The rapid growth of health insurance and the rising incidence of fraudulent claims underscore the necessity for an efficient and professional claims management system. However, there is a noticeable lack of shared relevant experience from previous research in this field. In response to this challenge, we introduce AntAkso, a robust claims management system specifically designed for health insurance operations within Alipay. AntAkso incorporates a digital and professional management system, achieving a notable decrease in the volume of false claims, reduction in administrative costs, and heightened satisfaction among its policyholders. We begin by highlighting the core components of this system, including the case stratification, hospital recommendation, and case dispatch modules, along with the pivotal algorithms employed, i.e., the fraud detection, recommendation, and robust satisficing algorithms. We also detail the system's implementation and deployment. We substantiate the proposed system's effectiveness and efficiency with empirical evidence from experiments on a large set of real-world health insurance claims data.

The strong performance of neural networks typically hinges on the availability of extensive labeled data, yet acquiring ground-truth labels is often challenging. Instead, noisy supervisions from multiple sources, e.g., by multiple well-designed rules, are more convenient to collect. In this paper, we focus on the realistic problem of learning from multiple noisy label sources, and argue that prior studies have overlooked the crucial self-cognition ability of neural networks, i.e., the inherent capability of autonomously distinguishing noise during training. We theoretically analyze this ability of neural networks when meeting multiple noisy label sources, which reveals that neural networks possess the capability to recognize both instance-wise noise within each single noisy label source and annotator-wise quality among multiple noisy label sources. Inspired by the theoretical analyses, we introduce an approach named Self-cognitive Denoising for Multiple noisy label sources (SDM), which exploits the self-cognition ability of neural networks to denoise during training. Furthermore, we build a selective distillation module following the theoretical insights to optimize computational efficiency. The experiments on various datasets demonstrate the superiority of our method.

This paper considers the problem of learning from multiple sets of inaccurate labels, which can be easily obtained from low-cost annotators, such as rule-based annotators. Previous works typically concentrate on aggregating information from all the annotators, overlooking the significance of data refinement. This paper presents a collaborative refining approach for learning from inaccurate labels. To refine the data, we introduce the annotator agreement as an instrument, which refers to whether multiple annotators agree or disagree on the labels for a given sample. For samples where some annotators disagree, a comparative strategy is proposed to filter noise. Through theoretical analysis, the connections among multiple sets of labels, the respective models trained on them, and the true labels are uncovered to identify relatively reliable labels. For samples where all annotators agree, an aggregating strategy is designed to mitigate potential noise. Guided by theoretical bounds on loss values, a sample selection criterion is introduced and modified to be more robust against potentially problematic values. Through these two methods, all the samples are refined during training, and these refined samples are used to train a lightweight model simultaneously. Extensive experiments are conducted on benchmark and real-world datasets to demonstrate the superiority of our methods.

The importance of incorporating interpretability into machine learning models has been increasingly emphasized. While previous literature has typically focused on feature level interpretability, such as analyzing which features are important and how they influence the final decision, real-world applications often require domain level interpretability, which relates to a group of features. Domain-level interpretability holds the potential for enhanced informativeness and comprehensibility. Unfortunately, there has been limited research in this direction. In this paper, we address this issue and introduce our proposed method DIDE, which obtains domain-level interpretability from domain-specific latent embeddings. To enhance the effectiveness of the framework, we draw inspiration from the gradient smooth philosophy and propose noisy injection in the embedding space, resulting in smoothed interpretability. We conduct extensive experiments to validate the effectiveness of DIDE, and demonstrate its applications in assisting daily business tasks in Alipay.

Machine learning techniques have been widely used and are mostly performed by predicting point estimation. Nevertheless, there are various scenarios that require more information beyond only point estimation. Probabilistic prediction is a typical research topic that provides probability distribution while predicting, which addressed with much attention recently. Most of the previous works make an assumption on the probabilistic distribution to a certain extent, which may lead to potential errors in the subsequent decision-making procedure. In this paper, we propose a distribution-free method for regression problems on real-value response under the probabilistic prediction framework and present an effective boosting-based method to perform the training process. Moreover, we introduce a further improved method to accomplish an unbiased mean estimation of the target distribution. Thorough experiments on multiple benchmark data are conducted to demonstrate the effectiveness of the proposed method with regard to different measures.

Though neural networks have achieved impressive prediction performance, it's still hard for people to understand what neural networks have learned from the data. The black-box property of neural networks already becomes one of the main obstacles preventing from being applied to many high-stakes applications, such as finance and medicine that have critical requirement on the model transparency and interpretability. In order to enhance the explainability of neural networks, we propose a neural rule learning method-Feature Interactive Neural Rule Learning (FINRule) to incorporate the expressivity of neural networks and the interpretability of rule-based systems. Specifically, we conduct rule learning as differential discrete combination encoded by a feedforward neural network, in which each layer acts as a logical operator of explainable decision conditions. The first hidden layer can act as sharable atomic conditions which are connected to next hidden layer for formulating decision rules. Moreover, we propose to represent both atomic condition and rules with contextual embeddings, with aim to enrich the expressivity power by capturing high-order feature interactions. We conduct comprehensive experiments on real-world datasets to validate both effectiveness and explainability of the proposed method.

Treatment effect estimation is essential in the causal inference literature, which has attracted increasing attention in recent years. Most previous methods assume that the training and test data are drawn from the same distribution, which may not hold in practice since the effect estimators may need to be deployed across domains. Meanwhile, in real-world applications, little or no targeted treatments may be conducted in the new domain. Therefore, we focus on a more realistic scenario in this paper, where treatments and outcomes can be observed in the source domain, but the target domain only contains some unlabeled data, i.e., only features are available. In this scenario, thedistribution shift exists not only in the source data due to the selection bias between the control and treated groups, but also between the source and target data. We propose a novel direct learning framework along with the distribution adaptation and reliable scoring modules. In the distribution adaptation module, we design three specialized density ratio estimators to aid the issue of complex distribution shifts. Even so, we may face the challenge of unreliable pseudo-effects in this framework. To address that, we also design the uncertainty-based reliable scoring module as a vital support, which makes the method more reliable. The experiments are conducted on synthetic data and benchmark datasets, which demonstrate the superiority of our method.

Offline optimization aims to elaborately construct a solution that optimizes a black-box function with only access to the offline dataset. A typical manner of constructing the solution is to train a surrogate model of the black-box function on the offline dataset and optimize the solution guided by the surrogate model. However, this manner often encounters a fundamental challenge that the surrogate model could erroneously estimate out-of-distribution (OOD) solutions. Therefore, the optimizer would be misled to produce inferior solutions for online applications, i.e., degradation of performance. To this end, this paper formalizes the risk of degradation for OOD solutions and proposes an accumulative risk controlled offline optimization (ARCOO) method. Specifically, ARCOO learns a surrogate model in conjunction with an energy model. The energy model characterizes the risk of degradation by learning on high-risk solutions and low-risk ones contrastively. In the optimization procedure, the behavior of the optimizer in each step is controlled by a risk suppression factor calculated via the energy model, which leads to the controllable accumulative risk. Theoretically, we justify the efficacy of energy for accumulative risk control. Extensive experiments on offline optimization tasks show that ARCOO surpasses state-of-the-art methods in both degradation-resistance and optimality of the output solution.

In this paper, we consider the problem of long tail scenario modeling with budget limitation, i.e., insufficient human resources for model training stage and limited time and computing resources for model inference stage. This problem is widely encountered in various applications, yet has received deficient attention so far. We present an automatic system named ALT to deal with this problem. Several efforts are taken to improve the algorithms used in our system, such as employing various automatic machine learning related techniques, adopting the meta learning philosophy, and proposing an essential budget-limited neural architecture search method, etc. Moreover, to build the system, many optimizations are performed from a systematic perspective, and essential modules are armed, making the system more feasible and efficient. We perform abundant experiments to validate the effectiveness of our system and demonstrate the usefulness of the critical modules in our system. Moreover, online results are provided, which fully verified the efficacy of our system.

In this paper, we present a framework to deal with the fraud detection task with extremely few labeled frauds. We involve human intelligence in the loop in a labor-saving manner and introduce several ingenious designs to the model construction process. Namely, a rule mining module is introduced, and the learned rules will be refined with expert knowledge. The refined rules will be used to relabel the unlabeled samples and get the potential frauds. We further present a model to learn with the reliable frauds, the potential frauds, and the rest normal samples. Note that the label noise problem, class imbalance problem, and confirmation bias problem are all addressed with specific strategies when building the model. Experimental results are reported to demonstrate the effectiveness of the framework.

Decision rules have been successfully used in various classification applications because of their interpretability and efficiency. In many real-world scenarios, especially in industrial applications, it is necessary to generate rule sets under certain constraints, such as confidence constraints. However, most previous rule mining methods only emphasize the accuracy of the rule set but take no consideration of these constraints. In this paper, we propose a Confidence-constraint Rule Set Learning (CRSL) framework consisting of three main components, i.e. rule miner, rule ranker, and rule subset selector. Our method not only considers the trade-off between confidence and coverage of the rule set but also considers the trade-off between interpretability and performance. Experiments on benchmark data and large-scale industrial data demonstrate that the proposed method is able to achieve better performance (6.7% and 8.8% improvements) and competitive interpretability when compared with other rule set learning methods.

Unmeasured confounding poses a significant threat to the validity of causal inference. Despite that various ad hoc methods are developed to remove confounding effects, they are subject to certain fairly strong assumptions. In this work, we consider the estimation of conditional causal effects in the presence of unmeasured confounding using observational data and historical controls. Under an interpretable transportability condition, we prove the partial identifiability of conditional average treatment effect on the treated group (CATT). For tree-based models, a new notion, \emph{confounding entropy}, is proposed to measure the discrepancy introduced by unobserved confounders between the conditional outcome distribution of the treated and control groups. The confounding entropy generalizes conventional confounding bias, and can be estimated effectively using historical controls. We develop a new method, debiased causal tree, whose splitting rule is to minimize the empirical risk regularized by the confounding entropy. Notably, our method integrates current observational data (for empirical risk) and their historical controls (for confounding entropy) harmoniously. We highlight that, debiased causal tree can not only estimate CATT well in the presence of unmeasured confounding, but also is a robust estimator of conditional average treatment effect (CATE) against the imbalance of the treated and control populations when all confounders are observed. An extension of combining multiple debiased causal trees to further reduce biases by gradient boosting is considered. The computational feasibility and statistical power of our method are evidenced by simulations and a study of a credit card balance dataset.

As one of the core components of customer service bot, User Intent Prediction (UIP) aims at predicting users? intents (usually represented as predefined user questions) before they ask, and has been widely applied in real applications. However, when developing a machine learning system for this problem, two critical issues, i.e., the problem of feature drift and class imbalance, may emerge and seriously deprave the system performance. Moreover, various scenarios may arise due to business demands, making the aforementioned problems much more severe. To address these two problems, we propose an attention-based Deep Multi-instance Sequential Cross Network (aDMSCN) to deal with the UIP task. On the one hand,the UIP task can be subtly formalized as multi-instance learning(MIL) task with an attention-based method proposed to alleviate the influences of feature drift. To the best of our knowledge, this is the first attempt to model the problem from a MIL perspective.On the other hand, a ratio-sensitive loss is also developed in our model, which can mitigate the negative impact of class imbalance. Extensive experiments on both offline real-world datasets and on-line A/B testing show that our proposed framework significantly out performs other state-of-art methods for the UIP task.

Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems. Recently, a growing effort has been made to the development of automatic feature engineering methods, so that the substantial and tedious manual effort can be liberated. However, for industrial tasks, the efficiency and scalability of these methods are still far from satisfactory. In this paper, we proposed a staged method named SAFE (Scalable Automatic Feature Engineering), which can provide excellent efficiency and scalability, along with requisite interpretability and promising performance. Extensive experiments are conducted and the results show that the proposed method can provide prominent efficiency and competitive effectiveness when comparing with other methods. What's more, the adequate scalability of the proposed method ensures it to be deployed in large scale industrial tasks.

Multi-task learning (MTL) aims at improving the generalization performance of several related tasks by leveraging useful information contained in them. However, in industrial scenarios, interpretability is always demanded, and the data of different tasks may be in heterogeneous domains, making the existing methods unsuitable or unsatisfactory. In this paper, following the philosophy of boosted tree, we proposed a two-stage method. In stage one, a common model is built to learn the commonalities using the common features of all instances. Different from the training of conventional boosted tree model, we proposed a regularization strategy and an early-stopping mechanism to optimize the multi-task learning process. In stage two, started by fitting the residual error of the common model, a specific model is constructed with the task-specific instances to further boost the performance. Experiments on both benchmark and real-world datasets validate the effectiveness of the proposed method. What's more, interpretability can be naturally obtained from the tree based method, satisfying the industrial needs.

Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large-scale tasks with great performance is widely needed. Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. However, it has not been tested on extremely large-scale tasks. In this work, based on our parameter server system, we developed the distributed version of deep forest. To meet the need for real-world tasks, many improvements are introduced to the original deep forest model, including MART (Multiple Additive Regression Tree) as base learners for efficiency and effectiveness consideration, the cost-based method for handling prevalent class-imbalanced data, MART based feature selection for high dimension data, and different evaluation metrics for automatically determining the cascade level. We tested the deep forest model on an extra-large-scale task, i.e., automatic detection of cash-out fraud, with more than 100 million training samples. Experimental results showed that the deep forest model has the best performance according to the evaluation metrics from different perspectives even with very little effort for parameter tuning. This model can block fraud transactions in a large amount of money each day. Even compared with the best-deployed model, the deep forest model can additionally bring a significant decrease in economic loss each day.

This paper describes a PU learning (Positive and Unlabeled learning) based system for potential URL attack detection. Previous machine learning based solutions for this task mainly formalize it as a supervised learning problem. However, in some scenarios, the data obtained always contains only a handful of known attack URLs, along with a large number of unlabeled instances, making the supervised learning paradigms infeasible. In this work, we formalize this setting as a PU learning problem, and solve it by combining two different strategies (two-stage strategy and cost-sensitive strategy). Experimental results show that the developed system can effectively find potential URL attacks. This system can either be deployed as an assistance for existing system or be employed to help cyber-security engineers to effectively discover potential attack mode so that they can improve the existing system with significantly less efforts.

Multi-instance learning (MIL) deals with the tasks where each example is represented by a bag of instances. A bag is positive if it contains at least one positive instance, and negative otherwise. The positive instances are also called key instances. Only bag labels are observed, whereas specific instance labels are not available in MIL. Previous studies typically assume that training and test data follow the same distribution, which may be violated in many real-world tasks. In this paper, we address the problem that the distribution of key instances varies between training and test phase. We refer to this problem as MIL with key instance shift and solve it by proposing an embedding based method MIKI. Specifically, to transform the bags into informative vectors, we propose a weighted multi-class model to select the instances with high positiveness as instance prototypes. Then we learn the importance weights for transformed bag vectors and incorporate original instance weights into them to narrow the gap between training/test distributions. Experimental results validate the effectiveness of our approach when key instance shift occurs.