Part 1: Introduction to Unstructured Data
This is PART 1 of a four-part series.
THE VALUE OF UNSTRUCTURED DATA
Data is the new gold critical to receiving and processing an insurance claim. As a result, InsureTech companies are constantly figuring out how data can be gathered faster, transformed, analyzed, and presented into meaningful insights. Built on charging pre-determined premiums against uncertain future occurrences, insurance trajectories and claims patterns are unpredictable and volatile with uncertain and high risk. Yet the industry is booming, with the global insurance market touching $6.3 trillion in 2019 and growing exponentially yearly. The larger the scale of the sector, the more volatility, which is reflected in the number of cases going into litigation, payouts, and reconciliation. Understanding how to plan and pre-empt the trajectory of its claims processes goes a long way in predicting the insurance industry’s growth. This is where the role of unstructured data is of critical leverage.
THE ROLE OF DATA
Big data is one of the core critical areas of the InsureTech industry. Hitherto unexplored and vaguely defined, data refers to all the details about different insurance claims and underwriting processes, notes, voice recordings, PDFs, and more that build up continuously during the lifetime of a legal process. Data has had an almost fringe benefit in the insurance sector thus far, more to maintain a record of procedures and actions, carefully filed away for future use if any. However, data has exceptional value – a fact only now revealed with new-age technology platforms like Charlee™ – that can disrupt the traditional insurance business model.
Innovations in any industry have thus far been introduced by way of technologically amplified processes and innovations in methodology. Exploring the previous ‘useless’ data has helped advance the business phenomenally in the insurance sector, giving us a tremendous advantage. The trick lies in analyzing this data using new technologies to make sense of it, obtain practical insights into customer needs to make better, targeted products for them, and help the business make purposeful decisions. While generic Machine Learning platforms have been doing their bit in such analysis, it is for the first time that unstructured data is used as a source of information, from which predictive analytical algorithms can glean relevant insights. This is throwing up even more profound insights that predict how a claim will proceed, where it will end, and what can be done to reduce spending and save time for insurers.
AI and data analytics reduce the manual hours spent on repetitive tasks, which pool plenty of people to do predictable work. Instead, it is now possible to build upon data to gain practical, far-reaching insights that offer more targeted products and make claims processes more efficient and less error-prone.
UNSTRUCTURED DATA INSIGHTS
Unstructured data is the unexplored frontier in Natural Language Processing (NLP) based predictive analytics. It comprises claim notes, third-party data (e.g., ISO and NICB reports ), news articles, and audio and video files. This is new and unknown information that often remains untouched, which otherwise can change the course of legal processes. Such data can also reveal unexplored business areas, unnecessarily consuming money, prolonging conflict resolution, and causing businesses to suffer loss and mayhem.
As easy as such data is to access, why hasn’t much been done about it?
Unstructured data is a treasure trove of information within our fingertips to use in whatever way we feel possible. Yet, there haven’t been many technological advancements to understand it until now. Most new technologies were mining data but using it ineffectively. Thus, although traditional machine learning had learned to mine it, extracting insights was insufficient to make any reliable and accurate predictive analysis.
WHAT HAS CHANGED UNSTRUCTURED DATA ANALYSIS?
Predictive analytics-based machine learning insights are revolutionizing the way such unstructured data is read, and insights derived. By understanding patterns of claims behavior using prediction and claims management models on multiple customers and millions of insurance claims, we are now able to deliver clear, critical insights of claims trajectories, attorney interventions needed, accurately predict patterns of behavior, and convert them into actionable intelligence to make high-level decision calls.
Unutilized data thus becomes the most significant missing piece of the puzzle that allows a platform such as Charlee.ai to derive key operational claims insights and predict litigation, severity, and the claims trajectory.
Written by: Charmaine Kenita and John Standish
