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48 results for claim description

It is proved that the set of geodesic circles in two dimensions may be given a variational description and the explicit form of it is presented. In the limit case of the Euclidean geometry a certain claim of uniqueness of such description is proved. A formal notion of 'spin' force is discovered as a by-product of the v…

2014-07-23abs ↗pdf ↗

We give an explicit formula for the probability distribution based on a relativistic extension of Brownian motion. The distribution 1) is properly normalized and 2) obeys the tower law (semigroup property), so we can construct martingales and self-financing hedging strategies and price claims (options). This model is a…

2016-10-08abs ↗pdf ↗

FCDD improves image anomaly detection without post-hoc explainers.

problem Image anomaly detection, especially pixel-wise.
method Fully Convolutional Data Description (FCDD) directly addresses anomaly detection without post-hoc methods.
result FCDD achieves state-of-the-art results on pixel-wise AD tasks.

Grid diagrams encode useful geometric information about knots in S^3. In particular, they can be used to combinatorially define the knot Floer homology of a knot K in S^3, and they have a straightforward connection to Legendrian representatives of K in (S^3, ξ_\st), where ξ_\st is the standard, tight contact structure.…

2008-04-18abs ↗pdf ↗

We use the supergeometric formalism, more precisely, the so-called "big bracket" (for which brackets and anchors are encoded by functions on some graded symplectic manifold) to address the theory of Jacobi algebroids and bialgebroids (following mainly Iglesias-Marrero and Grabowski-Marmo as a guideline). This formalism…

2010-12-13abs ↗pdf ↗

Reinforcement learning improves insurance claims reserving by learning from all claim trajectories.

problem Traditional reserving models learn only from settled claims, missing valuable data from ongoing claims.
method Formulated as a Markov decision process, uses reinforcement learning to update OCL estimates sequentially.
result Soft Actor-Critic implementation achieves competitive claim-level accuracy and strong aggregate performance.

The study analyzes how bonus-malus systems and delayed claims settlement affect insurance companies' financial stability.

problem Analyzing the impact of bonus-malus systems and delayed claims settlement on insurance companies' financial stability.
method Examined a discrete-time risk model with time-varying premiums, evaluating two types of claims and settlement delays.
result Delayed settlement of by-claims leads to lower ruin probabilities under specific assumptions.

Deep Claim predicts payer responses from claims data using deep learning.

problem Predicting payer responses from claims data to improve healthcare performance.
method Learning complex dependencies in claim inputs to create a compact representation, then using deep learning to predict responses.
result Deep Claim improves claim denial prediction by 22.21%.

The tail of the distribution of a sum of a random number of independent and identically distributed nonnegative random variables depends on the tails of the number of terms and of the terms themselves. This situation is of interest in the collective risk model, where the total claim size in a portfolio is the sum of a …

2007-03-01abs ↗pdf ↗

Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.

problem Improper health insurance payments from fraud and upcoding.
method Two machine learning models: an upcoding model based on severity code distributions and a random forest model for claim sorting.
result Random forest model saved 12% to 40% in improper payments compared to a baseline approach.

This study compares the largest claims from two insurance portfolios using stochastic orderings.

problem Comparing the largest claims from two heterogeneous insurance portfolios.
method Used various stochastic orderings and established sufficient conditions associated with model parameters.
result Established sufficient conditions for comparing the largest claims from two insurance portfolios.

Model detects insurance fraud using social network analysis.

problem Fraudulent insurance claims by exaggeration or intentional damage.
method Network construction linking claims and parties, BiRank algorithm for fraud score computation, feature extraction from network and claims, supervised model building.
result Network features improve fraud detection performance.

Model predicts individual insurance claim reserves using activation patterns.

problem Accurately predicting individual claim reserves in insurance contracts.
method Multinomial logistic regression to model claim activation and development.
result The model generates accurate predictions of total and per coverage reserves.

Traditional non-life reserving models largely neglect the vast amount of information collected over the lifetime of a claim. This information includes covariates describing the policy, claim cause as well as the detailed history collected during a claim's development over time. We present the hierarchical reserving mod…

2019-10-28abs ↗pdf ↗

A new method for modeling insurance claim frequencies using random proportions.

problem Inaccurate fitting of classical distributions to insurance claim frequency data.
method Modeling claim frequencies using random proportions of insurance contracts and applying goodness-of-fit tests.
result A new statistical approach for better modeling insurance claim frequencies.

The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.

problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.

In this work, we focus on fine-tuning an OpenAI GPT-2 pre-trained model for generating patent claims. GPT-2 has demonstrated impressive efficacy of pre-trained language models on various tasks, particularly coherent text generation. Patent claim language itself has rarely been explored in the past and poses a unique ch…

2019-07-01abs ↗pdf ↗

In this paper we study a risk-minimizing hedging problem for a semimartingale incomplete financial market where d+1 assets are traded continuously and whose price is expressed in units of the numéraire portfolio. According to the so-called benchmark approach, we investigate the (benchmarked) risk-minimizing strategy in…

2013-07-23abs ↗pdf ↗

Study tackles imbalanced data in car insurance claims prediction.

problem Predicting rare events (claims) in car insurance with imbalanced data.
method Various machine learning techniques (logistic-regression, decision tree, random forest, xgBoost, feed-forward network) applied to imbalanced dataset.
result Comparison of machine learning algorithms' performance in claim occurrence prediction.

Time-aware fact-checking improves veracity predictions for time-sensitive claims.

problem Fact-checking decisions should consider temporal information of claims and evidence.
method Investigated four temporal ranking methods to optimize evidence ranking for fact-checking models.
result Time-aware evidence ranking surpasses relevance assumptions and improves veracity predictions for time-sensitive claims.

We investigate, focusing on the ruin probability, an adaptation of the Cramer-Lundberg model for the surplus process of an insurance company, in which, conditionally on their intensities, the two mixed Poisson processes governing the arrival times of the premiums and of the claims respectively, are independent. Such a …

2016-02-15abs ↗pdf ↗

Audit shows risk claims from distributional reinforcement learning agents are often false.

problem Evaluating the risk claims made by distributional reinforcement learning agents.
method Combines a decision-relevant screening metric, ground truth from Monte Carlo, and statistical methods to audit risk claims.
result 40-95% of the strongest risk claims are refuted, indicating the learned risk reflects a training artifact rather than environment stochasticity.

Study uses SVM to predict weather-induced home insurance claims and losses.

problem Assessing future weather-induced home insurance claims and losses for disaster preparedness.
method Support Vector Machine (SVM) regression for forecasting future claim dynamics.
result Illustrates SVM approach in forecasting weather-induced home insurance claims in a Canadian city.

The paper deals with bonus-malus systems with different claim types and varying deductibles. The premium relativities are softened for the policyholders who are in the malus zone and these policyholders are subject to per claim deductibles depending on their levels in the bonus-malus scale and the types of the reported…

2017-07-04abs ↗pdf ↗