Zurück zur Auswahl
Semester | Frühjahrsemester 2025 |
Angebotsmuster | Jedes Frühjahrsem. |
Dozierende | Kumar Rishabh (kumar.rishabh@unibas.ch, BeurteilerIn) |
Inhalt | This course examines the major functions of modern banking—maturity transformation, risk management, and addressing information asymmetries and contract complexities—while analyzing how these are being reshaped by fintech innovations. We will explore the role of big data, artificial intelligence, machine learning, and blockchain in driving technological change, challenging traditional banking practices, and transforming the financial landscape. Separately, we will analyze the vulnerabilities and resilience of financial institutions through detailed case studies, including the 2023 U.S. banking crisis and the collapse of Credit Suisse. The course will examine these events in-depth, focusing on their underlying causes and lessons for the future of financial stability. This course combines theory with practical application. Students will study banking theories rooted in contract theory and gain a jargon-free introduction to machine learning techniques, with hands-on experience in applying these tools to banking and fintech datasets. Access to curated datasets will enable students to analyze real-world problems and develop practical insights for their essays. The course includes lectures by the instructor and guest sessions by policymakers and industry experts. Previous guest lecturers have included representatives from FINMA, BIS, Raiffeisen Bank, Basler Kantonal Bank, Aperture, and Hypothekarbank Lenzburg, providing practical insights into the intersection of regulation, banking, and technology. No prior background is required, but active engagement and curiosity are essential. This course equips students with the skills to critically analyze the forces shaping modern finance and apply their knowledge to real-world challenges. |
Lernziele | Upon completion of this course, students can expect to have gained: -- Understand the evolution of the banking sector and its current challenges. -- Analyze the disruptive impact of fintech and big tech on financial services-- . -- Apply theoretical concepts from asymmetric information and contract theory to banking. -- Develop practical skills in using econometric tools and machine learning tools with real-world financial and banking data. |
Literatur | The course is based on original papers. All the papers will be available on ADAM. Take a look at the course syllabus on ADAM for the list of papers covered in each lecture session. |
Bemerkungen | This course is also open to MIME students from the University of Bern and actively encourages collaboration between students from both the University of Bern and the University of Basel. |
Weblink | Weblink: Course Syllabus |
Teilnahmevoraussetzungen | This course has no formal prerequisites, as advanced topics will be developed from the ground up, making it accessible to all interested students. While prior knowledge in microeconomics and econometrics may be beneficial, it is not a requirement. We particularly encourage motivated students, even those without prior background in these areas, to join. Engaging actively in discussions, asking questions, and utilizing opportunities to meet with instructors can greatly enhance the learning experience for all students, regardless of their initial level of familiarity with the subject matter. |
Anmeldung zur Lehrveranstaltung | Registration: Please enroll in the Online Services (services.unibas.ch); Eucor-Students and mobility students of other Swiss Universities or the FHNW first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html After successful registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! |
Unterrichtssprache | Englisch |
Einsatz digitaler Medien | kein spezifischer Einsatz |
Intervall | Wochentag | Zeit | Raum |
---|---|---|---|
wöchentlich | Dienstag | 12.30-15.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S14 HG.32 |
Module |
Modul: Ausgewählte Themen aus Ökonomie und Rechtswissenschaft (Masterstudium: Actuarial Science) Modul: Field Electives in Finance and Money (Masterstudium: Finance and Money) Modul: Specific Electives in Economics (Masterstudium: Wirtschaftswissenschaften) Modul: Specific Electives in Finance, Controlling, Banking (Masterstudium: Wirtschaftswissenschaften) Spezialisierungsmodul: Areas of Specialization in International and/or Monetary Economics (Masterstudium: International and Monetary Economics) |
Prüfung | Leistungsnachweis |
Hinweise zur Prüfung | 1. Solo or Group Presentations (40%) The format—solo or group—will depend on the class size. The instructor will finalize the format at least two weeks before the first presentation to ensure clear guidelines. Students will present individually or in groups of 2-3, delivering 20-minute presentations on selected papers or case studies. Each student may present up to three times during the semester, with the best two contributing to the final grade. Whether solo or group, presentations will receive individual written feedback from the instructor. 2. Essay (50%) Students will write an essay on a provided topic or one of their choice (subject to approval). Essays can be completed individually or in group, with group submissions encouraged. 3. Class Participation (10%) Active engagement in class discussions will be assessed and contribute to the final grade. |
An-/Abmeldung zur Prüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
Wiederholungsprüfung | keine Wiederholungsprüfung |
Skala | 1-6 0,1 |
Belegen bei Nichtbestehen | beliebig wiederholbar |
Zuständige Fakultät | Wirtschaftswissenschaftliche Fakultät / WWZ, studiendekanat-wwz@unibas.ch |
Anbietende Organisationseinheit | Wirtschaftswissenschaftliche Fakultät / WWZ |