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 fraudulent insurance claim is critical for the insurance industry.Insurance companies or agency platforms aim to confidently estimate the fraud risk of claims by gathering data from various sources.Although more data sources can improve the estimation accuracy, they inevitably lead to increased costs.Therefore, a great challenge of fraud risk verification lies in well balancing these two aspects.To this end, this paper proposes a framework named cost-efficient fraud risk optimization with submodularity (CEROS) to optimize the process of fraud risk verification.CEROS efficiently allocates investigation resources across multiple information sources, balancing the trade-off between accuracy and cost.CEROS consists of two parts that we propose: a submodular set-wise classification model * Equal Contribution.

Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the user preference. Existing algorithms usually extract a user representation vector from the user behavior sequence, then feed the vector into a feed-forward network (FFN) together with other features for feature interactions, and finally produce a personalized ranking score. Despite tremendous progress in the past, there is still room for improvement. Firstly, the personalized patterns of feature interactions for different users are not explicitly modeled. Secondly, most of existing algorithms have poor personalized ranking results for long-tail users with few historical behaviors due to the data sparsity.To overcome the two challenges, we propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. Firstly, AW-MoE leverages the MoE framework to capture personalized feature interactions for different users. To model the user preference, the user behavior sequence is simultaneously fed into expert networks and the gate network. Within the gate network, one gate unit and one activation unit are designed to adaptively learn the fine-grained activation vector for experts using an attention mechanism. Secondly, a random masking strategy is applied to the user behavior sequence to simulate long-tail users, and an auxiliary contrastive loss is imposed to the output of the gate network to improve the model generalization for these users. This is validated by a higher performance gain on the long-tail user test set.Experiment results on a JD real production dataset and a public dataset demonstrate the effectiveness of AW-MoE, which significantly outperforms state-of-art methods. Notably, AW-MoE has been successfully deployed in the JD e-commerce search engine, serving the real traffic of hundreds of millions of active users.

Per-flow size measurement is key to many streaming applications and management systems, particularly in high-speed networks. Performing such measurement on the data plane of a network device at the line rate requires on-chip memory and computing resources that are shared by other key network functions. It leads to the need for very compact and fast data structures, called sketches, which trade off space for accuracy. Such a need also arises in other application context for extremely large data sets. The goal of sketch design is two-fold: to measure flow size as accurately as possible and to do so as efficiently as possible (for low overhead and thus high processing throughput). The existing sketches can be broadly categorized to multi-update sketches and single update sketches. The former are more accurate but carry larger overhead. The latter incur small overhead but their accuracy is poor. This paper proposes a Single update Sketch with a Variable counter Structure (SSVS), a new sketch design which is several times faster than the existing multi-update sketches with comparable accuracy, and is several times more accurate than the existing single update sketches with comparable overhead. The new sketch design embodies several technical contributions that integrate the enabling properties from both multi-update sketches and single update sketches in a novel structure that effectively controls the measurement error with minimum processing overhead.

Estimating the cardinality of a data stream is a fundamental problem underlying numerous applications such as traffic monitoring in a network or a datacenter, popularity tracking on social media, and cache optimization in proxy servers. Existing solutions suffer from high processing/query overhead or memory in-efficiency, which prevents them from operating online for data streams with very high arrival rates. This paper takes a new solution path different from the prior art and proposes a self-morphing bitmap, which combines operational simplicity with structural dynamics, allowing the bitmap to be morphed in a series of steps with an evolving sampling probability that automatically adapts to different stream sizes. We evaluate the self-morphing bitmap theoretically and experimentally. The results demonstrate that it significantly outperforms the prior art.

Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, data item processing throughput, and online query throughput.

We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.