to traditional, large banks implementing new solutions to stay competitive in a changing environment. AI-powered regulatory change management solutions can help automate the burdensome tasks of regulatory research and analysis, so banks and financial firms can stay up to date on all regulatory updates — related to open banking and otherwise. And obligations management tools can automatically deliver up a complete obligations register, reducing to mere minutes a task that can take thousands of hours. 

Ultimately, the wide variety of RegTech solutions currently available will allow banks and financial firms to stay ahead of the waves of regulation by quickly and efficiently building a RegTech stack specific to their needs.

READ ARTICLE: How Ascent Simplifies Regulatory Change Management with Automation

 

The digital disruption revolutionizing the financial services industry isn’t going to subside anytime soon. And banks and financial services firms can leverage RegTech solutions to help make this fast-paced change work for them.

LEARN MORE: Click here to learn about Ascent Solutions

 

Ready to see a live demo? Get in touch today!