Predictive analytics in legal billing: an applied machine learning approach to forecasting bill rejections

Abstract

In the legal industry, accurate billing is not only a matter of financial importance but also critical to maintaining strong client relationships. However, bill rejections or discounts requested by clients can significantly impact the revenue streams of law firms. This thesis presents a practical application of machine learning -the XGBoost algorithm- to forecast the likelihood of bill rejections based on historical billing data. The research explores various factors that contribute to bill rejections, including project rates, billing office attributes, employee roles, and the narratives associated with the work descriptions. Through the development and deployment of a predictive tool, this thesis provides a datadriven approach to identifying high-risk bills before they are sent to clients. The findings suggest that while narratives are important, other factors such as project area and billing office play a more significant role in determining whether a bill will be accepted or rejected. This work also delves into the preprocessing techniques, feature engineering, and hyperparameter optimization processes that are crucial to the model's success. The implications of these findings are discussed in the context of improving legal billing practices and reducing financial risks for law firms.