AI brain analyzing interconnected business processes for anomalies.

Decoding Business Process Blindspots: How AI Can Spot Anomalies and Boost Efficiency

Jordan Keane in Tech & Innovation September 2025 • 4 min read.

"Uncover hidden inefficiencies and errors in your business with AI-powered anomaly detection. Learn how graph neural networks are revolutionizing process mining."

In today's fast-paced business environment, process mining has become an essential tool for organizations seeking to understand, monitor, and enhance their operational workflows. By leveraging data traces generated during business process execution, companies can gain valuable insights into how work gets done. This information can then be used to identify areas for improvement, streamline operations, and ultimately boost efficiency and profitability.

Traditional process mining approaches often rely on analyzing 'flattened' event logs, which present a simplified, sequential view of business processes. However, real-world process executions are rarely so straightforward. They often exhibit complex, graph-like structures, where events can be associated with multiple cases or objects. This complexity poses a significant challenge for traditional techniques, which may struggle to capture the nuances of these intricate relationships.

Enter object-centric process mining, a paradigm that embraces the complexity of real-world processes by allowing events to be related to different objects or cases. This approach offers a more accurate and comprehensive depiction of how work is actually performed. Now, a new study introduces a novel framework for anomaly detection in object-centric business processes, leveraging the power of graph neural networks (GNNs) to identify deviations from normal behavior and unlock new opportunities for optimization.

What are Anomalous Events in Object-Centric Business Processes?

AI brain analyzing interconnected business processes for anomalies.

Anomalous events in object-centric business processes are occurrences that deviate significantly from the established patterns and norms of operation. These anomalies can manifest in various forms, including:

Inefficiencies: Activities that consume excessive time or resources, hindering overall process performance.
Errors: Mistakes or defects in process execution that lead to rework, delays, or customer dissatisfaction.
Fraud: Intentional manipulation of processes for personal gain or malicious purposes.

Activity Type Anomalies: Unexpected or unauthorized activities occurring within the process.
Attribute Anomalies: Unusual or inconsistent data values associated with specific events.
Temporal Order Anomalies: Deviations from the expected sequence of events.

Detecting these anomalies is crucial for maintaining the effectiveness and efficiency of business operations. By identifying and addressing these irregularities, organizations can mitigate risks, improve compliance, and enhance overall performance.

The Future of Anomaly Detection in Business Processes

The study's findings suggest that GNNs hold immense potential for anomaly detection in object-centric business processes. By leveraging the power of graph-based analysis, organizations can gain deeper insights into their operational workflows, identify hidden inefficiencies, and mitigate potential risks. As GNN technology continues to evolve, we can expect even more sophisticated and effective anomaly detection solutions to emerge, further transforming the landscape of business process management.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2403.00775,

Title: Detecting Anomalous Events In Object-Centric Business Processes Via Graph Neural Networks

Subject: q-fin.st cs.db cs.lg

Authors: Alessandro Niro, Michael Werner

Published: 14-02-2024

Everything You Need To Know

What makes object-centric process mining different from traditional process mining?

Traditional process mining often simplifies business processes by analyzing 'flattened' event logs, presenting a sequential view. Object-centric process mining, on the other hand, embraces the complexity of real-world processes. It allows events to be related to different objects or cases, providing a more accurate and comprehensive depiction of how work is actually performed. This is especially useful when processes involve intricate relationships that traditional methods struggle to capture. Missing from traditional is the object relationships that provide a broader, and often, clearer picture of process flow and bottlenecks. By not accounting for the object, you only get a siloed understanding of a more complicated process.

What are some examples of anomalies that can be detected in object-centric business processes?

Anomalies in object-centric business processes can take various forms. These include inefficiencies where activities consume excessive time or resources, errors which are mistakes or defects in process execution leading to rework or delays, and fraud where there is intentional manipulation of processes for personal gain. Furthermore, anomalies can be categorized as activity type anomalies, indicating unexpected activities; attribute anomalies, showing unusual data values; and temporal order anomalies, which are deviations from the expected sequence of events. Discovering these anomalies early is key to a healthy process.

How can graph neural networks (GNNs) be used to detect anomalies in business processes?

Graph neural networks (GNNs) can analyze the graph-like structures inherent in object-centric business processes. By leveraging graph-based analysis, GNNs can identify deviations from normal behavior and unlock opportunities for optimization that might be missed by traditional methods. GNN's are able to detect patterns in complex relationships between events and objects, making them powerful tools for anomaly detection. GNN's provide the depth to understanding the intricate connections that are not accessible otherwise.

Why is it important to detect anomalies in business processes?

Detecting anomalies is crucial for maintaining the effectiveness and efficiency of business operations. By identifying and addressing irregularities such as inefficiencies, errors, and fraud, organizations can mitigate risks, improve compliance, and enhance overall performance. Addressing these anomalies proactively leads to optimized processes, reduced costs, and increased customer satisfaction. Anomaly detection ensures that business processes are running as intended, minimizing potential disruptions and maximizing value.

What are the potential future advancements in anomaly detection for business process management using GNNs?

As graph neural network (GNN) technology continues to evolve, we can anticipate more sophisticated and effective anomaly detection solutions to emerge. Future advancements may include enhanced algorithms capable of identifying more subtle and complex anomalies, improved integration with real-time data streams, and the development of user-friendly interfaces that make GNN-based anomaly detection accessible to a wider range of business users. These advancements promise to further transform the landscape of business process management by providing deeper insights and more proactive risk mitigation capabilities. The ability to predict anomalies before they impact production will be a huge leap forward.