The programme aims to develop graduates who can:

1. Understand modern banking systems and financial institutions.
2. Analyse the transformation of banking through FinTech.
3. Apply programming and computational thinking to financial problems.
4. Analyse financial and customer datasets.
5. Develop basic AI and machine-learning solutions.
6. Apply AI to credit, fraud, risk and customer analytics.
7. Understand digital payments and financial platforms.
8. Evaluate blockchain and digital-asset applications.
9. Understand cybersecurity risks in digital financial services.
10. Apply responsible-AI principles.
11. Understand AI governance and financial regulation.
12. Develop and evaluate FinTech products.
13. Apply data-driven approaches to banking decisions.
14. Communicate financial and technical information effectively.
15. Conduct independent research and applied AI/FinTech projects.

3.1 Standard Undergraduate Entry

Applicants should normally have:

• A recognised senior secondary/high-school qualification.
• A qualification that permits admission to undergraduate university study in the applicant’s country.
• Mathematics or a quantitative subject is strongly recommended.
• Basic computer literacy is recommended.
• English-language proficiency.

Because of the programme’s quantitative and technological nature, applicants with mathematics, computer science, IT, economics, finance, accounting, business or science backgrounds may be particularly well prepared.

3.2 International Qualifications

IBSF may consider equivalent qualifications including:

• International Baccalaureate (IB)
• GCE A-Levels
• US High School Diploma
• Canadian Secondary School Diploma
• Australian Senior Secondary Certificate
• European secondary-school qualifications
• National secondary-school certificates
• Recognised international foundation programmes
• Other qualifications recognised for undergraduate admission

3.3 Diploma and Advanced Standing

Applicants holding relevant recognised diplomas may apply for credit transfer/advanced standing, subject to curriculum mapping and IBSF academic regulations.

Relevant areas include:

• Banking
• Finance
• FinTech
• Computer Science
• Information Technology
• Data Science
• Business Administration
• Accounting
• Economics
• Statistics

3.4 Mature-Age Admission

IBSF may provide alternative admission routes through:

• Mature-age entry
• Recognition of Prior Learning
• Relevant professional experience
• Entrance assessment
• Interview

subject to applicable regulatory requirements.

3.5 English Language

Applicants whose prior education was not conducted in English may be required to demonstrate English-language proficiency through an accepted test or qualification.

The programme uses a 24 ECTS compulsory + 6 ECTS optional model in each semester.

The programme progresses from:

Banking Foundations → Computing → Digital Finance → FinTech → Data Analytics → Machine Learning → AI Applications → Advanced AI → AI Governance → Financial Innovation → Professional Practice.

SEMESTER 1

Foundations of Banking, Finance & Digital Technology

Compulsory Modules — 24 ECTS

Optional Modules — Choose ONE — 6 ECTS

Semester Total: 30 ECTS

Key content

Principles of Banking & Financial Services

• Banking systems
• Commercial banking
• Retail banking
• Corporate banking
• Central banking
• Financial intermediation
• Banking products

Principles of Finance & Economics

• Microeconomics
• Macroeconomics
• Financial markets
• Interest rates
• Inflation
• Risk and return
• Financial decision-making

Computing & Programming Fundamentals

• Computer systems
• Algorithms
• Computational thinking
• Programming concepts
• Variables
• Functions
• Control structures
• Introduction to Python
• Basic financial programming

SEMESTER 2

Digital Banking, Accounting & Data Foundations

Compulsory Modules — 24 ECTS

Optional Modules — Choose ONE — 6 ECTS

Semester Total: 30 ECTS

Key content

Banking Operations

• Deposits
• Lending
• Payments
• Cards
• ATMs
• Branch operations
• Customer service
• Digital banking

Statistics & Data Analysis

• Probability
• Descriptive statistics
• Correlation
• Regression
• Sampling
• Data visualisation
• Financial datasets

Database Systems & SQL

• Database concepts
• Relational databases
• Data modelling
• SQL
• Data queries
• Data integrity
• Database security

SEMESTER 3

FinTech, Programming & Financial Analytics

Compulsory Modules — 24 ECTS

Optional Modules — Choose ONE — 6 ECTS

Semester Total: 30 ECTS

Key content

FinTech & Digital Financial Services

• FinTech ecosystem
• Digital banking
• Open banking
• Embedded finance
• Banking-as-a-Service
• Digital payments
• RegTech
• SupTech
• Digital identity

Python Programming for Finance

• Python
• NumPy
• Pandas
• Data manipulation
• APIs
• Financial calculations
• Automated reporting

Financial Data Analytics

• Data acquisition
• Data cleaning
• Exploratory analysis
• Data visualisation
• Forecasting
• Customer analytics
• Risk analytics

Financial Markets & Investment

• Equity markets
• Bond markets
• Foreign exchange
• Money markets
• Investment instruments
• Market risk

SEMESTER 4

Artificial Intelligence, Risk & Digital Financial Security

Compulsory Modules — 24 ECTS

Optional Modules — Choose ONE — 6 ECTS

Semester Total: 30 ECTS

Key content

Artificial Intelligence & Machine Learning

• AI foundations
• Machine learning
• Supervised learning
• Unsupervised learning
• Classification
• Regression
• Clustering
• Feature engineering
• Model evaluation

AI Applications in Banking

• AI credit scoring
• Loan-default prediction
• Fraud detection
• Customer segmentation
• AML analytics
• Chatbots
• Personalisation
• Automated decision support

Financial Risk Management & Analytics

• Credit risk
• Market risk
• Liquidity risk
• Operational risk
• Model risk
• Stress testing
• Scenario analysis
• Predictive risk modelling

Cybersecurity & Digital Financial Crime

• Cyber threats
• Identity theft
• Financial fraud
• Malware
• Phishing
• AML
• Security controls
• Incident response
• Digital financial crime

SEMESTER 5

Advanced AI & Digital Financial Transformation

Compulsory Modules — 24 ECTS

Optional Modules — Choose ONE — 6 ECTS

Semester Total: 30 ECTS

Key content

Advanced Machine Learning

• Neural networks
• Ensemble learning
• Time-series forecasting
• Anomaly detection
• Model optimisation
• Model validation
• Predictive modelling

Generative AI & Intelligent Financial Systems

• Large language models
• Generative AI
• Prompt engineering
• Retrieval-augmented generation
• AI agents
• Financial research assistants
• Automated financial reporting
• Intelligent customer service

Digital Banking Strategy

• Digital transformation
• Platform banking
• Open banking
• Banking-as-a-Service
• APIs
• Digital products
• Customer experience
• Innovation strategy

AI Governance, Ethics & Regulation

• Responsible AI
• AI accountability
• Explainability
• Transparency
• Bias
• Privacy
• Data governance
• Model governance
• Human oversight

NIST’s AI Risk Management Framework provides an internationally recognised reference for managing AI risks and promoting trustworthy AI, including consideration of security, privacy, transparency, accountability and related risks.

SEMESTER 6

Strategic FinTech, AI Decision-Making & Professional Practice

Compulsory Modules — 24 ECTS

Optional Modules — Choose ONE — 6 ECTS

Semester Total: 30 ECTS

The final-year project should integrate banking + finance + FinTech + AI.

Students should ideally develop or evaluate a practical solution such as:

• AI-powered credit-scoring system
• Fraud-detection model
• AML transaction-monitoring system
• AI banking chatbot
• Loan-default prediction model
• Financial forecasting platform
• Robo-advisory prototype
• AI financial-risk dashboard
• Digital banking platform
• FinTech payment solution
• AI investment research assistant
• Central-bank analytics platform
• AI-powered financial-inclusion solution

Project requirements

Students should demonstrate:

1. Problem identification
2. Financial-sector analysis
3. Data requirements
4. Technology selection
5. System/model design
6. Implementation
7. Testing
8. Validation
9. Risk assessment
10. Ethical and regulatory analysis
11. Results
12. Recommendations

This major applied project is particularly important for international comparability because current computing accreditation criteria emphasise integration of knowledge through major projects and measurable graduate outcomes.

Students should complete supervised professional practice with organisations such as:

• Commercial banks
• Central banks
• Digital banks
• FinTech companies
• Payment companies
• Mobile-money operators
• Investment firms
• Insurance companies
• Financial regulators
• Technology companies
• Cybersecurity companies
• Data-analytics companies
• Consulting firms
• Development-finance institutions

Possible internship roles

• FinTech Analyst
• Digital Banking Analyst
• Financial Data Analyst
• Banking Technology Analyst
• Junior AI Analyst
• Business Intelligence Analyst
• Risk Analytics Assistant
• Cyber Risk Analyst
• Financial Systems Analyst
• Digital Transformation Analyst

Upon completion, graduates should be able to:

Banking

• Explain commercial and central banking.
• Analyse banking products and services.
• Evaluate digital banking models.
• Analyse banking risks.
• Understand financial regulation.

FinTech

• Evaluate FinTech business models.
• Explain digital-payment ecosystems.
• Analyse open banking.
• Understand embedded finance.
• Evaluate blockchain applications.
• Understand RegTech and SupTech.

Artificial Intelligence

• Explain AI concepts.
• Develop basic machine-learning models.
• Prepare and analyse financial datasets.
• Apply predictive analytics.
• Evaluate AI models.
• Apply generative AI responsibly.
• Understand AI governance.

Financial Analytics

Graduates should be able to:

• Analyse financial datasets.
• Develop dashboards.
• Conduct predictive analysis.
• Build credit-risk models.
• Analyse fraud patterns.
• Conduct customer analytics.
• Forecast financial variables.

Responsible Technology

Students should understand:

• Algorithmic bias
• Explainability
• Transparency
• Data privacy
• Data governance
• Model risk
• AI security
• Human oversight
• Regulatory compliance

AACSB’s current standards specifically emphasise responsible and ethical technology use, human judgment, critical evaluation, innovation and experiential learning.

IBSF should use a practical, technology-intensive learning model.

Teaching methods

• Lectures
• Tutorials
• Coding laboratories
• AI laboratories
• Financial laboratories
• Banking simulations
• FinTech case studies
• Machine-learning projects
• Data-analysis projects
• Cybersecurity exercises
• Investment simulations
• FinTech product development
• Group projects
• Industry projects
• Hackathons
• Research projects
• Internship
• Industry guest lectures

Recommended IBSF facility

IBSF FINTECH & AI FINANCIAL INNOVATION LAB

The programme would benefit significantly from a dedicated laboratory equipped for:

• Python programming
• AI/ML development
• Financial datasets
• Data visualisation
• Financial modelling
• Banking simulations
• Digital-payment simulations
• Cybersecurity exercises
• Blockchain experimentation
• AI governance and testing
• FinTech product development

This would give IBSF a distinctive practical identity and strengthen the connection between classroom learning and financial-sector practice.

Assessment should measure financial knowledge, computing competence, analytical capability, AI skills and professional judgment.

Recommended assessment framework

Assessment methods

Students may be evaluated through:

• Written examinations
• Coding assignments
• Financial-data projects
• Machine-learning models
• Banking case studies
• Financial simulations
• FinTech prototypes
• AI model evaluation
• Presentations
• Technical reports
• Research papers
• Internship assessments
• Capstone projects

The programme should maintain an Assurance of Learning system that measures attainment of programme learning outcomes and uses evidence to improve the curriculum. This is consistent with AACSB’s approach to systematic assessment and continuous improvement.

The degree prepares graduates for careers at the intersection of banking, financial services, technology and artificial intelligence.

Digital Banking

• Digital Banking Analyst
• Banking Technology Analyst
• Digital Transformation Analyst
• Digital Product Analyst
• Banking Data Analyst
• Digital Payments Analyst

FinTech

• FinTech Analyst
• FinTech Product Analyst
• FinTech Consultant
• Digital Finance Specialist
• Payments Technology Analyst
• Open Banking Analyst
• Embedded Finance Analyst

Artificial Intelligence

• Junior AI Analyst
• AI Business Analyst
• Financial AI Analyst
• AI Product Analyst
• Machine Learning Analyst
• AI Implementation Analyst
• AI Governance Analyst

Financial Data

• Financial Data Analyst
• Business Intelligence Analyst
• Credit Analytics Analyst
• Risk Data Analyst
• Customer Analytics Analyst
• Financial Data Scientist — subject to appropriate postgraduate/technical experience

Risk, Compliance & Financial Crime

• Financial Risk Analyst
• Model Risk Analyst
• AML Analytics Analyst
• Fraud Analytics Analyst
• Cyber Risk Analyst
• AI Compliance Analyst

Investment & Finance

• Investment Analytics Associate
• Financial Modelling Analyst
• Portfolio Analytics Analyst
• Quantitative Finance Analyst
• Treasury Analytics Analyst

Central Banking & Regulation

Graduates may work in:

• Central banks
• Financial regulators
• Payment-system regulators
• Digital-finance regulatory units
• Financial-stability departments
• FinTech innovation offices
• Regulatory technology units

IBSF can organise the optional modules into six distinctive pathways.

PATHWAY 1 — DIGITAL BANKING & FINTECH

Focus: Digital banking, payments, open banking, blockchain and FinTech innovation.

Career orientation: Digital Banking Analyst, FinTech Specialist, Payments Analyst.

PATHWAY 2 — AI & FINANCIAL ANALYTICS

Focus: Machine learning, financial analytics, predictive modelling and generative AI.

Career orientation: Financial AI Analyst, Data Analyst, Machine Learning Analyst.

PATHWAY 3 — AI-POWERED RISK & FINANCIAL CRIME

Focus: Risk analytics, fraud detection, AML, cybersecurity and AI risk.

Career orientation: Risk Analyst, Fraud Analytics Analyst, AML Analytics Specialist, Model Risk Analyst.

PATHWAY 4 — DIGITAL INVESTMENT & WEALTH MANAGEMENT

Focus: Robo-advisory, algorithmic investment, portfolio analytics and digital wealth management.

Career orientation: Investment Analytics Analyst, Digital Wealth Analyst, Robo-Advisory Specialist.

PATHWAY 5 — REGTECH & SUPTECH

Focus: Digital regulation, AI governance, compliance analytics and supervisory technology.

Career orientation: RegTech Analyst, SupTech Analyst, AI Compliance Analyst.

PATHWAY 6 — AI & FINTECH ENTREPRENEURSHIP

Focus: FinTech ventures, AI products, digital platforms, innovation and product management.

Career orientation: FinTech Product Manager, Innovation Analyst, AI/FinTech Entrepreneur.

Degree Award: Bachelor of Science (B.Sc.) in Banking, FinTech & Artificial Intelligence
Duration: 3 Years
Structure: 6 Semesters
Total Academic Load: 180 ECTS
Indicative U.S. Credit Reference: Approximately 120 semester credits, subject to formal credit evaluation
Mode of Study: Full-Time | Blended | Online | Face-to-Face
Language: English
Level: Undergraduate / First Cycle
Academic School: School of Banking, FinTech & Artificial Intelligence

International academic positioning

The proposed IBSF programme combines banking and finance with computing, data science, artificial intelligence and FinTech. It is designed around the European three-year/180-ECTS model while incorporating internationally recognised expectations for quantitative skills, computing, financial-sector knowledge, ethical technology use, experiential learning and outcome-based assessment.

For the computing component, the curriculum is informed by current international computing-programme expectations covering programming, data, algorithms, security, privacy, professional responsibility and applied projects. Current 2026–27 ABET criteria for AI-related computing programmes also identify AI foundations, programming, data and knowledge engineering, machine learning/deep learning, AI-system design, infrastructure and responsible AI as important curriculum areas.

For business education, AACSB’s current standards emphasise responsible technology use, innovation, experiential learning, contemporary research and systematic Assurance of Learning.