The MSc is deliberately positioned at the intersection of Finance + Banking + Technology + AI + Data + Digital Strategy.

Core Academic Domains

Financial Technology

• FinTech business models
• Digital financial services
• Financial platforms
• Embedded finance
• Banking-as-a-Service

Artificial Intelligence

• Machine learning
• Deep learning
• Generative AI
• Natural language processing
• Predictive analytics
• AI agents and intelligent systems

Digital Banking

• Digital banking strategy
• Mobile banking
• Digital payments
• Open banking
• Digital wallets
• Customer experience
• Digital transformation

Financial Data

• Financial databases
• Python
• SQL
• Data engineering
• Data visualization
• Financial analytics
• Predictive modelling

Financial Security

• Cybersecurity
• Fraud analytics
• AML analytics
• Identity management
• Data protection
• Model security

Digital Regulation

• RegTech
• SupTech
• AI governance
• Digital financial regulation
• Technology risk
• Responsible AI

The programme aims to produce graduates who can:

1. Analyse the transformation of financial services through technology.
2. Design and evaluate digital banking strategies.
3. Apply AI and machine learning to financial problems.
4. Develop data-driven financial solutions.
5. Analyse FinTech business models.
6. Apply programming and data-engineering techniques.
7. Develop predictive financial models.
8. Evaluate generative AI applications in financial services.
9. Design digital-payment and open-banking solutions.
10. Evaluate blockchain and digital-asset applications.
11. Apply cybersecurity principles to digital financial institutions.
12. Develop AI-assisted fraud and AML solutions.
13. Analyse digital financial risks.
14. Evaluate RegTech and SupTech solutions.
15. Apply responsible-AI principles to financial applications.
16. Manage technology-driven financial transformation.
17. Conduct advanced research in FinTech and digital banking.
18. Lead multidisciplinary technology and financial-services teams.

4.1 Academic Entry

Applicants should normally hold a recognized bachelor’s degree or equivalent qualification.

Preferred backgrounds include:

• Finance
• Banking
• Accounting
• Economics
• Business Administration
• Computer Science
• Information Technology
• Information Systems
• Data Science
• Mathematics
• Statistics
• Engineering
• FinTech
• Digital Business
• Economics and Finance

Applicants from other disciplines may be considered where they demonstrate relevant professional experience or appropriate preparation.

4.2 International Qualifications

IBSF may consider recognized bachelor’s qualifications from:

• Africa
• Europe
• United Kingdom
• United States
• Canada
• Australia
• New Zealand
• Asia
• Gulf/MENA
• Other recognized higher-education systems

Qualifications may be subject to applicable recognition/equivalency procedures.

4.3 Professional Experience

Professional experience is advantageous in:

• Banking
• FinTech
• IT
• Digital banking
• Financial technology
• Software development
• Data analytics
• Cybersecurity
• Financial services
• Accounting
• Investment
• Payments
• Telecommunications
• Consulting
• Financial regulation

4.4 Technical Preparation

Applicants should ideally have basic competence in:

• Mathematics
• Statistics
• Computer applications
• Data analysis

Applicants without programming experience may be admitted where they can complete an appropriate preparatory/bridging module.

4.5 English Language

Where previous higher education was not conducted in English, applicants may be required to demonstrate appropriate English proficiency through an accepted qualification or institutional assessment.

The MSc consists of 90 ECTS over three semesters.

Each semester carries 30 ECTS:

24 ECTS compulsory + 6 ECTS optional = 30 ECTS

The programme progressively moves from:

Financial Technology Foundations → AI & Data → Digital Banking → Advanced AI → Financial Innovation → Strategic Transformation → Applied Research

SEMESTER 1

FINTECH, DIGITAL BANKING & AI FOUNDATIONS

Compulsory Modules

Choose ONE Optional Module

Semester 1 Focus

Students establish the technological and financial foundations necessary for advanced digital-finance study.

Key areas include:

• FinTech ecosystem
• Digital banking
• Python/programming
• SQL
• Financial datasets
• Data engineering
• Machine learning
• Financial analytics
• Digital transformation

The curriculum’s computing foundation is consistent with current ABET expectations around advanced computing, mathematics/statistics, privacy/security and application-oriented projects.

SEMESTER 2

ADVANCED AI, DIGITAL FINANCE & FINANCIAL SECURITY

Compulsory Modules

Choose ONE Optional Module

Semester 2 Focus

The second semester moves students into advanced applications of AI and technology.

Students study:

• Deep learning
• Neural networks
• NLP
• Generative AI
• AI agents
• Predictive modelling
• Digital payments
• Open banking
• Embedded finance
• Fraud detection
• AML analytics
• Cybersecurity
• Cloud infrastructure

SEMESTER 3

INTELLIGENT BANKING, REGTECH & DIGITAL FINANCIAL TRANSFORMATION

Compulsory Modules

Choose ONE Optional Specialisation Module

Semester 3 Credit Structure

To maintain the programme at exactly 90 ECTS, the 6-ECTS specialization module should replace one of the 6-ECTS taught compulsory modules rather than being added to the 30-ECTS semester.

The recommended institutional structure is therefore:

18 ECTS core + 6 ECTS specialization + 6 ECTS research preparation = 30 ECTS, with the dissertation formally embedded within the programme’s research component.

The AI component progresses from fundamentals to advanced financial applications.

AI Foundations

• AI concepts
• Intelligent systems
• Search and reasoning
• Knowledge representation
• Data preparation
• Programming
• Algorithms

Machine Learning

• Supervised learning
• Unsupervised learning
• Classification
• Regression
• Clustering
• Feature engineering
• Model evaluation

Deep Learning

• Neural networks
• CNNs
• RNNs
• Transformers
• Representation learning
• Model optimization

Generative AI

• Large language models
• Prompt engineering
• Retrieval-augmented generation
• AI agents
• Financial document analysis
• AI assistants
• Generative AI risk

Financial AI

Applications include:

• Credit scoring
• Loan-default prediction
• Fraud detection
• AML monitoring
• Customer segmentation
• Financial forecasting
• Algorithmic investment
• Risk modelling
• Financial sentiment analysis
• Automated financial reporting

The AI curriculum is deliberately structured around areas identified in the current ABET AI proposal, including AI foundations, programming, data/knowledge engineering, machine learning and deep learning, AI solution design, AI infrastructure and responsible AI.

Students develop advanced understanding of:

• Digital banking models
• Mobile banking
• Digital-only banks
• Banking-as-a-Service
• Open banking
• Embedded finance
• Digital wallets
• Payment platforms
• Digital onboarding
• Digital identity
• Personalization
• Automated banking
• Omnichannel banking
• Digital transformation

Students analyse:

• FinTech business models
• FinTech platforms
• Payments FinTech
• Lending FinTech
• InsurTech
• WealthTech
• RegTech
• PayTech
• Islamic FinTech
• Blockchain FinTech
• Embedded finance
• Financial marketplaces

Students also evaluate the strategic relationship between:

Banks + FinTechs + Technology Companies + Regulators + Customers

Students develop technical competence in:

• Python
• SQL
• Data cleaning
• Data preparation
• Data engineering
• Data visualization
• Statistical modelling
• Predictive analytics
• Financial databases
• Data pipelines
• Dashboard development
• Model evaluation

The current ABET data-science criteria emphasize the full data lifecycle—from acquisition and management through preparation, analysis, modelling/deployment and visualization—alongside ethics, governance, privacy, security, mathematics and statistics

Topics include:

• Payment systems
• Card payments
• Mobile payments
• Instant payments
• Digital wallets
• Account-to-account payments
• Open APIs
• Open banking
• Banking-as-a-Service
• Embedded payments
• Cross-border payments
• Payment fraud
• Payment security

Students examine:

• Distributed ledger technology
• Blockchain architecture
• Smart contracts
• Tokenization
• Stablecoins
• Digital assets
• Digital securities
• Decentralized finance
• Blockchain payments
• Custody
• Digital-asset regulation
• Blockchain risk

The emphasis should be on financial applications, architecture, governance and risk, rather than speculative trading.

Students examine:

• Cyber threats to banks
• Security architecture
• Identity and access management
• Encryption
• Cloud security
• API security
• Payment security
• Data protection
• Cyber risk
• Incident response
• Third-party technology risk
• AI security
• Digital fraud

Privacy and security are explicit components of current ABET master’s-level computing criteria.

A specialist component of the programme can cover:

• Fraud analytics
• Transaction monitoring
• Anomaly detection
• Customer risk scoring
• AML analytics
• Network analysis
• Behavioral analytics
• Sanctions screening
• Suspicious transaction detection
• Explainable AI
• False-positive reduction

Students can develop practical projects using synthetic or appropriately governed datasets.

Students study:

• AI ethics
• Algorithmic bias
• Explainability
• Transparency
• Accountability
• Human oversight
• Model risk
• AI security
• Data governance
• Privacy
• AI regulation
• Responsible AI
• AI audit
• AI impact assessment

The programme should establish responsible AI as a core competency rather than treating ethics as a standalone elective.

RegTech

Students explore technology used by financial institutions for:

• Regulatory reporting
• AML/KYC
• Compliance monitoring
• Risk management
• Transaction monitoring
• Regulatory data management

SupTech

Students examine how regulators and central banks can use technology for:

• Supervisory analytics
• Automated reporting
• Risk monitoring
• Financial-stability surveillance
• Fraud detection
• Regulatory intelligence
• AI-assisted supervision

The CBDC component covers:

• CBDC architecture
• Retail CBDCs
• Wholesale CBDCs
• Digital monetary systems
• Payment infrastructure
• Central-bank technology
• Privacy
• Cybersecurity
• Programmability
• Monetary-policy implications
• Financial stability
• Cross-border CBDCs
• Interoperability

This can become a particularly strong specialization for students targeting central banks and financial regulators.

Students choosing the entrepreneurship pathway can develop:

• FinTech business models
• Digital financial products
• Product-market fit
• Financial-platform strategy
• Venture financing
• Regulatory considerations
• Technology development
• Customer acquisition
• Financial inclusion
• FinTech scaling

Students can develop a complete FinTech venture/business-plan project as part of their applied work.

The programme should use a strongly applied technology + finance learning model.

Teaching Methods

• Advanced lectures
• Coding laboratories
• AI laboratories
• Financial technology laboratories
• Data science projects
• Digital banking simulations
• Case studies
• Hackathons
• FinTech product development
• AI model development
• Cybersecurity exercises
• Blockchain laboratories
• Financial-data projects
• Industry consulting projects
• Research seminars
• Executive guest lectures
• Technology demonstrations
• Group projects
• Independent research

The laboratory could provide an integrated environment for:

AI

• Machine learning
• Deep learning
• Generative AI
• NLP
• AI agents

Finance

• Financial datasets
• Market analytics
• Credit analytics
• Risk modelling
• Investment analytics

Banking

• Digital banking simulations
• Payment systems
• Open banking APIs
• Banking workflows

Technology

• Python
• SQL
• Cloud computing
• Blockchain
• APIs
• Data visualization

Security

• Cybersecurity exercises
• Fraud analytics
• AML simulations
• Identity management

Regulation

• RegTech
• SupTech
• AI governance
• Regulatory analytics

This laboratory would give IBSF a strong differentiator from conventional MBA/Finance programmes.

An indicative assessment model is:

Assessment should evaluate both technical capability and financial-sector application.

The programme should maintain an Assurance of Learning process in which student outcomes are measured systematically and assessment results are used for continuous programme improvement, consistent with current ABET expectations.

The final 12-ECTS research/project component should require students to solve a significant financial-services problem using technology, data and/or AI.

Potential Research Topics

• AI in credit scoring
• AI-powered fraud detection
• Generative AI in banking
• Digital banking adoption
• Open banking
• Digital payments
• CBDCs
• FinTech financial inclusion
• Blockchain payments
• Digital identity
• AI governance
• Algorithmic bias in financial services
• RegTech
• SupTech
• AI-powered investment
• Digital wealth management
• Cybersecurity in digital banking
• FinTech regulation
• AI in AML
• Predictive banking analytics

Applied Project Examples

Students could develop:

• AI-powered credit-scoring prototype
• Fraud detection model
• AML analytics platform
• Digital banking strategy
• FinTech payment solution
• AI banking chatbot
• Financial forecasting system
• Digital-wallet business model
• Robo-advisory prototype
• AI investment research assistant
• Regulatory analytics dashboard
• SupTech risk-monitoring system
• CBDC implementation framework
• Digital identity solution

The MSc is designed for specialist, managerial, strategic and entrepreneurial roles across financial technology and banking.

FINTECH

Potential positions include:

• FinTech Manager
• FinTech Business Analyst
• FinTech Product Manager
• FinTech Strategy Consultant
• Financial Innovation Manager
• FinTech Solutions Specialist
• Digital Finance Consultant

ARTIFICIAL INTELLIGENCE

Potential roles include:

• Financial AI Analyst
• AI Business Analyst
• AI Product Manager
• AI Implementation Specialist
• AI Solutions Consultant
• Machine Learning Analyst
• AI Governance Specialist
• AI Risk Analyst
• Financial Data Scientist*

*Subject to the candidate’s technical experience and/or further specialization.

DIGITAL BANKING

Potential roles include:

• Digital Banking Manager
• Digital Transformation Manager
• Digital Banking Analyst
• Digital Product Manager
• Digital Finance Strategist
• Banking Technology Analyst
• Digital Payments Manager
• Open Banking Specialist

DATA & ANALYTICS

Potential roles include:

• Financial Data Analyst
• Banking Data Analyst
• Data Analytics Manager
• Business Intelligence Analyst
• Financial Data Scientist
• Risk Analytics Analyst
• Customer Analytics Specialist
• Predictive Analytics Analyst

RISK, FRAUD & CYBERSECURITY

Potential roles include:

• Financial Crime Analytics Manager
• Fraud Analytics Specialist
• AML Analytics Specialist
• Cyber Risk Analyst
• Technology Risk Manager
• Model Risk Analyst
• AI Risk Specialist
• Digital Financial Crime Analyst

REGTECH & SUPTECH

Potential roles include:

• RegTech Manager
• Regulatory Technology Specialist
• Regulatory Data Analyst
• SupTech Specialist
• Digital Regulation Analyst
• Financial Technology Compliance Specialist
• AI Governance Analyst

CENTRAL BANKING & DIGITAL MONETARY SYSTEMS

The programme is particularly relevant to:

• Central banks
• Financial regulators
• Ministries of finance
• Payment-system regulators
• Financial-stability institutions

Potential positions include:

• CBDC Analyst
• Digital Currency Specialist
• FinTech Policy Analyst
• Payment Systems Analyst
• Digital Financial Regulation Specialist
• SupTech Analyst
• Financial Innovation Specialist
• Digital Finance Policy Specialist

Graduates may work in:

Commercial Banks | Digital Banks | Central Banks | FinTech Companies | Payment Companies | Mobile Money Providers | Investment Firms | Insurance Companies | Pension Funds | Financial Regulators | Technology Companies | Cybersecurity Firms | Consulting Firms | Big-Data Companies | AI Companies | Development Finance Institutions | International Financial Institutions

Degree Award: Master of Science (MSc)
Duration: 18 Months
Structure: 3 Semesters
Total Academic Load: 90 ECTS
Mode of Study: Full-Time | Blended | Online | Face-to-Face
Language: English
Level: Postgraduate / Second Cycle
Programme Tagline