SMARTR : A Framework for Early Detection using Survival Analysis of Longitudinal Texts (2024)

Abstract: This paper presents an innovative approach to the early detection of expensive insurance claims by leveraging survival analysis concepts within a deep learning framework exploiting textual information from claims notes. Our proposed SMARTR model addresses limitations of state-of-the-art models, such as handling data-label mismatches and non-uniform data frequency, to enhance a posteriori classification and early detection. Our results suggest that incorporating temporal dynamics and empty period representation improves model performance, highlighting the importance of considering time in insurance claim analysis. The approach appears promising for application to other insurance datasets.

## introduction most claims from the car insurance industry are straightforward to settle. the damage to the car's body generates benefits that are easy to predict. these prevalent claims are part of the loss an insurer can foresee from year to year in the portfolio of its policyholders. catastrophic claims, on the other hand, occur at unexpected moments, are of a completely different magnitude, and pose a danger to the company's financial health. these costly claims result from the bodily injuries a policyholder will suffer during a car accident. these injuries can, in the most extreme cases, cause permanent damage to the policyholder, such as disability or amputation. in addition to ranging from $100,000 to several million dollars, their handling can span over many years, during which various experts try to agree on the settlement. early detection of such claims is desirable: although the original injuries have occurred, taking care of that policyholder can prevent risk deterioration that causes more significant costs. furthermore, since these payments span several financial years, the actuaries need to adequately provision for future benefits so the money is reserved for the insured, not paid to shareholders as profits. our application attempts to detect expensive claims early in a privately held longitudinal textual corpus from a canadian insurer. this corpus contains claim files comprising textual documents monitoring a claim's settlement process over time, which we believe is helpful in detecting expensive claims early. the main contribution of this paper is the smartr model, an early classification model that uses a survival analysis model calibrated on text data. in our proposed model, adding a temporal aggregating layer and monthly padding improves the early detection time by, on average, 4 % without decreasing its classification performance. this paper is divided as follows. we present related work for survival analysis and fields interested in early detection in section 2. we then present the groundwork to include our dataset and survival analysis into a classification task in section 3. finally, we present the evaluation scheme, our models, and results analysis in section 4. ## related work survival analysis aims to relate factors causing an event to the waiting time until its occurrence. classic examples of using this analysis include evaluating the waiting time until a mechanical part fails or until a person dies. in the present paper, we model the waiting time between the occurrence of an accident and the moment it is identified as expensive. using a specialized neural encoder to generate representations used to calibrate a survival model is a familiar idea. a review of classical models was conducted by @xcite for credit scoring. these models were set aside until the mid-2010s, when neural networks benefited from significant advancements. a more recent review presents advances in machine learning survival models in @xcite . the first neural implementation of a cox proportional hazard survival model (cph) was presented by @xcite . in their work, the authors developed a network that offered automatic encoding of attribute interactions @xcite . the next iteration of the neural cph model, deepsurv @xcite , combines several architecture and methodology improvements. the better performance of this model demonstrates the ability of neural encoders to exploit complex interactions to calibrate a survival function. however, these approaches and models exploiting survival functions are not designed to handle textual data and have yet to be evaluated with longitudinal textual data in the application of a costly claim identification problem. the fraud detection field is interested in early detection @xcite , but our problem differs from theirs as we have a gold label to trust and leverage to train a classification model. medicine is also interested in early detection @xcite ; but the clinic uses cases that are seldom evaluated using timevarying covariates from a longitudinal study as our problem is. alternative approaches for early classification include adversarial training @xcite , where a loss function is calibrated to optimize the tradeoff between timeliness and performance. although attractive, we prefer an approach that provides a risk evaluation framework that actuaries can leverage in insurance operations and processes. our model is inspired by the safe model presented by zheng et al., which lacks the capacity to handle text data and is bound to inputs and labels produced at the same frequency (e.g. daily or monthly), two limiting factors to address our use case. ## methodology this section describes the dataset used and the approach to classifying observations using a survival probability. ## experiments this section presents our evaluation scheme, models, baselines, and result analysis. ## conclusion in this paper, we have proposed the smartr model and evaluated its enhancement. our results show that our approach improves overall classification performance compared to the safe model and allows a 4% faster early detection. our enhancements were tested on a longitudinal corpus comprised of claim files, where the early detection of expensive claims was the task to achieve. future work includes using an llm to aggregate texts from many notes and obtain key elements of a claim or a multi-decrement approach to model the probability that the claim settles without becoming expensive. this approach would allow the model to discern the common, less costly elements of both types of claims and those associated with claims that will be closed without becoming costly.