Credit scoring is an important topic in financial activities and bankruptcy prediction that has been extensively explored using deep neural network (DNN) methods. DNN-based credit scoring models rely heavily on a large amount of labeled data. The accuracy of DNN-based credit assessment models relies heavily on large amounts of labeled data. However, purely data-driven learning makes it difficult to encode human intent to guide the model to capture the desired patterns and leads to low transparency of the model. Therefore, the Probabilistic Soft Logic Posterior Regularization (PSLPR) framework is proposed for integrating prior knowledge of logic rule with neural network. First, the PSLPR framework calculates the rule satisfaction distance for each instance using a probabilistic soft logic formula. Second, the logic rules are integrated into the posterior distribution of the DNN output to form a logic output. Finally, a novel discrepancy loss which measures the difference between the real label and the logic output is used to incorporate logic rules into the parameters of the neural network. Extensive experiments were conducted on two datasets, the Australian credit dataset and the credit card customer default dataset. To evaluate the obtained systems, several performance metrics were used, including PCC, Recall, F1 and AUC. The results show that compared to the standard DNN model, the four evaluation metrics are increased by 7.14%, 14.29%, 8.15%, and 5.43% respectively on the Australian credit dataset.

Credit is a part of external image of enterprises, and it directly affects interests of enterprises. Nowadays, most of researches on predictions of enterprises credit use a single algorithm model or optimize a single model to predict an enterprises credit score. The accuracy of each model is different, and the generalization ability is generally weak. In order to improve generalization ability of models and accuracy of prediction results, a parallel double-layer prediction model is proposed in this paper. The model is based on Stacking and Bagging methods, which can improve generalization ability with high accuracy. Through experiments, we compare three single algorithm models, four integrated learning models with other combination strategies and parallel double-layer prediction model. Average value of four evaluation indexes are increased by 4.2349%, 63.1464%, 34.11837%, 1.26104%, 15.7862%, 10.1457% and 25.6310% respectively. The results show that the parallel double-layer prediction model is accurate and feasible.

As interactions between individuals and services increase, requests are more frequent and policy sets are larger. The evaluation performance of PDP (Policy Decision Point) plays a key role in the operation of a system. In order to solve bottlenecks of improving the PDP evaluation performance for large-scale policy sets, we propose an evaluation engine based on artificial neural networks, namely ANNPDP. We transform rules in a large-scale policy set described in the XACML (eXtensible Access Control Markup Language) into numerical rules. Evaluation networks are established and trained by the numerical rules. In order to ensure the accuracy, a misjudgment set is constructed for error corrections and stored by hash indexes. By simulating the arrival of requests, ANNPDP is compared with the Sun PDP, HPEngine, XEngine, and SBA-XACML. The experiment results show that ANNPDP has: 1) high performance: if the number of requests reaches 10,000, the evaluation time of ANNPDP on the large-scale policy set with 100,000 rules is approximately 0.46, 0.93, 0.71, and 1.43 percent of that of the Sun PDP, HPEngine, XEngine, and SBA-XACML, respectively, and 2) stability: as the size of the large-scale policy set and the number of requests increase, the evaluation time of ANNPDP grows linearly. ANNPDP can satisfy the requirements of an authorization system with large-scale policy sets.

EXtensible Access Control Markup Language (XACML) is one of the standardized languages for specifying access control policies. Policies described by the XACML are used to express the security requirement in the network and information system when we study authorization access control. With the aim to improve the Policy Decision Point (PDP) evaluation performance, we put forward a Graph and Clustering-Based Framework, employing the aggregate function. First, we partition the rule set into subsets. For the single value, we select the best partition quantity based on the aggregate function. As for the interval value, we handle with the start point and the finish point, respectively, in the same way as single value. Second, the policy set is split according to the partition of rule set. In this way, not only single values, but also interval values are taken into consideration. After that, we explore the searching tree to obtain the possibly matched rules. Finally, we construct the combining tree and output the policy decision on the basis of it. The experimental results show that our approach is orders of magnitude better than the Sun PDP. A comparison in evaluation performance between the redundancy detecting and eliminating engine and the Sun PDP, as well as XEngine and SBA-XACML, is made. Experimental results show that the evaluation performance of the PDP can be prominently improved by eliminating redundancies.