Unlocking the value of unstructured data through CHARLEE’s natural language processing
Not too long ago, data became the new gold with improved technologies like Machine Learning (ML) and Natural Language Processing (NLP). The sudden interest and growth in the Internet of Things (IoT) quickly accelerated the value of data. A treasure trove of hitherto unexplored and unknown nuggets of information changed the course of companies. Legacy data paved the way for a sharper marketing focus targeting digital advertising across industries. In addition, it revealed the unexplored areas of businesses unnecessarily consuming money, prolonging conflict resolutions, and overall hurting the business through errors and unidentified gaps in the business process.
Machine learning and NLP-based predictive analytics changed how data can be analyzed and presented. By complementing the human processes with intelligent suggestions and automation, predictive analytics pre-empt and assist with labor-intensive human tasks. As a result, human functions can be refocused and re-imagined for sharper strategic impact.
Unstructured Text is the New Gold in Predictive Analytics
In the InsureTech industry, reading and understanding data innovation has brought about a significant transformation. Generally, data is presented in structured (predictable) and unstructured (diverse formats) forms. Machine learning and NLP have changed how to analyze unstructured text in claim files. As a result, although you were only using 10-20% of the data to power your business, today, you can use 90% to predict, analyze and produce actionable insights and jumpstart the value curve.
Unstructured data in the InsureTech domain is the data trapped in millions of claim notes, reports, correspondence, and third-party documents (PDF, news articles, forms, audio, and video files). Unfortunately, this data you are sitting on is lying unutilized because of the inability of many new technologies to mine and process it effectively. So, unfortunately, insights from the unstructured text were undiscoverable – – until now!
Traditional machine learning is capable of mining data through predictive algorithms to infer a few details. However, the insights from unstructured data can predict important information that can mitigate a claim’s litigation trajectory or read through risk patterns for faster and more efficient settlements.
Charlee.ai Insights for Claims
Charlee.ai’s NLP and ML-based litigation prediction and claims management models are pretrained on multiple customers and millions of insurance claims. As a result, the Charlee.ai solutions can be quickly implemented and deliver a good return on investment (ROI) within three months. In addition, whatever the scope of the deployment, the platform can be deployed and implemented quickly and agilely, leveraging the pretrained models.
Semantic Based Analysis
What sets apart Charlee™ is our patented technology with semantic analysis that can read through the meaning of unstructured data and arrive at a quick, in-depth analysis of claims trends and patterns.
- Charlee’s semantic tagging extracts tags (similar to ‘keyword terms’ in SEO) from unstructured data, collating them to connect events and significantly increases the accuracy of patterns and predictions. For example, our platform has more than 75% accuracy in litigation prediction
- These pretrained models and insights are continuously collated and used as a base to derive predictions. The pretrained insights are continually updated as new claims are received and processed
- Charlee™ converts raw data from different sources into actionable insights. Identifying and removing domain noise and transforming such tags into actionable intelligence can only happen on our incisive platform.
Charlee.ai Designed by Insurance Experts
Charlee’s patented semantic-based NLP is designed by insurance experts and leverages their decades of experience in insurance claims handling and government regulations. Their expertise significantly improves precision and recall. For example, Charlee’s litigation prediction achieves over 80% precision with over a 70% recall.
