Decision rules have been widely applied in industrial applications such as finance, medicine, and biology, due to the critical requirement of interpretability. In order to make decision rules easier and more widely used in financial scenarios, an automatic intelligent rule system with rule learning and rule management capabilities is needed. However, the rule system for financial applications has distinctive challenges both in algorithms and systems. From the algorithm perspective, due to the characteristics of the financial data and scenarios, the rule learning algorithm faces the class-imbalanced issue, the scalability issue, and the diversity of optimization objectives. From the system perspective, a flexible rule learning and management framework is needed to adapt to fast-changing financial applications with heterogenous data, and engineering optimization is required to ensure the time and space efficiency of rule learning. In this work, we focus on developing a Rule-based Decision System (RDS) to deal with the algorithmic and systematic challenges mentioned above. RDS covers the full life cycle of the decision rules, including the rule learning module, rule management module, and rule deployment module. Moreover, the rule system offers an interactive interface to allow users to integrate the expert experiences into the decision rules and realize the human-in-the-loop. The RDS has been deployed on one of the world’s largest trading and money transfer platforms, serving hundreds of millions of users and transactions.

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.

Machine learning methods for fraud detection have achieved impressive prediction performance, but often sacrifice critical interpretability in many applications. In this work, we propose to learn interpretable models for fraud detection as a simple rule set. More specifically, we design a novel neural rule learning method by building a condition graph with an expectation to capture the high-order feature interactions. Each path in this condition graph can be regarded as a single rule. Inspired by the key idea of meta learning, we combine the neural rules with rules extracted from the tree-based models in order to provide generalizable rule candidates. Finally, we propose a flexible rule set learning framework by designing a greedy optimization method towards maximizing the recall number of fraud samples with a predefined criterion as the cost. We conduct comprehensive experiments on large-scale industrial datasets. Interestingly, we find that the neural rules and rules extracted from tree-based models can be complementary to each other to improve the prediction performance.

This paper presents a fuzzy reasoning Petri net (FRPN) model to represent a fuzzy production rule-based system. The issues of how to represent and reason about rules containing negative literals are addressed in the proposed PN model. The execution rules based on the model are defined formally using the operators in max-algebra. Then, a fuzzy reasoning algorithm is proposed to perform fuzzy reasoning automatically. The algorithm is consistent with the matrix equation expression method in the traditional PNs and allows one to exploit the maximum parallel reasoning potential embedded in the model. The legitimacy and feasibility of the proposed approach are proved and validated through a turbine fault diagnosis expert system.