BBA 404 AI and ML in Business Notes from Unnati Educations cover all 4 units of the paper in 67 pages: AI and ML basics, then AI in marketing, finance and HR. They include 12 case studies, 7 exam tips, 4 quick revision tables and 74 important exam questions, mapped to the revised BBA syllabus that applies from 2024-25.
Written and reviewed by Sheetal Kirola, Academic Head, M.Ed. · Checked against BBA Scheme and Syllabus (NEP Based), Revised, w.e.f. Academic Session 2024-2025 · Updated 13 October 2026 · Editorial policy
Scheme: Revised NEP-based BBA scheme 2024-25 | Applies to: students from academic session 2024-25 onwards | Verified against: BBA Scheme and Syllabus (NEP Based), Revised, w.e.f. Academic Session 2024-2025
BBA 404 AI and ML in Business at a glance: GGSIPU BBA Sem 8 credits, units and exam pattern
BBA 404 is taught only in Semester 8 of BBA (Hons.) and BBA (Hons. with Research), so three-year degree students never sit it. See all GGSIPU BBA Semester 8 papers.
| Code and title | BBA 404: AI and ML in Business |
|---|---|
| Category and credits | Inter-disciplinary Course / Minor, 4 credits |
| Weekly hours | Hons.: 4 lecture. Hons. with Research: 3 lecture + 1 tutorial/practical. Syllabus page: L-4, T-0. |
| Marks | 100: 40 internal (class test 15 + assignments, presentation, viva or group discussion 25) + 60 end-term |
| End-term pattern | Not printed in the official BBA PDF |
| Units | 4 units, 15 teaching hours each |
| Paired lab | None |
| Pass rules | GGSIPU Ordinance 11 (examinations and results) |
BBA 404 AI and ML in Business Notes unit-wise: 74 exam questions and 12 case studies across 4 units
More course papers: GGSIPU BBA notes.
BBA 404 Unit 1: Fundamentals of AI and ML
Pages 5 to 22 (18 pages). Definitions (McCarthy, Samuel, Mitchell), four learning types, neural networks with backpropagation, AI versus human intelligence on 12 bases, eight phases of AI history, ChatGPT, TensorFlow and IBM Watson, and 12 limitations. 2 case studies, 19 questions.
| Algorithm | What it does | Business use |
|---|---|---|
| Linear regression | Predicts a number | Sales forecasting |
| Logistic regression | Predicts a yes/no probability | Loan default |
| K-means clustering | Groups similar records | Customer segmentation |
| Apriori | Finds items bought together | Market basket analysis |
Quick look: ML algorithms in business (from page 9)
- Algorithm
- Linear regression
- What it does
- Predicts a number
- Business use
- Sales forecasting
- Algorithm
- Logistic regression
- What it does
- Predicts a yes/no probability
- Business use
- Loan default
- Algorithm
- K-means clustering
- What it does
- Groups similar records
- Business use
- Customer segmentation
- Algorithm
- Apriori
- What it does
- Finds items bought together
- Business use
- Market basket analysis
BBA 404 Unit 2: Applications of AI in Marketing
Pages 23 to 38 (16 pages). Predictive analysis, AI segmentation, content-based, collaborative and hybrid recommenders, five chatbot types, real-time bidding through DSPs and SSPs, sentiment analysis, social listening, four levels of personalisation and the 2023 Dark Patterns guidelines. 5 case studies; 19 questions.
| Customer | Last purchase | R-F-M | Segment |
|---|---|---|---|
| A | 5 days ago | 5-5-5 | Champion |
| B | 20 days ago | 4-3-3 | Loyal |
| C | 120 days ago | 2-3-3 | At risk |
| D | 300 days ago | 1-1-1 | Lost |
Quick look: RFM scoring (from page 27)
- Customer
- A
- Last purchase
- 5 days ago
- R-F-M
- 5-5-5
- Segment
- Champion
- Customer
- B
- Last purchase
- 20 days ago
- R-F-M
- 4-3-3
- Segment
- Loyal
- Customer
- C
- Last purchase
- 120 days ago
- R-F-M
- 2-3-3
- Segment
- At risk
- Customer
- D
- Last purchase
- 300 days ago
- R-F-M
- 1-1-1
- Segment
- Lost
BBA 404 Unit 3: Applications of AI in Finance
Pages 39 to 53 (15 pages). After fraud (see the excerpt below): seven algo trading strategies, HFT and co-location, SEBI rules, credit scoring on UPI, GST and Account Aggregator data, robo-advisors, bank chatbots, rolling forecasts, RegTech and SupTech. 2 case studies, 18 questions.
| Term | Meaning |
|---|---|
| Probability of Default (PD) | Chance the borrower fails to repay |
| Loss Given Default (LGD) | Share of the loan lost after recovery |
| Exposure at Default (EAD) | Amount outstanding at default |
| Expected Loss (EL) | EL = PD × LGD × EAD |
Quick look: credit risk terms (from page 45)
- Term
- Probability of Default (PD)
- Meaning
- Chance the borrower fails to repay
- Term
- Loss Given Default (LGD)
- Meaning
- Share of the loan lost after recovery
- Term
- Exposure at Default (EAD)
- Meaning
- Amount outstanding at default
- Term
- Expected Loss (EL)
- Meaning
EL = PD × LGD × EAD
BBA 404 Unit 4: Applications of AI in Human Resource Management
Pages 54 to 67 (14 pages). ATS and 7-step resume screening, six assessment types, employee sentiment, HR chatbots, continuous performance management, learning experience platforms, attrition prediction and D&I metrics with Indian law. 3 case studies, including Amazon's scrapped recruiting tool; 18 questions.
Quick look: calculating the attrition rate (from page 63)
- Attrition rate = employees who left ÷ average employees × 100.
- Average employees = (400 + 440) ÷ 2 = 420.
- Rate = 63 ÷ 420 × 100 = 15%.
BBA 404 exam corner: 46 short-answer and 28 long-answer questions by unit
Each unit lists "Short Answer Questions (2–5 Marks)" and "Long Answer Questions (10–15 Marks)": exam-style practice, not tagged with past years.
| Unit | Short-answer | Long-answer | Case studies |
|---|---|---|---|
| Unit 1 | 12 | 7 | 2 |
| Unit 2 | 12 | 7 | 5 |
| Unit 3 | 11 | 7 | 2 |
| Unit 4 | 11 | 7 | 3 |
| Total | 46 | 28 | 12 |
Questions per unit
- Unit
- Unit 1
- Short-answer
- 12
- Long-answer
- 7
- Case studies
- 2
- Unit
- Unit 2
- Short-answer
- 12
- Long-answer
- 7
- Case studies
- 5
- Unit
- Unit 3
- Short-answer
- 11
- Long-answer
- 7
- Case studies
- 2
- Unit
- Unit 4
- Short-answer
- 11
- Long-answer
- 7
- Case studies
- 3
- Unit
- Total
- Short-answer
- 46
- Long-answer
- 28
- Case studies
- 12
- Unit 1: What is an Artificial Neural Network? Explain its structure, working and business applications. Pointer: pages 11 to 12.
- Unit 2: Explain ad targeting and programmatic advertising, the real-time bidding process and the role of AI. Pointer: RTB flow, pages 31 to 33.
- Unit 3: What are robo-advisors? Explain their working and compare them with traditional advisors. Pointer: pages 46 to 48.
- Unit 4: Discuss AI in resume screening, assessment and interviewing, and the ethical concerns. Pointer: Amazon case, pages 56 to 58.
The BBA 404 notes list 46 short-answer and 28 long-answer exam questions across 4 units, plus 12 case studies.
More practice sits in the BBA 404 important questions.
Who writes and checks the BBA 404 AI and ML notes?
- Sheetal Kirola: wrote and reviewed these notes · Academic Head, Unnati Educations · M.Ed. · 7 years of experience · Sheetal Kirola's profile
These GGSIPU BBA Sem 8 AI and ML in Business notes follow the revised BBA scheme from 2024-25. The 2026-27 edition cites current Indian rules such as the DPDP Act 2023 and RBI's FREE-AI framework. Updated 13 October 2026; see our editorial policy.
The same team prepares GGSIPU important questions for every course.
How to write BBA 404 answers: difference tables, flow diagrams and Indian case examples
BBA 404 has only two small calculations, so marks come from structure. The notes build almost every topic the same way: definition box, comparison table, Indian company examples, then benefits and limitations. Use that build in the exam.
Short answers. For a "differentiate" question, the notes advise six to eight points in a table with one example each side. Their supervised versus unsupervised table (pages 10 to 11) compares data, supervision, goal, problem types, accuracy, complexity, algorithms and a business example.
Long answers. The exam tips give one move per topic:
- History of AI: phases with years, a small timeline table, then Indian examples.
- Programmatic advertising: draw Advertiser → DSP → Ad Exchange ← SSP ← Publisher.
- Algo trading: add the 2010 Flash Crash and SEBI's rules.
- RegTech: expand KYC, AML, PEP, FIU-IND and STR once.
- AI in recruitment: add a bias paragraph with the Amazon case.
Common mix-ups. All ML is AI, but not all AI is ML; HFT is the ultra-fast subset of algo trading.
Worked example: "Differentiate between AI and ML"
Open with one line: AI is the goal (intelligent behaviour), ML is one way to reach it (learning from data). Then:
| Basis | AI | ML |
|---|---|---|
| Scope | Broader concept | Subset of AI |
| Output | Decision or action | Prediction or pattern |
| Example | Chatbots, self-driving cars | Spam filters, credit scoring |
Answer frame for the AI versus ML question
- Basis
- Scope
- AI
- Broader concept
- ML
- Subset of AI
- Basis
- Output
- AI
- Decision or action
- ML
- Prediction or pattern
- Basis
- Example
- AI
- Chatbots, self-driving cars
- ML
- Spam filters, credit scoring
For the two numericals (expected loss, attrition rate), write the formula, the substitution, then the answer with ₹ or %.
Practise this format on GGSIPU BBA important questions.
BBA 404 and the GGSIPU BBA Sem 8 papers it connects to
BBA 404 has no practical paper; every semester's codes sit on the GGSIPU BBA syllabus page.
Hons. pairs it with BBA 402 Research Ethics and Writing and three electives; Hons. with Research adds BBA 412 Research Project/Dissertation.
- Unit 1 → BBA 402 Research Ethics and Writing.
- Unit 2 → BBA 414 Content Marketing, BBA 416 Retail & Mall Management, BBA 418 Digital Branding Engagement.
- Unit 3 → BBA 420 Financial Risk Management, BBA 424 Financial Analytics, BBA 422 Mergers and Corporate Restructuring.
- Unit 4 → BBA 410 Human Resource Development - Strategies and Systems, BBA 408 Management of Industrial Relations, BBA 406 Cross Cultural and Global Management.
Other electives: BBA 426 International Business Ethics, BBA 428 Global Outsourcing - Issues & Perspectives, BBA 430 International Business Venturing Abroad, BBA 432 Family Business Management, BBA 434 Business Valuation for Startups, BBA 436 Government & Institutional support system for MSMEs.
AI-based fraud detection in finance: excerpt from page 41 of the BBA 404 notes
Excerpt from page 41
Meaning
AI-based fraud detection is the use of machine learning models to analyse transactions and user behaviour in real time, identify patterns that point to fraud, and block or flag suspicious activity before money is lost. Prevention goes one step earlier: stopping fraud at the source through authentication, risk scoring and customer alerts.
Types of financial fraud
- Card fraud: stolen or cloned card details used for purchases.
- UPI and digital payment fraud: fake payment requests, QR code scams, SIM swap.
- Phishing and vishing: fake emails or calls to steal OTPs and passwords.
- Identity theft: someone else's Aadhaar or PAN used to open accounts or take loans.
- Account takeover: a hacker gets into a genuine account.
- Loan and application fraud: fake documents or synthetic identities.
- Insurance claim fraud: false or inflated claims.
- Money laundering: illegal money disguised as legitimate.
- Deepfake fraud: AI-generated voice or video impersonating officials or relatives.
Rule-based versus AI-based fraud detection
| Basis | Rule-based system | AI/ML-based system |
|---|---|---|
| Method | Fixed rules, e.g. "flag if amount is above ₹1 lakh" | Learns patterns from millions of past transactions |
| New fraud types | Missed until someone writes a new rule | Spots unusual patterns even when new |
| False alarms | High; many genuine customers blocked | Lower; knows each customer's normal behaviour |
| Speed | Slow updates | Real-time scoring in milliseconds |
| Adaptability | Static | Keeps learning |
Traditional rule-based system compared with an AI/ML system (page 41)
- Basis
- Method
- Rule-based system
- Fixed rules, e.g. "flag if amount is above ₹1 lakh"
- AI/ML-based system
- Learns patterns from millions of past transactions
- Basis
- New fraud types
- Rule-based system
- Missed until someone writes a new rule
- AI/ML-based system
- Spots unusual patterns even when new
- Basis
- False alarms
- Rule-based system
- High; many genuine customers blocked
- AI/ML-based system
- Lower; knows each customer's normal behaviour
- Basis
- Speed
- Rule-based system
- Slow updates
- AI/ML-based system
- Real-time scoring in milliseconds
- Basis
- Adaptability
- Rule-based system
- Static
- AI/ML-based system
- Keeps learning
How AI detects fraud
- Data collection: amount, time, location, device, IP address, merchant and past behaviour.
- Behaviour profiling: the model learns each customer's normal spending, cities, devices and timings.
- Real-time scoring: every new transaction gets a fraud risk score.
- Anomaly detection: a large deviation is flagged, for example a Delhi customer's card used in two countries within an hour.
- Decision: low risk is approved, medium risk asks for an OTP or a call, high risk is blocked.
- Feedback loop: confirmed fraud cases are used to retrain the model.
Do the BBA 404 notes cover the full GGSIPU syllabus?
Yes. Every official BBA 404 topic has its own section in the notes; the unit check found no gaps.
| Official unit | Official topics | Where in the notes | Status |
|---|---|---|---|
| I: Fundamentals of AI and ML | Introduction to AI and ML; Algorithms; Supervised vs Unsupervised Learning; Neural Networks; AI vs Human Intelligence; History and Evolution of AI in Business; Overview of AI Tools and Platforms (ChatGPT, TensorFlow, IBM Watson, etc.); Scope and Limitations of AI/ML in Business Contexts | Sections 1-6, pp. 6-20 | Covered |
| II: Applications of AI in Marketing | Predictive Analysis; Customer Segmentation; Recommendation Systems; Chatbots and Conversational AI; Ad Targeting and Programmatic Advertising; Sentiment analysis and Social Listening; Content creation and personalization | Sections 2-8, pp. 25-37 | Covered |
| III: Applications of AI in Finance | Fraud detection and prevention; Algorithmic and high frequency trading; Credit scoring and risk assessment; Robo advisory and wealth management; Customer service and virtual assistants; Financial forecasting and budgeting; Compliance and regulatory technology; Personal finance management | Sections 2-9, pp. 41-52 | Covered |
| IV: Applications of AI in HRM | Resume screening and shortlisting; Candidate assessment and interviewing; Employee sentiment analysis; Chatbots for HR services; Performance management; Learning and development personalization; Attrition prediction; Diversity and inclusion analytics | Sections 2-9, pp. 56-65 | Covered |
Official BBA 404 syllabus topics mapped to the notes
- Official unit
- I: Fundamentals of AI and ML
- Official topics
- Introduction to AI and ML; Algorithms; Supervised vs Unsupervised Learning; Neural Networks; AI vs Human Intelligence; History and Evolution of AI in Business; Overview of AI Tools and Platforms (ChatGPT, TensorFlow, IBM Watson, etc.); Scope and Limitations of AI/ML in Business Contexts
- Where in the notes
- Sections 1-6, pp. 6-20
- Status
- Covered
- Official unit
- II: Applications of AI in Marketing
- Official topics
- Predictive Analysis; Customer Segmentation; Recommendation Systems; Chatbots and Conversational AI; Ad Targeting and Programmatic Advertising; Sentiment analysis and Social Listening; Content creation and personalization
- Where in the notes
- Sections 2-8, pp. 25-37
- Status
- Covered
- Official unit
- III: Applications of AI in Finance
- Official topics
- Fraud detection and prevention; Algorithmic and high frequency trading; Credit scoring and risk assessment; Robo advisory and wealth management; Customer service and virtual assistants; Financial forecasting and budgeting; Compliance and regulatory technology; Personal finance management
- Where in the notes
- Sections 2-9, pp. 41-52
- Status
- Covered
- Official unit
- IV: Applications of AI in HRM
- Official topics
- Resume screening and shortlisting; Candidate assessment and interviewing; Employee sentiment analysis; Chatbots for HR services; Performance management; Learning and development personalization; Attrition prediction; Diversity and inclusion analytics
- Where in the notes
- Sections 2-9, pp. 56-65
- Status
- Covered
Verify it yourself (BBA 404 is on page 200): BBA Scheme and Syllabus (NEP Based), Revised, w.e.f. 2024-25.
Every course in GGSIPU notes is mapped to its official PDF the same way.
BBA 404 AI and ML notes FAQs
How do I get the full BBA 404 notes?
Tap "Get Full Notes" to message 9899436384 on WhatsApp. The full BBA 404 notes come only as a printed book, not as a PDF. Once your order is confirmed on WhatsApp, the book reaches you within 5 to 7 days at most, depending on your location.
What is the passing mark for BBA 404?
BBA 404 is marked out of 100: 40 internal and 60 in the end-term exam. The internal 40 splits into a class test of 15 and 25 for assignments, presentations, viva or group discussion. The official BBA scheme PDF does not print a pass percentage for this paper, so check end-term minimums and re-appear rules in GGSIPU Ordinance 11 on ipu.ac.in.
Is BBA 404 in both BBA (Hons.) and BBA (Hons. with Research)?
Yes. Both eighth-semester schemes list BBA 404 as an inter-disciplinary course or minor of 4 credits. Hons. shows 4 lecture hours a week; Hons. with Research shows 3 lecture hours plus 1 tutorial or practical hour. The four units are identical, so the same notes work for either track. Three-year degree students who exit after Semester 6 do not study it.
Do I need coding skills for BBA 404?
No. BBA 404 has no lab paper, and the notes explain AI and ML in business language rather than code. Tools such as TensorFlow are described by what they do and who uses them; the notes simply mention that TensorFlow itself needs Python. What earns marks is clear tables, simple flow diagrams and Indian examples like UPI fraud monitoring and CIBIL scores.
Which BBA 404 unit is the longest in the notes?
Unit I, Fundamentals of AI and ML, is the longest at 18 pages because it builds the base for the rest: definitions, learning types, neural networks, AI history and tools. Unit II on marketing runs 16 pages and has the most case studies, five in all. Unit III on finance runs 15 pages and Unit IV on HRM runs 14.
Are there numericals in BBA 404?
Only two small ones appear in the BBA 404 notes. Unit III works out expected loss on a ₹5,00,000 loan with EL = PD × LGD × EAD, which gives ₹12,000. Unit IV works out an attrition rate of 15% from leavers and average headcount. The Unit IV question list also asks for an attrition rate, so practise both formulas once.
Get the full BBA 404 AI and ML in Business Notes on WhatsApp
| Unit | Pages | Case studies |
|---|---|---|
| Unit 1: Fundamentals of AI and ML | 5 to 22 (18) | 2 |
| Unit 2: AI in Marketing | 23 to 38 (16) | 5 |
| Unit 3: AI in Finance | 39 to 53 (15) | 2 |
| Unit 4: AI in HRM | 54 to 67 (14) | 3 |
BBA 404 notes book by unit
- Unit
- Unit 1: Fundamentals of AI and ML
- Pages
- 5 to 22 (18)
- Case studies
- 2
- Unit
- Unit 2: AI in Marketing
- Pages
- 23 to 38 (16)
- Case studies
- 5
- Unit
- Unit 3: AI in Finance
- Pages
- 39 to 53 (15)
- Case studies
- 2
- Unit
- Unit 4: AI in HRM
- Pages
- 54 to 67 (14)
- Case studies
- 3
67 pages, 74 exam questions and 7 exam tips. These AI and ML in Business notes in English come as a printed book only, not a PDF, delivered within 5 to 7 days of your WhatsApp order, depending on your location.
Get the full BBA 404 notes book on WhatsApp (4 units, 67 pages)
Unnati Educations is an independent education provider based in Vikaspuri, Delhi. We are not affiliated with, authorised by, or endorsed by Guru Gobind Singh Indraprastha University (GGSIPU). 'GGSIPU' and 'IPU' are used only to describe the syllabus our notes follow. For official information, visit ipu.ac.in.
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