Responsible AI-Based Business Process Management and Improvement

Organizations today seek to improve and adapt their business processes because of an increasingly competitive economy. The use and application of Artificial Intelligence (AI) for business process improvement and management is often discussed and put in place, regardless of its potentially negative impact if AI is implemented in the wrong way, especially around the processing and storing of personal data. We discuss the use of AI for the management and improvement of business processes, especially in the financial domain, and how to ensure responsible AI use in enterprises for this aim. We propose an information system design for responsible and trustworthy business processes, and we envision that businesses will need strong and well-defined control points in their information systems for managing processes and creating associated audits to enforce their principles. We define questions and challenges that companies will need to reflect upon and follow to achieve an application of responsible AI in an enterprise context. We also outline considerations for AI and data protection regulation for companies, while also considering the technical challenges that would need to be solved

AI-Based Solution for Sustainability Tracing for Companies

Many companies look for novel ways to trace their operational sustainability. The application of AI to analyze and make sense of the big data the company holds represents one promising approach for this aim. The authors study how one can set and design an AI-based solution for improving the sustainability of complex business processes and decision-making in companies of different types. First, they provide a general analysis of current frameworks for measuring adherence to sustainability goals for companies, then they present a conceptual framework and architecture design for an AI-enabled sustainability service for companies. The implications of our research suggest that AI can provide distinct functions: (a) automation: taking big data from different departments and analyzing them with the aim of tracing the sustainability of the company; (b) support: to help decision-making and create relevant insights for stakeholders that are coherent with defined sustainability decision criteria. To the authors' knowledge, no previous research has provided analysis and design of such AI solution for companies

Knowledge Management and Data Analysis Techniques for Data-Driven Financial Companies

In today’s fast-paced financial industry, knowledge management and data-driven decision making have become essential for the success of financial technology (FinTech) companies. Big data (BD) is a prevalent phenomenon that can be found across many industries, including finance. Despite its complexity and difficulty to comprehend, big data is a critical component of financial services enterprises and technology architectures. We examine BD from various aspects, considering data science (DS) techniques and methodologies that can be applied during the operation of an enterprise. Our aim is to provide an overview of knowledge management (KM) practices and data analysis (DA) strategies and techniques in the daily operations of financial companies. We address the role of knowledge management, data analytics in a financial institution. The paper demonstrates financial institutions’ enablement for new services resulting from technological advancements

Identifying struggling teams in online challenge-based learning

Our research aims to determine how students perceive groupwork and identify patterns of less successful groups in online challenge-based learning.This study involved 29 university students working in nine teams in an online challenge-based course. We applied Volet’s (2001) Student Appraisal of Group Assignments (SAGA) instrument to measure students’ perceptions on six constructs: Cognitive Benefits, Motivation Influence, Affect, Interpersonal, Management, and Group Assessment. Questionnaires were administered at different time points (before, during, and after the project). Focus groups were conducted to gain insights into students’ experiences.Findings suggest that students reporting decreasing or stalling perception scores on the Motivation Influence, Interpersonal constructs would likely not be in high-performing groups. Additionally, challenge-based learning is less suitable for time-compressed courses.The study expands our understanding of students’ perceptions of online challenge-based learning, at different performance levels, and difficulties in these projects. Practical implications of this study are support for teachers in identifying struggling teams, and designing and facilitating challenge-based courses

Combating Misinformation and Polarization in the Corporate Sphere: Integrating Social, Technological and AI Strategies

In an era where misinformation and polarization present significant challenges, this research examines the root causes within social networks and assesses how corporations can use AI technologies for prompt detection. This research uses a dual approach: a "telephone game" with 225 participants from a Spanish university to study the spread of misinformation, and interviews with 15 experts from three French tech companies to investigate technological solutions. The findings indicate that almost one-third of participants inadvertently contribute to polarization, and around one-quarter propagated misinformation. The study also identifies the existing tools enhanced by AI and Machine Learning that effectively detect misinformation and polarization in corporate settings. This investigation provides crucial insights for practitioners to strengthen their strategies against misinformation and technical challenges and opportunities

Model to Program and Blockchain Approaches for Business Processes and Workflows in Finance

Business process modeling and verification have become an essential way to control and assure organizational evolution. We overview the opportunities for the application of blockchain in Business Process Management and Modeling in Finance and we focus on in-depth analysis of claim process in insurance as a use case. We investigate the utilization of blockchain technology for model checking of Workflow, Business Processes to ensure consistency, integrity, and security in a dynamically changing business environment. We create a UML profile for the blockchain, then we combine it with a UML activity diagram followed by a verification using Petri nets to guarantee a distributed computing system and scalable with mutable data. Our paper creates a unified picture of the approaches towards business processes modeling used in the financial industry organized around the set of premises intending to develop a future research agenda for blockchain business process modeling, specifically for the financial industry domain

Investigating effective dynamics of virtual student teams through analysis of Trello boards

A recent challenge in distant distributed learning courses is to establish effective collaboration in small teams. Team work in such courses often has to take place asynchronously, which puts additional requirements on communication and coordination. This article describes an ongoing project in which we analyze the dynamics of virtual student teams located in different Universities by examining their Trello activity. The students work together in a project where they need to propose a solution for a business challenge. On the basis of their Trello activity we aim to characterize different patterns of team work in geographically dispersed teams to better understand how collaboration in virtual students teams develops over time. These insights can be used to detect ineffective team dynamics and to generate interventions that promote better collaboration