The adoption of multilingual text analytics is gaining in popularity, but many organizations still do not understand what the technology is all about or how it can increase the insights gleaned from data collection.
Multilingual text analytics makes it possible to rapidly identify information that is out there but otherwise missed because it is hiding in plain sight, possibly in another language. Multilingual text analytics supports speedy data collection from around the globe – agnostic of data format and language – and provides analytical context to enhance situational awareness and drive decision-making.
To help explain the potential of multilingual text analytics, here are nine examples on how an organization might use it.
1. Risk management
Text analytics helps improve risk management by identifying data points that otherwise might go unfound. Organizations can better identify relevant negative comments from across the world made by displeased customers, or similarly garnering positive feedback that helps organizations know they are on the right track.
For example, text analytics software can be used to monitor multiple languages to search for complaint keywords used when people are talking with customer service reps. This can help make companies prioritize bugs in a new version of the product or problems with their service so they can resolve the issue quickly.
2. Copyright or trademark infringement
Organizations suffer heavy losses every year from illicit sales of replica products – and much of this takes place outside the United States in dozens of languages. Multilingual text analytics can automatically recognize the terms replica and knock-off in numerous languages and representations, and when applying these queries across millions of Internet data sources, aiding organizations in identifying when their product lines are being misused.
3. Cyber Fraud
As cybercriminals become more efficient with hacking, different types of cyber fraud (i.e. stealing credit card numbers, identities, etc.) have become more prominent. In addition to multilingual capabilities, advanced text analytics solutions can help search for numerical strings such as credit card sequences to identify personal information being exchanged online.
4. Identifying customer needs
Financial institutions offer a variety of card features including types of coverage, lines of credit, and billing cycle variations. However, they do not have a good way of understanding which features are actually resonating with their customers and which ones are putting them at greater risk. By analyzing text from support calls, emails, and more, text analytics capabilities can help identify and aggregate data to give financial institutions a better understanding of which features are working and which ones are not.
5. Transportation safety
The transportation industry can gain critical insight into potential threats to public transportation systems, including aircraft, trains, buses, and other forms of mass transportation. Gathering information using text analytics, organizations can more quickly respond to mentions of threats, public safety issues, accidents, excessive delays, and other emerging issues.
6. Sporting events
When the public attends sporting events, they expect to be kept safe. Text analytics-driven applications can be used for early identification of information that impacts the safety of fans, personnel, and players, as well as monitoring the brand for the organization.
7. Insurance claims processing
Insurance fraud is a common problem as criminals attempt to defraud insurance companies. Using text analytics techniques on internal holdings, insurance companies can search for identifying terminology that may indicate fraudulent activity. Some insurance companies are already taking advantage of text analytics to review structured and unstructured data to reduce the risk of fraud and increase the speed of the claims process.
As more healthcare data is collected in varying formats and stored in different silos, searching across holdings becomes very difficult. Applying text analytics solutions to healthcare data can help healthcare professionals to quickly identify relevant information and discern trends in patient care, medical studies, disease patterns, and many more use cases that improve efficiency and understanding.
9. Supply chain monitoring
When the supply chain covers the globe, across a variety of cultures and languages, it can make it difficult to find challenges early. Multilingual text analytics can help manufacturing companies gain insights into their global supply chain by monitoring information across multiple languages and different regions and send alerts to interested analysts when potential problems occur.
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