7 Applications of Natural Language Processing That Shape Our Future

Over the years, we’ve seen how companies expanded their ways of handling customer inquiries, be it to social media platforms or messengers. Nowadays, a brand answering their customer about stock availability on Instagram wouldn’t be surprising. The majority of customers’ requests are still handled via text messages or calls — a perfect ground for NLP to shine.

Many customer service centers have the same recurring problem — they often have too many requests to handle. This results in long waiting times and, consequentially, in dissatisfied customers. Moreover, in our fast-paced world, instant communication is becoming more common, which significantly increases customers’ expectations and makes companies rethink customer engagement strategies.

For example, Nina, an intelligent automated virtual assistant developed by Nuance, can process the majority of both text- and voice-based requests without the involvement of customer service agents. In 2018, Swedbank integrated Nina to free up the time of their 700 communication center managers. Nina now handles some of the basic banking inquiries like checking customers’ balance or ordering foreign currency. When Nina is incapable of solving a particular issue, it simply redirects the customer to a human customer agent.

Swedbank reports very uplifting results: Nina can now resolve 78% of inquiries by itself and answer more than 350 types of customer questions. The adoption of a virtual assistant now allows Swedbank’s contact center agents to dedicate their time to sales rather than to resolving often repetitive customer requests.

It’s important to note, however, that Swedbank’s success won’t be possible without its solid data governance policies and extremely large datasets. Being a company with millions of customers, the bank could reliably train its machine learning system. Even the most common questions can be formulated differently and asked in various languages, which makes the algorithm training especially critical. This implies that the same degree of success would only be possible for companies that have sufficient resources for system training.

Nowadays, compliance divisions at financial institutions and insurance companies are dealing with a plethora of constantly changing regulations. Updating regulatory change management processes is costly and time-consuming, especially when a company operates in multiple regions. Regulatory fines are notoriously big, and not abiding by particular mandates can lead to losing a business license. Given that organizations are trying to avoid these detrimental consequences while efficiently allocating their resources, turning to automation is a necessary decision in this case.

Compliance.ai is a US-based provider of regulatory change management solutions that uses AI-based software. The company provides NLP-based tools for monitoring the growing number of regulatory changes in real time, enabling financial institutions and insurance companies to stay proactive and compliant. The software automatically extracts relevant data from dozens of documents found on numerous regulatory websites, including federal regulatory agencies and news media, and then prioritizes each regulatory update for organizations to take actions.

Notably, Compliance.ai confirms that the algorithm chooses the most relevant regulatory updates based on inputs from real niche compliance experts. In theory, such an extreme level of intelligent process automation can significantly ease auditing, eliminate manual work, and boost the productivity of compliance teams.

One of HR’s most critical tasks is to find the right person before their competitors do. Given that recruiters are often bombarded with lavish amounts of resumes, the most promising candidates can be easily missed. This problem has been tackled from many angles, including conventional applicant tracking systems (ATS). While they have indisputably proved to be effective, they act just as basic word filters that match metadata to vacancy.

NLP-based software can maximize recruiting efficiency by intelligently filtering through hundreds of applicants that meet the job requirements. The intelligence factor lies in gender-neutrality, bias-resistance, and sentiment analysis, which conventional ATS systems don’t have. NLP-based tools can provide optimal candidate suggestions, while significantly reducing the time it takes to find the right one.

Moreover, this technology can also help recruiters target better candidates with their job postings. Many experienced HR professionals will confirm that appealing to the target audience in your job advert can sometimes feel like rocket science. Word choice and sentence structure can attract the wrong candidates and discourage the best ones. This is where NLP software comes into play.

For example, Textio, a Seattle-based augmented writing platform helps recruiters to measure the effectiveness of job posts by assessing how well the chosen style of writing appeals to certain candidates. Thanks to advanced NLP-based features coupled with ML algorithms, Textio can predict how fast the job post will be filled compared to other similar posts, identify which words appeal to specific age groups, and optimize for inclusivity. Textio’s value is now recognized by HR professionals worldwide as the company’s software has been used by industry giants P&G and Johnson & Johnson. For example, Cisco reports that they can now find 18% more qualified candidates and fill roles a week faster.

While big organizations tend to heavily invest in cybersecurity, it often addresses only external threats. In the 2020 Cost of Insider Threats Global Report, Ponemon Institute surveyed 204 large IT companies around the world to find out that the number of insider threats has increased by 47% in the last two years. The average cost of these types of incidents cost each organization an average of $2.79 million.

The types of insider threats range from fraudulent schemes to the theft of intellectual property to the leakage of an organization’s confidential information. Given that data volumes the number of their access points are constantly expanding, corporations need to find new ways of protecting their information.

Accenture has developed an NLP-powered insider threat detection solution that helps companies determine if their employees have any fraudulent or vile intents. The tool scans companies’ internal communication channels, performs sentiment analysis, and updates a ‘risk’ score of any employee. This method of detecting insider threats is reminiscent of PC antivirus software, which constantly performs security checks in the background. It can help companies prevent emerging problems’ escalation, thus avoiding costly legal battles as well as reputational damages.

Conversations have been driving business for centuries, and all forms of communication contain only unstructured data. The use cases above indicate that this unstructured content has a tremendous hidden potential for extracting valuable insights. With a thought-out NLP implementation plan, organizations can understand their data deeper, enhancing their efficiency and leveling up their business intelligence.