#1 Financial Services ranks as the most Al exposed sector globally, making Al a critical capability for finance professionals.
77.4% Al-related job postings in Financial Services grew 77.4% in 2025, significantly outpacing overall hiring in the sector.
53% Professionals with Al capabilities in Financial Services command an average 53% wage premium over non-Al roles.
88% Most Al hiring is for professionals who can apply Al in finance, not build Al systems highlighting the growing need for Al-literate finance leaders.

This programme equips finance professionals with practical fluency in artificial intelligence, enabling them to confidently understand, use, and evaluate Al technologies within modern financial and fintech environments. It is designed to bridge the ga between traditional finance expertise and emerging Al-driven decision systems, helping participants move beyond conceptual awareness to hands on familiarity with the tools, models, and workflows increasingly used across the finance function.

Why This Programme:

Al First, Not Generic Al: A specialized programme focused exclusively on finance, banking, audit,
risk, compliance, and fintech covering practical Al applications relevant to finance professionals rather than generic Al concepts.
Accessible, No-Code First: Al is introduced using accessible tools like Gen Al platforms, Excel, Power BI, Python demonstrations, Google Colab, and no-code platforms, making it suitable even without a technical background.
Spans the Entire Finance Value Chain: Al is introduced using accessible tools like Gen Al platforms, Excel, Power BI, Python demonstrations, Google Colab, and no-code platforms, making it suitable even without a technical background.

Desired Candidate Profile

Eligibility:

This programme is designed for a diverse group of professionals and aspiring experts sitting at the intersection of finance and technology

Indian Participants

Graduates (10+2+3) or Diploma Holders (10+2+3 or 10+2+4) from a recognised university (UGC/AICTE/DEC/AIU/State Government) in any discipline.
Diploma holders with relevant work experience may be considered on a case-by-case basis.

International Participants

Graduation or equivalent degree from any recognised University or Institution in their respective country.

Also Open To

Professionals with strong academic or professional credentials - such as an M.Com, CA, CMA, or CFA.
Tech-savvy individuals looking to pivot into finance, provided they bring a high degree of comfort with technology.

Course Modules

Programme Curriculum

Module 01: Finance in the Age of AI

AI, Machine Learning, GenAI, and automation: what each one does
Where AI is being used in finance today
GenAI platforms, Excel, Power BI, Python, Google Colab, and no-code platforms
Short demonstrations of AI-enabled finance work

Module 02: Financial Data & AI Foundations

Accounting, market, transaction, customer, and textual data
Data quality, missing values, outliers, bias, and leakage
Basic machine learning concepts - without heavy mathematics
Reading and interpreting model outputs in a finance context

Module 03: Generative AI for Financial Work

How large language models and Generative AI work
Prompting for financial analysis, explanation, and decision support
Annual reports, earnings calls, analyst reports, and board papers
Build the idea of a finance knowledge assistant
Hallucination, confidentiality, false confidence, and verification

Module 04: AI in the CFO Office

AI in FP&A, budgeting, and forecasting
AI-assisted management reporting and dashboard commentary
Scenario analysis, sensitivity analysis, and planning support
Working capital, cost, revenue, and margin analytics
Designing an AI-enabled finance workflow

Module 05: AI in Banking, Lending, Payments & FinTech

AI in banking and financial services
Digital lending, credit scoring, underwriting, and collections
Payments, open banking, embedded finance, and platform models
Wealth-tech, insurtech, regtech, and personalized finance
How AI creates scale, speed, and competitive advantage
Fintech Teardown: Analyze how one fintech embeds AI in its business model.
Credit Scenario: Walk through an AI-assisted credit-scoring decision.

Module 06: AI in Risk, Audit, Fraud Detection & Compliance

AI for risk assessment and audit analytics
Fraud detection and anomaly identification
Credit risk, operational risk, and compliance use cases
Continuous monitoring and internal control applications
Explainability, documentation, and accountability
Anomaly Hunt: Use AI to flag anomalies in a transaction dataset.
Control Check: Design an AI-assisted continuous-monitoring control.

Module 07: Digital Finance Architecture & Emerging Technologies

APIs, cloud, and data platforms
Cybersecurity and operational resilience in digital finance
Blockchain, smart contracts, CBDCs, and tokenization
Architecture Map: Sketch the digital-finance stack for a use case of your choice.
Emerging-Tech Brief: Evaluate one emerging technology s relevance to your organization.

Module 08: Responsible AI, Strategy & Capstone

AI governance and model risk management
Bias, fairness, explainability, privacy, and human oversight
Prioritizing AI use cases in finance
Design an AI-enabled use case in finance, banking, risk, audit, or fintech covering the business problem, data
requirements, workflow, tools, expected benefits, key risks, governance controls, and implementation roadmap.