Digital Transformation nabard seo some.pptx

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1. Digital Transformation Prof. Saurabh Kumar

2. Does Technology Matters in Any Organization?

3. Can Technology Alone Give a Competitive Advantage to Organizations?

4. Digital Transformation Digital transformation is the process of using digital technologies to create new — or modify existing — business processes, culture, and customer experiences to meet changing business and market requirements. ERP, CRM, SCM implementation in organizations

5. For Example Workflow of travel application for a staff in IIM Indore Earlier- Employee  Supervisor  Department Head Dean Director After ERP- Employee  Department Head Dean Director

6. Inaccurate requirements Uninvolved top management Shifting project objectives Inaccurate estimates Unexpected risks Dependency delays Not enough resources Poor project management Possible Reasons for large IT Project Failure

7. Example of Inaccurate Requirements

8. Example of Poor Project Management

9. Use of Data in Digital Transformation

10. Market Basket Analysis Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items It works by looking for combinations of items that occur together frequently in transactions For example: Bread and Butter are bought together, so they are placed near to each other Toothbrush and Toothpaste Milk, cheese, curd Similar examples in Banking/ Insurance Sector?

11. Technological Enablers of Digital Transformation Internet of Things (IoT) : IoT refers to the network of interconnected devices and objects that can collect and exchange data. IoT is applied through telematics, where sensors and devices gather data from vehicles, homes, or wearable devices. Artificial Intelligence (AI) and Machine Learning: AI involves the creation of machines capable of simulating human intelligence, including learning and problem-solving. Machine learning, a subset of AI, involves algorithms that improve performance over time through data analysis. AI and machine learning are transforming the banking industry by doing risk assessment, fraud detections and so on.

12. Machine Learning Classification Example S.No Age Gender Income Loan Default 1 32 Male 10 lpa No 2 24 Female 12 lpa Yes 3 56 Male 19 lpa No 4 23 Male 8 lpa Yes 5 22 Female 15 lpa Yes 6 45 Male 12 lpa No 7 51 Female 8 lpa No 8 28 Female 10 lpa ? 9 18 Male 4 lpa ? 10 72 Male 28 lpa ? 12

13. Illustrating Classification

14. Example of Classification Task

15. Example of Decision Tree

16. Example of Decision Tree

17. Descriptive Analytics Predictive Analytics Prescriptive Analytics* *(UP 112 project)

18. Gartner AI Hype Cycle-2025

19. Gartner AI Hype Cycle-2024

20. When should a manager/decision maker choose to adopt a technology out of the five phases?

21. Generative AI Can ChatGPT be used for digital transformation of organizations? What are the threats and challenges?

22. Image Generation: First Side A n d r e w N g

23. Image Generation: Second Side A n d r e w N g

24. Image, Audio and Video generation A n d r e w N g Voice generation https:// voicemaker.in /voice-samples

25. Image, Audio and Video generation A n d r e w N g Video generation https:// www.youtube.com / watch?v =HK6y8DAPN_0&ab_channel= OpenAI

26. AI is a set of tools U n s u p e r v i s e d learning Ge n e r a t i v e A I Su p e r v i s e d l e a r n i n g ( l a b e li n g t h i n g s ) AI R e i n f o r c e m e n t learning A n d r e w N g

27. Supervised learning (labeling things) I n p u t ( A ) O ut p u t ( B ) A p p li c a t i o n A n d r e w N g Email A d , u s e r i n f o I m a g e , r a d a r i n f o X-ray image I m a g e o f p h o n e Audio recording Restaurant reviews S p a m ? ( / 1 ) C l i c k ? ( / 1 ) P o s i t i o n o f o t h e r c a rs Diagnosis D e f e c t ? ( / 1 ) T e x t t r a n s c r i p t Sentiment (pos/neg) Spam filtering O n li n e a d v e r t i s i n g Self-driving car Healthcare Tools like Cashify S p ee c h r e c o g n i t i o n R e p u t a t i o n m o n i t o r i n g

28. Generating text using Large Language Models (LLMs) T e x t g en e r a t i on p r oc e s s I l o v e e a t i n g prompt food my _________ snacks A I o u t p u t A n d r e w N g

29. How Large Language Models (LLMs) work My favorite food is a My favorite food is a bagel My favorite food is a bagel with My favorite food is a bagel with cream bagel with cream c h e e se LLMs are built by using supervised learning (A → B) to repeatedly predict the next word. M y f a v o r i t e f oo d i s a bag e l w i t h c r ea m c h ee s e Input (A) Output (B) A n d r e w N g When we train a very large AI system on a lot of data (hundreds of billions of words), we get a Large Language Model like ChatGPT.

30. Bias and Toxicity - Gen AI An LLM can reflect the biases that exist in the text it learned from. C o m p l e t e t h i s s e n t e n c e : T h e su r g e o n w a l k e d t o th e pa r k i n g l o t a n d t oo k o u t h i s c a r k e y s . C o m p l e t e t h i s s e n t e n c e : T h e nu r s e w a l k e d t o t h e pa r k i n g l o t a n d t oo k o u t h e r p hon e . a s s u m e d m a l e a s s u m e d f e m a l e Some LLMs can output toxic or other harmful speech, but most models have gotten much safer over time. A n d r e w N g

31. Small Case Studies-1 At a bank, information about Mr. Naveen He has three accounts: savings, credit card, and a car loan He makes five deposits and 25 withdrawals per month He never visits a branch in person He has a total of Rs 3,50,000 in cash deposited He owes a total of Rs 1,15,000 between his credit card and car loan What is the next best offer you can place to him in an e-mail?

32. Example ( Contd ) If Mr. Naveen’s web behavior is examined and we got additional information He browsed home loan rates five times in past month He viewed websites like makaan.com , housing.com , 99acres.com He explored home loan options (i.e., fixed versus variable, 15- versus 30-year) twice in the past month Then, it is pretty easy to decide what to discuss next with Mr. Naveen

33. Privacy Issues in Social Network Facebook/ Instagram Whatsapp Concept of privacy calculus- Tradeoff Classic case of Truecaller

34. Privacy Issues in Location Data Retail store example- wifi - geofencing Fastag removal on highways

35. Retailers Taking Phone Numbers

36. How to avoid the hackers to gain unauthorized access to the systems? Create complex passwords & change them often Install anti-virus programs and highly secured firewall Log out of accounts when you are done with them Make sure you are on the official website when entering passwords Use two-factor authentication Clear your browser cookies and never ever save your passwords in the browser and so on

37. Digital Personal Data Protection (DPDP) Act 2023 The Act deals with safeguards around personal data only and keep non-personal data (information that does not reveal the identity of an individual) out of its ambit It will continue to evolve (with the changing technology) Entities that fail to take “reasonable security safeguards” to prevent personal data breaches will be fined as high as Rs 250 crore.

38. Digital Personal Data Protection (DPDP) Act 2023 The draft data protection bill 2022 states that- “When it comes to data localization rules, where the government mandated all companies dealing with sensitive data of Indian users to keep a copy within its borders. The government has allowed the transfer of data and its storage in “trusted geographies” in the revised draft of the data protection Bill, doing away with the data localisation requirement proposed in the earlier version. The government will define which geographies are “trusted” from time to time” The clause is now removed in DPDP 2023

39. Individual Privacy Vs National Security Apple Vs FBI Edward Snowden example

40. Thank you! Email: [email protected]