An ever-growing list of regulations is burdening companies with the increasingly complex challenge of measuring, analysing and mitigating risk. The task can be heavily time and resource-intensive.
Thankfully over recent years, there have been tremendous advancements in Artificial Intelligence (AI) including natural language processing (NLP) technologies which make it far easier to organise vast amounts of data and analyse findings for more accurate and in-depth risk assessment and compliance monitoring.
In a report published by IBM, a survey conducted on risk and compliance professionals found that 63% of respondents used NLP to some extent at their organisation, with 11% using it as a “core component” to support their compliance activities.
In light of this, we’ll discuss exactly what NLP is and how it is used for business analysis and risk assessment.
What Is Natural Language Processing (NLP)?
You might consider NLP as the bridge that connects human speech to computer processing. Human language is complex. There are many ways we can express an emotion. To add to the confusion, we can be subtle, sarcastic, or poetic. To decode meaning, we may need to read between the lines.
As humans, we process this information relatively easily and without too much effort. For a computer, the task is extremely difficult. For computers, our way of communicating is highly inefficient and often illogical. A computer needs to be trained to make sense of language. That’s where NLP comes in.
At Symanto, we combine deep learning with NLP to create technologies that are highly capable of making sense of written language, and turning it into structured data.
Analysing Risk With NLP
NLP is used to parse relevant information from dense, written content. This saves hours of valuable time by eliminating the need for someone to read through everything manually.
For example, you can use NLP to analyse hundreds of pages of legal documents and extract information pertaining to your compliance process. From there you can use the information to write up regulatory rules and catch outstanding compliance issues.
You can use NLP with any unstructured text data: legal documents, industry reports, company policy documents, news reports, social media comments, reviews, customer reviews, emails, CRM data. All of this data has remained largely untapped because of the enormous challenge that came with collecting and processing it. Now, thanks to NLP technologies such as those created here at Symanto, it’s a process that can take only a matter of minutes.
Examples of NLP in Business Analysis and Risk Assessment
M&A Due diligence
Before a merger or acquisition, it’s important to get the full picture of the target company. Their financial reports might only tell you part of the story.
Use NLP to get a more detailed view of the company, their products and how they operate. Search customer reviews and social media to view customer sentiment, discover which topics have a negative sentiment and whether these issues can realistically be resolved.
Find out where the target company sits within the market. Run queries on their main competitors and compare brand mentions. Is the target company gaining or losing traction within the market? Does it have an easily marketable unique selling point? Use this information to forecast the performance of the target company and decide whether it is suitable for mergers and acquisitions.
Environmental, social and governance reporting is increasingly important among investors. Currently, there is no standardised method for ESG analysis which makes risk assessment a particular challenge. Use NLP to surface relevant ESG information from company documents and reports to find outstanding compliance issues.
What are the key drivers affecting your company’s reputation and trust among key stakeholders? Some of this information can be derived from customer reviews and social media data. View spikes in brand mentions over time to pinpoint moments when your brand has faced scrutiny in the public eye. What has subsequently been done to recover from PR crises, and is there room for improvement?
Does your company’s performance match up with its reputation? You might assume that it’s preferable to have a reputation that exceeds performance, but this comes with its own risk. What happens when your company repeatedly falls short of expectations?
According to this Harvard Business Review article, “a positive reputation requires that at least 20% of the stories in the leading media be positive, no more than 10% negative, and the rest neutral.” Use NLP to measure sentiment, and decipher your brand’s reputation.