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Admissions

Applications for the 2027 cohort open on 1 October 2026. Complete applications should be submitted online by 30 November 2026. Applications are assessed on a rolling basis starting October 15, and applicants will normally receive a decision within three weeks. All admission decisions will be communicated no later than 15 December 2026.

Places are limited to a maximum of 25 participants. Early application is therefore encouraged, and the application window may close once the cohort is full. Only complete application dossiers can be assessed.

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Who should apply

The CAS is designed for professionals who want to combine rigorous quantitative risk analysis with modern data science and artificial intelligence. It is particularly relevant for participants working in insurance, reinsurance, banking, asset management, consulting, regulation, technology, actuarial functions, quantitative risk management, analytics, or related fields.

Admission requirements

• Academic background. A university degree acknowledged by UZH, or an equivalent qualification. Relevant fields include mathematics, statistics, actuarial science, physics, engineering, computer science, data science, economics, finance, or another quantitatively oriented discipline.
• Professional experience. At least two years of relevant professional experience in finance, banking, insurance, risk management, actuarial work, quantitative analytics, data science, consulting, regulation, or a closely related field.
• Quantitative preparation. Applicants should be comfortable with quantitative reasoning and should have a working foundation in probability, statistics, and mathematical notation appropriate for an advanced continuing-education programme.
• Coding readiness. Basic programming experience is required, preferably in Python or a comparable language. Participants must be able to read, run, and adapt basic code examples. Applicants who otherwise meet the admission criteria but need to consolidate their Python skills may be admitted subject to successful completion of the CAS pre-course or another approved preparation route.
• English proficiency. A good command of English is required because teaching, course materials, group work, and assessments are conducted in English. Formal evidence may be requested where the application dossier does not otherwise demonstrate sufficient proficiency.
• Availability and participation. Applicants must be able to attend the scheduled teaching days, complete preparation and assessment tasks, and devote the required time to the capstone project.

Admission on the basis of equivalent qualifications

In exceptional cases, applicants who do not meet the standard academic requirement may be considered on the basis of comparable qualifications and substantial relevant professional experience. The Programme Committee may request an interview or additional evidence. Admission is selective, and there is no general entitlement to a place.

Required documents

Applicants should submit the following documents through the online application portal. Documents should be provided in PDF format unless the portal specifies otherwise.

• Completed online application form. All mandatory fields must be completed, including education and employment history.
• Curriculum vitae. A concise CV, preferably no longer than two or three pages, highlighting education, relevant professional responsibilities, quantitative methods, data or AI experience, programming skills, and continuing education.
• Motivation letter. Maximum one page. The letter should explain the applicant's reasons for applying, learning objectives, relevance of the programme to current or future responsibilities, and readiness for the programme's quantitative and coding workload.
• Degree certificate and transcript. Diploma certificate and transcript of records for the highest or most relevant university degree.
• Evidence of relevant professional experience. Employment certificates, an employer confirmation, or comparable documentation sufficient to verify the required professional experience.