How InMoment Assists with Regulatory Compliance

The challenges involved in regulatory compliance vary greatly between industries, countries, and companies. But many compliance tools lack flexibility or are missing key technologies for parsing complex structures in legal, medical and financial documents. That’s where InMoment comes in.

Leverage InMoment to Lower Your Regulatory Compliance Costs and Risks

InMoment helps you tackle compliance challenges involving text data through “semi-custom” solutions. We combine semi-structured data parsing, natural language processing (NLP), and machine learning with other features and technology suited to your specific problems. By working from our existing infrastructure through a staged Proof of Concept, we reduce your initial investment and deliver tangible results more quickly. 

We don’t “solve” or automate your entire industry. Instead, we help you improve existing compliance processes and scale your compliance teams more easily, resulting in lower costs and reduced risk across your organization.

Your Regulatory Compliance Technology Toolkit

Curious about the tech InMoment provides that will help you get the job done? Here’s an overview of your toolkit:

Natural Language Processing Features

  • Sentiment Analysis: Combine natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes and categories within a sentence or phrase.
  • Theme Analysis: Use natural language processing (NLP) to break down sentences into n-grams and noun phrases and then evaluate the themes and facets within.
  • Entity Recognition:  Identify people, places, and things within a piece of text.
  • Categorization: Categorize customer reviews, support tickets, or any other type of text document into groups based on their contents.
  • Intention Extraction: Determine the expressed intent of customers and reviewers.
  • Summarization: Extract the most relevant sentences from each document so you can quickly understand the main ideas without spending valuable time reading the whole document.

Semi-Structured Data Parsing: A powerful tool for identifying and extracting text data from PDFs, .docx files and other “semi-structured” documents while understanding the structures and relationships of each element.

Machine Learning: Custom machine learning “micromodels” to tackle unique challenges in your data, such as entity recognition on ambiguous company names or classifying news articles into pre-defined topic lists.

Add-ons and Integrations: 

  • ​​Low-level NLP configuration
  • Custom user interfaces
  • Specific technology integrations
  • Feedback loops for model training
  • User and project management tools
  • Database/warehouse hookups
  • Upload wizards and connectors
  • …. And more

InMoment for Regulatory Compliance in Action: A Quick Case Study

InMoment has helped brands across healthcare, biotechnology, pharmaceuticals, financial services, and more, but today we will share a financial services case study.

An Australian financial services firm needed help ensuring their compliance with federal disclosure mandates across hundreds of Statement of Advice (SoA) documents. Before, the firm’s auditors manually reviewed a subset, but this process was slow and unreliable.

InMoment focused on improving the firm’s existing audit process. First, we trained our semi-structured data parser to understand the underlying structure of SoAs. Then we configured our NLP to identify, extract and analyze entities within each section. Finally, we built a connector to structure and export this data into an easy-to-scan spreadsheet.

“InMoment’s solution for financial services disclosure compliance identifies, analyzes and structures key data from Statement of Advice documents for internal review.”

This solution substantially reduces the firm’s noncompliance risk by empowering regulatory compliance auditors to review hundreds of documents in minutes. Now they can quickly and reliably spot missing disclosures, suspicious recommendations, and other areas where advisors may not be working in their clients’ best interests.

To learn more about how InMoment can help revolutionize your approach to regulatory compliance, check out our dedicated website here.

Three Considerations When Leveraging Artificial Intelligence (AI) for Regulatory Compliance

Companies are investing heavily in artificial intelligence (AI) to save money and time—especially those in industries who have to constantly deal with regulatory compliance documents. After all, who wants to sift through endless amounts of tables and lists? Those working in legal, medical, or financial sectors are often all too familiar with this infamous struggle. And considering this, it makes sense that PwC predicts AI could contribute $15.7 trillion to the global economy in the near future. Busy work, laborious practices, and the humdrum of paperwork are not the most ideal job duty for any employee. This poses an important question: is AI powerful enough so that no employee will ever have to touch a regulatory compliance document again?

To help you answer that question, here are the top three things you need to consider:

Consideration #1: What Is Artificial Intelligence?

Fundamentally, we should think of artificial intelligence as a tool rather than a replacement for human expertise. AI doesn’t accomplish anything without a proper wielder to fully comprehend how to use it. Therefore, it’s key to rethink your AI strategy. Ask yourself, how are you approaching AI? 

Let’s first take a look at the problem at hand. Regulatory compliance documents require an extreme attention to detail due to their naturally complex text structure. That’s why traditional text analytics don’t necessarily do the trick. Essentially, what you want AI to do is to read, check, and extract data from a document that’s written and filled out by humans. 

The thing is these documents aren’t standardized, resulting in arbitrary changes in format and other elements. This puts AI in a tough situation. As a technology that functions through learning from examples, how can it learn if the examples change unpredictably?

Consideration #2: AI Cannot Succeed Alone

That doesn’t mean you should totally scrap AI, it just needs a little help. In the case of regulatory compliance, AI cannot succeed alone, but it can be a core part of your success. To tackle regulatory compliance documents, you need a combination of three technologies: 

  • Semi-Structured Data Parsing
  • Natural Language Processing
  • Machine Learning and AI

Each of these technologies supply needed functions, such as extracting text, understanding the meaning of a text, pattern recognition and response, etc. But with all these helpful aids, the human eye still remains the most reliable. Technology may not be able to totally replace humans in this context, but it can certainly provide a solution that mitigates the heavy burden of regulatory compliance.

Consideration #3: Designing an Effective and Personal AI Strategy

It’s likely that your specific industry and country encounters problems other companies outside of your field or location don’t. In that way, making sure that your AI implementation covers all bases in the documents you process can feel like a solo battle. And that’s why you need to invest in a platform that will allow for the customization your brand needs. Regulatory compliance documents vary depending on the business setting and thus have unique requirements for AI to fulfill.

Wrapping It Up

So the short answer is no, in this case AI cannot fully replace humans in regulatory compliance. But it can certainly aid businesses in working more efficiently and effectively. Rather than approaching this as AI or humans, one or the other, it should instead be AI for humans. Many AI for compliance tools fail to provide useful solutions because they don’t understand this complex relationship.

If you’re interested in learning more about how to sharpen your approach to AI for regulatory compliance, read the full white paper where we also include specific case examples!

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