Regulatory Compliance
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A Better Approach to AI for Regulatory Compliance

Companies with regulatory compliance burdens are flocking to artificial intelligence (AI) for time-savings and cost reductions. The money involved is staggering: In 2021, Healthcare AI funding reached $8.5 billion across 366 deals at an average of $30 million each.1

The reasons for these investments? AI can deliver the global healthcare systems and services up to $906 billion of value annually,2 and AI could deliver the global banking industry up to $1 trillion additional value annually.3 And PwC predicts that AI could contribute $15.7 trillion4 to the global economy.

But these big numbers don’t eliminate the fact that every month brings new articles and think-pieces about bursting the AI hype bubble. So, before you pay too much attention to those types of articles, let us tell you how our thoughts and experiences warrant a much better approach to AI for regulatory compliance.

AI for Compliance Requires More Than AI

The truth is that “artificial intelligence” is just a tool. It doesn’t really “do” anything on its own. What matters is how you combine AI with other technologies and the experts that know your business. In a field like regulatory compliance, for example, the challenges involved can vary wildly by industry and country. So, in order to address these problems, you need a combination of three technologies and human expertise:

The complex structure of regulatory and legal documents means that each of these technologies will fall short if it’s not supported by the other two and many times with a helpful human eye. But most off-the-shelf analytics tools and vendors use only one or two of the three, leaving valuable data behind or overlooking critical context. InMoment, on the other hand, has developed all three tools into a comprehensive platform and has a team of highly trained experts. We build semi-custom applications that solve specific compliance challenges for our clients.

Why AI Often Falls Short in Regulatory Compliance

Every organization is subject to some regulation. Healthcare providers, pharmaceutical companies, and financial services firms face particularly heavy burdens. But despite massive investment and clear market opportunities, technology solutions for regulatory compliance are proving difficult to develop. Why?

In short: Because the complex structure of the documents involved means that traditional data analytics techniques aren’t able to extract and understand all of the data that compliance professionals rely on.

The Key to Building an Effective AI for Regulatory Compliance

To build an AI solution that actually helps humans with regulatory compliance tasks, you need to understand three points:

  1. Regulatory compliance is very complex, but generally means confirming something: whether a particular document is compliant or that your organization is properly tracking and adhering to shifting regulatory updates
  2. Legal, medical, and financial documents have important information contained within both structured elements (tables and lists) and unstructured text
  3. Traditional data analytics techniques fall short in regulatory compliance because identifying and extracting all of the data from a legal document requires more than AI

Using AI to Improve Existing Processes

Rather than trying to build an end-to-end, failure-prone “AI for disclosure compliance,” InMoment focuses on
improving
existing processes. Our system empowers financial auditors to review all of their documents almost simultaneously, instead of spot-checking a handful. This substantially reduces non-compliance risk for financial services firms and banks.

We combine our semi-structured data parser with text analytics to quickly analyze long financial documents and extract all of the data, wherever it’s located: legal disclosures, asset allocation tables, statements of advice, client roles, and more. Then, our natural language processing algorithms parse the underlying structure and meaning of the information. This enables us to make complex connections between data points wherever they appear in the document. Finally, we use artificial intelligence to structure this data and prepare it for further analysis.

References

  1. CB Insights, State of AI
  2. McKinsey, The Executives AI Playbook
  3. McKinsey, The Executives AI Playbook
  4. PwC, PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution

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