Abstract Detecting and reacting to non-technical losses (NTL) is a fundamental activity that energy providers need to face in their daily routines. This is known to be challenging since the phenomenon of NTL is multi-factored, dynamic and extremely contextual, which makes artificial intelligence (AI) and, in particular, machine learning, natural areas to bring effective and tailored solutions. If the human factor is disregarded in the process of detecting NTL, there is a high risk of performance degradation since typical problems like dataset shift and biases cannot be easily identified by an algorithm. This paper presents a case study on incorporating explainable AI (XAI) in a mature NTL detection system that has been in production in the last years both in electricity and gas. The experience shows that incorporating this capability brings interesting improvements to the initial system and especially serves as a common ground where domain experts, data scientists, and business analysts can meet.

The detection of Non-Technical Losses using black-box supervised classification algorithms faces technical, economic, and transparency challenges that compromise the quality and fairness of predictions. In this work, we explain how we have mitigated them in a deployed NTL detection system by moving from classification into a regression system and introducing explanatory techniques to improve its accuracy and understanding.

Process mining techniques aim at analyzing and monitoring processes through event data. Formal models like Petri nets serve as an effective representation of the processes. A central question in the field is to assess the conformance of a process model with respect to the real process executions. The notion of anti-alignment, which represents a model run that is as distant as possible to the process executions, has been demonstrated to be crucial to measure precision of models. However, the only known algorithm for computing anti-alignments has a high complexity, which prevents it from being applied on real-life problem instances. In this paper we propose a novel algorithm for computing anti-alignments, based on the well-known graph-based $A^{*}$ scheme. By introducing a discount factor in the edit distance used for the search of anti-alignments, we obtain the first efficient algorithm to approximate them. We show how this approximation is quite accurate in practice, by comparing it with the optimal results for small instances where the optimal algorithm can also compute anti-alignments. Finally, we compare the obtained precision metric with respect to the state-of-the-art metrics in the literature for real-life examples.

Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.

Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.

The discovery of process models from event logs has emerged as one of the crucial problems for enabling the continuous support in the life-cycle of an information system. However, in a decade of process discovery research, the algorithms and tools that have appeared are known to have strong limitations in several dimensions. The size of the logs and the formal properties of the model discovered are the two main challenges nowadays. In this paper we propose the use of numerical abstract domains for tackling these two problems, for the particular case of the discovery of Petri nets. First, numerical abstract domains enable the discovery of general process models, requiring no knowledge (e.g., the bound of the Petri net to derive) for the discovery algorithm. Second, by using divide and conquer techniques we are able to control the size of the process discovery problems. The methods proposed in this paper have been implemented in a prototype tool and experiments are reported illustrating the significance of this fresh view of the process discovery problem.

A central problem in the area of Process Mining is to obtain a formal model that represents the processes that are conducted in a system. If realized, this simple motivation allows for powerful techniques that can be used to formally analyze and optimize a system, without the need to resort to its semiformal and sometimes inaccurate specification. The problem addressed in this paper is known as Process Discovery: to obtain a formal model from a set of system executions. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets) from a set of traces. On its genuine form, the theory is applied on an automaton and therefore one should convert the traces into an acyclic automaton in order to apply these techniques. Given that the complexity of the region-based techniques depends on the size of the input automata, revealing the underlying cycles and folding the initial automaton can incur in a significant complexity alleviation of the region-based techniques. In this paper, we follow this idea by incorporating region information in the cycle detection algorithm, enabling the identification of complex cycles that cannot be obtained efficiently with state-of-the-art techniques. The experimental results obtained by the devised tool suggest that the techniques presented in this paper are a big step into widening the application of the theory of regions in Process Mining for industrial scenarios.

Process Conformance is becoming a crucial area due to the changing nature of processes within an Information System. By confronting specifications against system executions (the main problem tackled in process conformance), both system bugs and obsolete/incorrect specifications can be revealed. This paper presents novel techniques to enrich the process conformance analysis for the precision dimension. The new features of the metric proposed in this paper provides a complete view of the precision between a log and a model. The techniques have been implemented as a plug-in in an open-source Process Mining platform and experimental results witnessing both the theory and the goals of this work are presented.