System tracks regulatory changes for compliance officers.
problem Struggling to keep up with regulatory changes.
method Fetch announcements, classify importance and applicability.
result Simple hierarchical classification works best.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “Business Process Classification”
Recurrent neural networks improve process instance classification.
problem Classifying ongoing process instances based on activities.
method Applied recurrent neural networks, specifically GRU, to classify business process instances.
result GRU outperforms LSTM in training time with similar accuracy.
CONDA-PM framework helps analyze concept drift in business processes.
problem Analyzing changes in business processes over time.
method Systematic Literature Review and framework development.
result Highlights areas needing research to complement existing efforts.
The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
Development of efficient business process models and determination of their characteristic properties are subject of intense interdisciplinary research. Here, we consider a business process model as a directed graph. Its nodes correspond to the units identified by the modeler and the link direction indicates the causal…
Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel m…
This study integrates cost-sensitive and causal classification methods.
problem Improving classification model performance in business decision-making.
method A unifying evaluation framework for cost-sensitive and causal classification.
result Conventional classification is a specific case of causal classification.
Study evaluates sustainability of European banks using a new model.
problem Lack of a framework to evaluate sustainability of banking business models.
method Delphi-Analytic Hierarchy Process method to develop and assess the model.
result Norwegian and German banks have higher sustainability of their business models.
New approach optimizes sales process for B2B businesses.
problem Optimizing the sales process for B2B businesses.
method Causal Predictive Optimization and Generation with three layers: prediction, optimization, and serving.
result Significant wins over legacy systems in LinkedIn implementation.
Graph neural networks detect anomalies in object-centric business processes.
problem Detecting anomalies in graph-like business processes.
method Graph convolutional autoencoder architecture for anomaly detection.
result Promising performance in detecting anomalies at the activity type and attributes level.
This paper studies business cycle patterns in UK sectoral output. It analyzes the distinction between white noise processes and their non-white noise counterparts in the frequency domain and further examines the associated features and patterns for the process where white noise conditions are violated. The characterist…
Paper proposes a new method for learning business process representations.
problem Challenges in capturing all useful information in business process data.
method Combines Gramian Angular Fields and Convolutional Neural Networks for representation learning.
result Demonstrates effectiveness of the approach through visualization and multiple process prediction tasks.
Reduces false positives in classifying rare online platforms.
problem Challenges in accurately identifying rare online platforms with ML.
method Calibrated probabilities and ensembles to reduce bias.
result Significantly reduces false positives in rare event detection.
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networkscs.LG
GRM uses graph neural networks to score process activity relevance.
problem Improving business processes with performance measures.
method Graph Relevance Miner (GRM) based on graph neural networks.
result Quantitatively evaluated relevance scores with four datasets.
Business Architecture (BA) plays a significant role in helping organizations understand enterprise structures and processes, and align them with strategic objectives. However, traditional BAs are represented in fixed structure with static model elements and fail to dynamically capture business insights based on interna…
New method improves prediction accuracy in business process mining by handling concept drift.
problem Improving prediction quality in business process mining affected by concept drift.
method Systematically analyzed and compared different data selection strategies for retraining machine learning models.
result Improved accuracy from 0.5400 to 0.7010 with concept drift handling.
New method builds business taxonomies from corporate reports.
problem Challenging to classify emerging market industries.
method Concept-level hierarchical clustering of annual reports.
result Automatic construction of business taxonomies.
Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situ…
The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business proc…
Generative AI agents improve ERP systems by automating complex financial tasks.
problem Static, rule-based workflows limit adaptability and intelligence in ERP systems.
method Introducing Generative Business Process AI Agents (GBPAs) that integrate generative AI with business process modeling and multi-agent orchestration.
result GBPAs achieve up to 40% reduction in processing time and 94% drop in error rate.
This paper explores how to interpret machine learning models in business process analytics.
problem The lack of interpretability in machine learning models used for predictive process analytics.
method Derives explanations using interpretable machine learning techniques to compare and contrast predictive models.
result Highlights scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data.
Study proposes a novel local explanation method for deep learning classifiers in process mining.
problem Lack of interpretability in deep learning models for process mining.
method Defines local regions using latent space representations and visualizes explanations.
result Deep learning classifier achieves high performance and local explanations increase user trust.
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper pr…
Paper tackles invoice line item matching in P2P processes using agent feedback.
problem Matching product/service descriptions in invoices with purchase orders.
method Two approaches using agent feedback data: similarity ranking and classification.
result Proposed approaches outperform benchmarks and real-world data sets.
New framework maximizes perturbed samples for inverse classification with budget constraints.
problem Maximizing perturbed samples for desired classification outcomes under budget constraints.
method Gradient methods, stochastic processes, Lagrangian relaxations, Gumbel trick.
result Stochastic process-based algorithms outperform in different budget settings.
Extends expected value framework for cost-sensitive causal decision-making.
problem Optimizing operational decision-making with cost-sensitive causal classification.
method Introduces a cost-sensitive decision boundary based on estimated individual treatment effects, positive outcome probability, and cost parameters.
result Effective in maximizing expected causal profit, outperforming cost-insensitive ranking approach.
Bayesian model uses mobile data to assess business resilience after hurricanes.
problem Evaluating economic impact of extreme shocks on businesses.
method Bayesian structural time series model with mobile phone data.
result Estimates business resilience after hurricanes, revealing key characteristics.
Study improves document processing in banking with multimodal analytics.
problem Raising operational efficiency in banking through document-intensive processes.
method Comparative analysis of text classifiers and multimodal model (LayoutXLM) on company register extracts.
result Incorporating layout information in a model substantially increases performance.
The paper proposes a ML workflow for B2B sales prediction.
problem Predicting business to business sales outcomes using subjective human evaluations.
method A two-pipeline ML approach on Azure ML, including data enrichment and model training.
result ML predictions improve accuracy and increase sales value.
The paper analyzes optimal dividend strategies for risky businesses, considering both periodic and extraordinary payments.
problem Maximizing dividends paid until ruin, net of transaction costs.
method Modeling cash surplus as Brownian motion, considering different types of dividends with transaction costs.
result Optimal strategies depend on business profitability and transaction costs, sometimes including liquidation.
We have created a framework for analyzing subscription based businesses in terms of a unified metric which we call SCV (single customer value). The major advance in this paper is to model customer churn as an exponential decay variable, which directly follows from experimental data relating to subscription based busine…
XGBoost model improves business risk classification with feature selection and Bayesian hyper-parameter optimization.
problem Improving business risk classification models using advanced machine learning techniques.
method XGBoost, feature selection (FS), Bayesian hyper-parameter optimization (TPE and RS), 10-fold cross-validation.
result Bayesian TPE optimization outperforms random search and achieves higher accuracy and AUC, recall, and F1 score.
Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)cs.AI
This paper evaluates conformance measures in process mining using conformance propositions.
problem Lack of formal definition and evaluation of conformance measures in process mining.
method Formulated 21 conformance propositions to evaluate existing measures.
result Identified challenges and requirements for conformance measures in process mining.
Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approachcs.CL
The study detects deceptive language in business communication using AI.
problem Deceptive language in business communication.
method Combining classical rhetoric, communication psychology, and linguistic theory with computational textual analysis and transformer models.
result Detection accuracies of over 99% achieved in controlled settings.
Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now accessible to comprehensive analyses with the availability of large data…
We present examples of agent-based and stochastic models of competition and business processes in economics and finance. We start from as simple as possible models, which have microscopic, agent-based, versions and macroscopic treatment in behavior. Microscopic and macroscopic versions of herding model proposed by Kirm…
Paper proposes GANs for generating business process suffixes and remaining times.
problem Generating accurate suffixes and remaining times for business process models.
method Encoder-decoder GAN architecture with Gumbel-Softmax distribution for categorical sequences.
result The approach outperforms baselines in suffix and remaining time prediction accuracy.
Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. In this paper CPU sales forecasting of Intel Corporation, a multinational semico…
We study a model of a corporation which has the possibility to choose various production/business policies with different expected profits and risks. In the model there are restrictions on the dividend distribution rates as well as restrictions on the risk the company can undertake. The objective is to maximize the exp…
Graph-based method predicts business conduct risk from incomplete data.
problem Sparse and biased data limits risk assessment.
method Visibility-aware GCNII framework on corporate graph.
result Graph-based approach outperforms non-graph methods in predicting future incidents.
Cleaner Production in Optimized Multivariate Networks: Operations Management through a Roll of Diceq-fin.GN
The study optimizes supply chain management through a dice-based model to predict cleaner production.
problem Uncertainty in supply chain management and economic predictions.
method A 4-component SC module (environmental, demand, economic, social uncertainties) ranked by weight, using Analytical Hierarchical Process and optimization of a weighted cost function.
result Identifies conditions validating the sustainability of a business venture and optimizes market uncertainty.
Paper proposes a GAN-based method for better next event prediction in business processes.
problem Insufficient training data and sub-optimal network configuration limit deep learning approaches to next event prediction.
method Adversarial training framework using Generative Adversarial Networks (GANs) for sequential temporal data.
result The proposed approach achieves at least as good accuracy as non-adversarial methods and outperforms them in accuracy and prediction earliness.
Predictive process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. Traditionally, predictive process monitoring methods are optimized with respect to accuracy. However, in environments …
We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from p…
Paper identifies food types from Yelp photos using machine learning.
problem Ineffective labeling of food photos on Yelp.
method Image pre-processing, CNN feature extraction, and classification algorithms.
result Identifies up to 10 food types from raw photos with high accuracy.
Enhances price sentiment index using survey comments.
problem Improving accuracy in price sentiment analysis.
method Classified comments from Economy Watchers Survey using LLMs.
result Higher correlation with existing indices.
Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring…
A new approach optimizes capital allocation for firms with multiple business lines.
problem Optimizing capital allocation for firms with multiple business lines, especially considering correlations between lines.
method Introducing a common environmental factor, a Bayesian approach to calibrate latent state distribution, and optimal capital determination.
result Developed an easy-to-implement approach for capital risk management in multi-dimensional insurance risk models.