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48 results for largest claims

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.

Let Xλ1,,Xλn X_{λ_1},\ldots,X_{λ_n} be dependent non-negative random variables and Yi=IpiXλiY_i=I_{p_i} X_{λ_i}, i=1,,ni=1,\ldots,n, where Ip1,,IpnI_{p_1},\ldots,I_{p_n} are independent Bernoulli random variables independent of XλiX_{λ_i}'s, with E[Ipi]=pi{\rm E}[I_{p_i}]=p_i, i=1,,ni=1,\ldots,n. In actuarial sciences, YiY_i corresponds to the claim amo…

2018-12-14abs ↗pdf ↗

Sornette et al. claimed that the optimal supply does not agree with the average demand, by analyzing a bakery model where a daily demand fluctuates with a uniform distribution. In this note, we extend the model to general probability distributions, and obtain the formula of the optimal supply for Gaussian distribution,…

2005-03-29abs ↗pdf ↗

Revisits VIC method to correct intrinsic reward bias in stochastic environments.

problem Intrinsic reward bias in VIC leading to suboptimal solutions.
method Proposes two methods based on transitional probability model and Gaussian mixture model to correct bias.
result Achieves maximal empowerment through corrected intrinsic reward.

We respond to the issues discussed by Farmer and Lillo (FL) related to our proposed approach to understanding the origin of power-law distributions in stock price fluctuations. First, we extend our previous analysis to 1000 US stocks and perform a new estimation of market impact that accounts for splitting of large ord…

2004-03-02abs ↗pdf ↗

Cryptocurrency markets exhibit violent, synchronised drawdowns, challenging diversification claims.

problem Cryptocurrency markets' violent drawdowns challenge diversification claims.
method Dynamic conditional tail dependence analysis
result Near-complete and stable lower-tail graph, upper tail that thins over time, dissolution of token categories into a core.

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.

The set of equivalence classes of cobounded actions of a group on different hyperbolic metric spaces carries a natural partial order. The resulting poset thus gives rise to a notion of the "best" hyperbolic action of a group as the largest element of this poset, if such an element exists. We call such an action a large…

2019-10-30abs ↗pdf ↗

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 ↗

We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 stocks listed on the Shanghai and Shenzhen stock exchanges. Both raw correlation matrix and partial correlation matrix with respect to the ma…

2016-01-30abs ↗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 ↗

Study non-standard bi-orders on punctured torus bundles, matching standard ones in key subgroups.

problem Investigate non-standard bi-orders on punctured torus bundles.
method Analyze various bi-orderings and compare them to standard ones formed by the lower central series.
result For every bi-ordering, the largest and second largest proper convex subgroups match those of a standard bi-ordering. Third largest subgroup matches if it exists.

Deployment-complete benchmarking assesses if evidence leads to consistent deployment actions.

problem Lack of clear evidence leading to consistent deployment actions.
method Introduces deployment-complete benchmarking to test if benchmark evidence determines deployment actions.
result Benchmark evidence must be complete for a claim to lead to a consistent deployment action.

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 ↗

Study on Kähler manifolds shows rigidity of eigenvalues with positive Ricci bound.

problem Optimal rigidity results for eigenvalues on Kähler manifolds with positive Ricci lower bound.
method Established optimal rigidity results for eigenvalues on Kähler manifolds with positive Ricci lower bound.
result Complex projective space is the only Kähler manifold with the largest multiplicity of the first eigenvalue.