Every day in the world of regulatory compliance, humans create knowledge out of data. 

Risk and Compliance teams scan the regulatory horizon, gathering information relevant to their industry. Compliance analysts then assess those regulatory documents to extract the laws, rules and regulations within them, and then analyze those requirements to determine which might apply to their business. This manual process takes the raw data of regulatory rulebooks and other documents and, through hundreds of hours of hard work, turns it into knowledge.

Only then, finally armed with this knowledge, are teams able to begin the real, vital work of compliance — reconciling their obligations with their policies and procedures, creating controls, and implementing compliance throughout the business. This knowledge creation process is a necessary but tedious and burdensome part of regulatory change management.

And, in our current regulatory climate, it is increasingly impractical.

The Global Financial Crisis of 2007-2008 remade the financial services landscape and, in the process, ushered in a new era of regulatory oversight — one that moves at a superhuman pace. Rule changes have increased 500 percent in the last decade. In fact, a new regulatory update gets implemented every 7 minutes.

Historically, increasing personnel was the primary lever teams pulled in order to keep up with this breakneck regulatory pace. But the current global health crisis and its bear market budget constraints has made that option no longer feasible.

Instead, teams have to turn to the only remaining path forward: emerging technologies.

Regulatory knowledge automation is the process of using machines to complete the knowledge creation process. By leveraging next-generation technologies like machine learning and natural language processing, the knowledge creation work can be completed in mere minutes, at a fraction of the cost, and free from human-error. 

And most importantly, regulatory knowledge automation can liberate Risk and Compliance teams from the burdensome aspects of regulatory change management so that they can instead focus on the more critically human responsibilities of their roles. 

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

Automating Regulatory Change Management

Regulatory change management is composed of three stages, all of which can be simplified by emerging technology.

Stage 1: Monitor

In order to keep their finger on the pulse of the regulatory environment, track emerging trends, and identify new obligations and regulatory updates, Risk and Compliance teams have had to closely monitor the vast and fast-paced regulatory landscape.

Historically, this has meant that Compliance analysts had to become experts at finding any industry-related regulatory information and teasing out its applicability to their business. This process — often referred to as horizon scanning or regulatory monitoring — can be an extremely burdensome one, especially considering the high cost for a mistake. Like the proverbial needle in the haystack, any obligation missed among the thousands of lines of regulatory information could have severe consequences come audit time.

But, at root, this process is one of aggregation — something artificial intelligence has long since proved its expertise at. Regulatory intelligence tools now exist that can deliver up a comprehensive view of the regulatory landscape in an instant — taking human-error out of the equation and freeing up an analyst’s time.

Stage 2: Identify

Once the information has been aggregated in the horizon scanning stage, teams then have to sift through the vast troves of documentation to extract the laws, rules, and regulations within them — a complicated undertaking given the dense language and complex structure of these documents, and one that has to be repeated every time a new update is released.

This process had, historically, been the line in the sand for how automation could help with regulatory change management. A machine could aggregate the documents for review, but a human was always needed to extract the knowledge out of all that regulatory data. 

By leveraging natural language processing (NLP) and machine learning (ML) technologies, though, we at Ascent have been able to change this. It is now possible to fully automate the knowledge creation process of regulatory change management.

To understand how, we need to understand how the technologies involved function.

NLP is the combination of computer science and linguistics that allows computers to understand human language. In essence, NLP takes the dense texts of regulatory documents and “translates” them into machine-readable language. ML is the capability to “train” systems how to complete a task. Once NLP has translated regulatory text into something that can be read by a machine, trained ML systems can extract the rules and requirements from that dense text.

These technologies make it possible to automate the “identify” stage of regulatory change management, reducing a 100+ hour workload into mere minutes. 

They also revolutionize the third and final stage of regulatory knowledge creation: Analysis.

Stage 3: Analyze

Once the regulatory documents have been gathered and the laws, rules, and regulations extracted, teams must then analyze this inventory of obligations to determine which apply to their business.

This is one of the most complex parts of regulatory change management and — just as with the “identify” stage — it had previously been impossible to automate. But the same technology we used to revolutionize the extraction of laws, rules, and regulations from documents can be used to analyze those obligations.

Ascent’s RegulationAI™ is a true innovation in regulatory technologies. It uses neural networks — the same technology that powers image recognition software and self-driving cars — to automatically assess which rules apply to your business. RegulationAI™ has been trained not only to identify laws, rules, and regulations from within regulatory documents, but to also take those obligations, assess how they correspond to various businesses based on their business practices and regulatory burden, and then determine which obligations apply. 

RegulationAI™ is able to deliver up a complete list of obligations that are specific to your business in mere minutes, at a fraction of the cost, and free from human-error.

This liberates Compliance and Risk teams to perform other, more critical work, but — because the technology digitizes the regulatory documents in the process — it also makes it possible to improve processes that come after knowledge creation. 

With regulatory documents now in a machine-readable form, it is possible to turn those documents into searchable databases, to track changes and rule updates, and even to ingest the data from those documents into a workflow management tool or GRC so that teams can interact with them — marking them for review, sharing them with other team members, and more.

READ ARTICLE: How Ascent Simplifies Regulatory Change Management with Automation

Start unlocking the power of automation today.

Ascent’s regulatory knowledge platform offers AI-powered solutions for each stage of the regulatory knowledge creation process.

Regulatory Monitoring: Our regulatory monitoring solution allows you to view, search, and organize regulatory content from around the world, all in one place.

Dynamic Rules: With Dynamic Rules, you can examine rules relevant to you and instantly and easily identify changes.

Obligations: Our obligations management tool automatically generates your obligations for you, helping you build a sustainable, bulletproof compliance program that scales.

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Modern challenges require modern tools. Interested in seeing how Ascent can help you identify your obligations and automatically keep them updated as rules change?

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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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