Insegnamento a.a. 2025-2026

The course provides an overview of advanced statistical and computational approaches that are increasingly relevant for research and practice in accounting and related fields. The content is organized into interconnected macro-topics, each illustrating both methodological perspectives and applied insights:

• Foundations and Emerging Infrastructures

Introduction to the course and discussion of how accounting systems are evolving, with particular attention to blockchain technologies and their implications for information flows and assurance.

• Statistical Modeling for Complex Data

Exploration of methods designed to capture heterogeneity, time dependence, and uncertainty in data. This includes finite mixture models, survival analysis, multi-state models, and robust statistical approaches based on depth measures.

• Functional and High-Dimensional Data Analysis

Introduction to functional data analysis techniques for structured, continuous data, as well as methods for dimensionality reduction and entropy balancing to handle large and complex datasets.

• Textual and Unstructured Data Analytics

Overview of natural language processing methods and their applications in accounting, ranging from extracting meaning from disclosures to detecting patterns in textual communication.

• Applications in Accounting and Auditing

Case studies and applied sessions focusing on fraud prediction, audit analytics, and event studies. These modules show how statistical and computational techniques can be used to address real-world challenges in assurance and decision-making.

• Collaborative Learning and Integration

Tutorships, in-class group projects, and final presentations encourage students to integrate different methods, reflect on their potential use cases, and develop transferable skills for future academic or professional applications.

• Knowledge and Understanding:

Acquire familiarity with a set of advanced statistical and computational approaches, understanding their purpose and potential role in business and accounting contexts.

• Applying Knowledge and Understanding:

Apply selected techniques at a basic level to illustrative problems and case studies, gaining an appreciation of how these tools can support analysis and decision-making.

• Learning Skills:

Develop the ability to recognize when and how these methods could be revisited and further explored in future academic or professional paths.

• Apply basic versions of advanced statistical and computational techniques to illustrative problems in accounting and business.
• Interpret results of these applications and connect them to practical questions in data-driven decision-making.
• Work collaboratively in groups to structure a problem, apply methods, and present findings in a clear and accessible way

• Lectures
• Collaborative Works / Assignments

Collaborative Works / Assignments

Students will work in small groups on a project that requires them to apply selected methods introduced in class to a specific problem or dataset. The assignment culminates in both a written report and a class presentation. In the report, groups are expected to document the steps of their analysis, explain the design choices made, and reflect on the challenges encountered. The presentation allows students to communicate their findings clearly and effectively, highlighting how their approach addresses the problem at hand. Through this collaborative work, students will strengthen their ability to structure a project, divide tasks, and synthesize results into a coherent output.

Student performance will be assessed through a combination of a collaborative group project and a written exam.

• Collaborative Works / Assignments (Project and Presentation)

Students work in groups to apply selected methods to a practical problem, culminating in a written report and a presentation. This component verifies the ability to apply knowledge in practice, make design choices, work collaboratively, and communicate findings effectively. It directly assesses the Intended Learning Outcomes related to Applying Knowledge and Understanding, Making Judgments, and Communication Skills.

• Written Exam

The exam consists of structured questions aimed at verifying the acquisition of knowledge and understanding of the main methods discussed in the course. It allows evaluation of the student’s ability to recall, explain, and critically frame the techniques, thereby linking to the Intended Learning Outcomes related to Knowledge and Understanding.

Assessment criteria and weighting scheme
The same assessment criteria apply to both attending and non-attending students. The relative weights of the project and the written exam in the determination of the final grade are as follows:

• 80% project, 20% written exam

Slides and handouts will be provided by the instructors.