Bachelor in Insurance Data Analysis & Risk Management
Explore this Bachelor programme at FINCOM Paris, a business school in Paris connecting analytical skills with business decisions.
Programme overview
Study insurance operations, statistics and risk management through underwriting, claims and business datasets. Learn to recognise bias, assess evidence and use data lawfully to support operational decisions.
FINCOM Paris — Paris College of Commerce and Financial Management. Duration: 3 years. Programme reference: ESPAST-27-B.
The programme progresses from foundations and method exercises to staged projects, enabling students to define problems, select methods and explain findings.
Study pathways
Insurance data; business risk; fraud indicators and digital investigation. Pathways share a common core. Applicants indicate their preferred pathway; availability is confirmed before admission.
Learning outcomes
- Apply insurance fundamentals to products, underwriting and claims, documenting methods, evidence and the basis for professional judgement.
- Apply statistics to distributions, sampling and error, documenting methods, evidence and the basis for professional judgement.
- Apply data management to quality, permissions and records, documenting methods, evidence and the basis for professional judgement.
- Apply risk indicators to frequency, losses and trends, documenting methods, evidence and the basis for professional judgement.
- Apply indicator analysis to anomalies, relationships and verification, documenting methods, evidence and the basis for professional judgement.
- Apply responsible communication to reporting, fairness and accountability, documenting methods, evidence and the basis for professional judgement.
Learning content and competency modules
| Module | Learning content | Assessment evidence |
|---|---|---|
| Insurance fundamentals | Products, underwriting and claims. Guided exercises and applied cases. | Problem definition and analytical report |
| Statistics | Distributions, sampling and error. Guided exercises and applied cases. | Reproducible case or model file |
| Data management | Quality, permissions and records. Guided exercises and applied cases. | Data and operational records with quality checks |
| Risk indicators | Frequency, losses and trends. Guided exercises and applied cases. | Option comparison and testing evidence |
| Indicator analysis | Anomalies, relationships and verification. Guided exercises and applied cases. | Project plan and collaboration records |
| Responsible communication | Reporting, fairness and accountability. Guided exercises and applied cases. | Final report and oral defence |
Study progression
- Year 1: build foundations in insurance fundamentals and statistics, with guided cases, academic reading and communication.
- Year 2: develop data management and risk indicators through pathway projects and evaluation of methods and results.
- Year 3: integrate indicator analysis and responsible communication in a final project, documented portfolio and oral defence.
Projects and practice
Analyse simulated insurance data to develop risk indicators, verify anomalies and recommend improvements.
Project submissions document the problem, process, individual contribution, validation evidence and limitations. Individual contributions are assessed within team projects. Placements and external projects depend on actual arrangements; no particular employer or placement is guaranteed.
Teaching and assessment
Business cases, data laboratories, scenario analysis and team projects use public, simulated or authorised information. Cases may draw on finance, insurance, industry, telecommunications and healthcare.
Module assignments, cases, practical work, staged projects and the final oral defence assess conceptual accuracy, methods, evidence quality, reliability, communication and reflection. Weightings, reassessment and extension rules are published before teaching begins. Moodle can support resources, assignments, quizzes and feedback; practical activities are recorded and assessed separately.
Entry requirements
Provide evidence of secondary education or equivalent study. A foundation in mathematics, logical reasoning and written communication is recommended. Business, data or social-research projects may support the application.
Submit education evidence, transcripts, a CV, a motivation statement and any required project portfolio. Teaching languages, language evidence and translation requirements are specified in the intake announcement. Website language options do not determine teaching language.
Diploma
Complete the required modules, assessments, final project and applicable academic requirements to receive an institution-issued diploma.
Career development
Insurance data assistant; risk management assistant; claims analysis assistant. Employment depends on skills, experience and employer requirements; regulated roles also require applicable qualifications and authorisations. Further study is subject to the receiving institution’s admission requirements.
Tuition and fees
Contact admissions for the tuition and payment terms applicable to your chosen programme before enrolment.
Applications
Select this programme and study level when submitting a preliminary application. The admissions office reviews materials and contacts the applicant. A preliminary application is not an admission decision or a payment commitment.
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