AI for Leaders and Managers in Finance

Blended learning

U wie riicht sech d'Formatioun?

Open to all candidates

Erreechten Niveau

Mëttelstuf

Dauer

13,00 Woch(en)

Sprooch(e) vun der Déngschtleeschtung

EN

Nächst Sessioun

04.05.2026
Plaz
Online

Präis

100,00€

Virkenntnisser

No prerequisite

Ziler

  • Investigate the current landscape of AI technologies and their relevance to financial challenges.
  • Identify the strategic benefits of implementing AI-driven solutions in the finance sector.
  • Evaluate potential efficiency gains, cost reductions, and revenue growth opportunities through AI integration.
  • Analyze successful AI applications in optimizing financial processes and customer interactions.
  • Assess the ethical, legal, and societal implications of AI deployment in financial contexts.
  • Anticipate and mitigate risks associated with data privacy, algorithmic biases, and AI-driven decision-making in finance.
  • Explore how AI disrupts traditional financial models and shapes new market dynamics.
  • Develop strategies to leverage AI for innovation, competitiveness, and sustainable growth within the finance industry.

Inhalt

Week 1: Introduction to AI in Finance

  • Overview of course objectives and structure.
  • Importance of AI for finance leaders and managers.
  • Examination of current AI technologies and their impact on financial sectors.

Week 2: Strategic Applications of AI in Finance

  • Recognizing strategic advantages and opportunities of AI adoption in finance.
  • Case studies illustrating successful AI implementations in various financial domains.


Week 3: Economic Impact of AI in Finance

  • Evaluating efficiency gains, cost reductions, and revenue growth through AI.
  • Cost-benefit analysis of AI adoption in financial operations.
  • Exploration of economic models and forecasts related to AI-driven financial innovation.

Week 4: AI Applications in Financial Processes

  • Analysis of successful AI applications optimizing financial processes.
  • Understanding AI-driven automation and its impact on productivity and efficiency.
  • Practical exercises on identifying processes for AI integration in finance.


Week 5: AI and Customer Interactions in Finance

  • Examination of AI applications enhancing customer experiences and engagement in finance.
  • Case studies on AI-driven personalization and recommendation systems in financial services.


Week 6: Ethical and Societal Implications of AI in Finance

  • Assessment of ethical, legal, and societal implications of AI deployment in finance.
  • Addressing concerns related to data privacy, algorithmic biases, and transparency in financial AI.


Week 7: Mid-Term Exam

Week 8: Risk Management in AI Deployment in Finance

  • Anticipating and mitigating risks associated with AI deployment in finance.
  • Strategies for managing risks related to data security, compliance, and regulatory issues in financial AI.


Week 9: Disruption and Financial Models

  • Understanding how AI disrupts traditional financial models.
  • Case studies of industries undergoing transformation due to AI innovations in finance.
  • Strategic implications of AI-driven disruption and business model innovation in finance.


Week 10: Leveraging AI for Competitive Advantage in Finance

  • Developing strategies to leverage AI for innovation and competitiveness in finance.
  • Identifying opportunities for AI integration across different financial sectors.
  • Creating a roadmap for AI adoption and implementation in financial organizations.


Week 11: Emerging Trends in AI for Finance

  • Identifying emerging trends and opportunities for integrating AI into financial operations.
  • Exploring AI applications in marketing, sales, finance, and supply chain management in finance.
  • Creating a roadmap for AI-driven financial solutions in organizations.

Week 12: Cultivating an AI-Ready Culture in Finance

  • Fostering a culture of innovation and continuous learning to capitalize on AI opportunities in finance.
  • Developing leadership strategies for driving AI adoption and transformation in finance.
  • Final reflections and action planning for implementing AI strategies in participants' financial organizations.


Week 13: FINAL EXAM

Behandelt Punkten

By the end of the course, participants will be able to:

  • Demonstrate a comprehensive understanding of AI technologies in finance.
  • Develop strategic insights for effectively leveraging AI in financial management.
  • Evaluate the ethical and societal implications of AI deployment in finance.
  • Mitigate risks associated with AI implementation in financial settings.
  • Formulate actionable strategies for AI adoption and transformation in finance.

Pedagogesch Methoden

The online delivery blends synchronous and asynchronous components. Students complete self-directed assignments hosted on the course platform. Weekly Forums support a required active, rubric-based student contributions that foster collaboration.

Evaluatioun

Exams may include essays, short answers, or MCQs. Formats include open/closed book. Time zones are considered. Assessments are formative and summative. Students have 2 weeks to review grades and must follow the appeal process in the Student Handbook

Certificat, Diplom

Certificate

Nächst Sessioun

Datum
Stad
Sprooch & Präis
04.05.2026

07.08.2026
Online
EN 100,00€

Zousätzlech Informatiounen

Please note that there are three semesters as admission periods as noted on the academic calendar.
Scholarship Awarded (Total Fee: €100)

Registration is done online.

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