Revolutionizing personal injury case valuations by developing a predictive data model, employing mathematical and machine learning approaches to provide accurate, transparent estimations, enhancing legal professionals’ decision-making. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

The problem

In the legal industry, estimating the value of personal injury cases is a process heavily reliant on the subjective experience of legal professionals. This reliance introduces a significant degree of variability and uncertainty in outcomes, often leaving individuals unsure about the potential compensation for their injuries. While tools like Colossus exist to aid in this estimation process, their predictive accuracy is limited, and their use is constrained by the proprietary nature of insurance company data. This situation underscores a critical need for an open, transparent, and accurate method of predicting case values, a need that becomes even more pressing in the face of data scarcity and the diverse nature of personal injury cases.

The impact of the current challenge is multifaceted. For individuals who have suffered personal injuries, the lack of access to reliable information on case values can lead to unequal negotiations with insurance adjusters or legal teams, potentially resulting in settlements that do not fully compensate for their losses. Legal professionals, on the other hand, face the daunting task of navigating through limited and often proprietary data to provide their clients with the best possible advice, a scenario that can strain resources and limit the effectiveness of legal representation.

Addressing these challenges, the initiative to develop a predictive data model for personal injury accidents in the United States aims to revolutionize the process of accident reimbursements. By employing a comprehensive mathematical and Machine Learning approach, the model seeks to predict case values early in the process, offering a solution that moves beyond the current reliance on legal expertise alone. This approach not only aims to enhance the efficiency and effectiveness of monitoring, reporting, and intervention strategies for child protection agencies but also democratizes access to crucial compensation information for individuals, empowering them to make informed decisions regarding their legal rights and potential settlements.

The goals

The aim of this project is to revolutionize the estimation of personal injury case values in the US through the development of a predictive data model. By harnessing a comprehensive mathematical and Machine Learning approach, this initiative seeks to provide a reliable tool that augments the expertise of legal professionals, offering a more objective basis for case valuation. This endeavor is set to unfold over a series of planned phases, each marked by specific milestones aimed at achieving the project’s goals:

• Development of a Predictive Data Model: At the heart of the project is the creation of an AI-based predictive model designed to accurately estimate the value of personal injury cases. This model will be developed using a blend of mathematical approaches and Machine Learning techniques, ensuring a robust tool capable of handling the complexities of personal injury valuations.
• Comprehensive Data Collection and Preparation: The project will embark on an extensive data collection and preparation phase, aiming to gather between 5,000 to 15,000 data points from various sources. This phase is crucial for building a diverse dataset that reflects the wide range of factors influencing personal injury case values.
• Prototype System Development and Testing: The initial weeks are dedicated to developing a prototype system that demonstrates the core functionality of the predictive data model. This includes the stages of data ingestion, analysis, report drafting, and basic editing capabilities, providing a tangible proof of concept for the tool’s potential.
• Model Refinement and Expansion: Following the development and initial testing, the project will focus on refining the predictive model based on feedback and testing results. Efforts will concentrate on enhancing the model’s accuracy and reliability, with the aim of achieving a 70-75% accuracy rate with the smallest possible dataset. This phase also lays the groundwork for future expansion and refinement of the model.

By accomplishing the above objectives, the project aspires to deliver a Minimum Viable Product that showcases the capabilities of the predictive data model, setting the stage for further advancements in legal technology. This initiative promises to significantly impact the legal industry by providing a more objective, transparent, and accessible tool for estimating personal injury case values, thereby contributing to fairer and more informed accident reimbursements.

Why join? The uniqueness of Omdena AI Innovation Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will build AI solutions to make a real-world impact and go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works