PROF DR. PHILIPPE BAECKE

Transcription

1 PROF DR. PHILIPPE BAECKE Professor of Business Analytics and Big Data Prof. at Vlerick Business School Affiliated Prof. at Ghent University Visiting Prof. at Trinity College (Dublin) Visiting Prof. at UCD & Kaplan Business School (Hong Kong) Visiting Prof. at Université de Namur Research Business Analytics & Big Data (electronic) Customer Relationship Management Digital Marketing Spatial & network analysis Ghent Campus, Office G1.09 Tel.:

2 THE VALUE OF ANALYTICS IN FINANCIAL SERVICES PROF. DR. PHILIPPE BAECKE

3 Impact Digital Financial Services

4 IMPACT FROM DIGITAL Consolidation of agents and brokers Margins 50% 40% 44% 30% 20% 10% 8% 12% 18% 23% 22% % < EUR EUR EUR EUR EUR > EUR (source: Benthurst & co 2016)

5 IMPACT FROM DIGITAL Customer experience Cost reduction

6 BIG DATA

7 BIG DATA Google trends: Big Data PhD Essays on Data Augmentation: The Value of Additional Information Data Driven Marketing Creating Business Value with Big Data

8 BIG DATA STRATEGY

9 DATA COLLECTION Touch points www Company Business Customer call Transactions sales Social Mobile Data silo Data silo Data silo Data silo Data silo Department 1 Department 2 Department 3 sensors

10 DATA COLLECTION Touch points www call Transactions sales Company Single customer view Business Department 1 Department 2 Customer Social Mobile Department 3 sensors

11 ANALYSE - DESCRIPTIVE Descriptive Analytics Data collection / IT infrastructure Data collection / IT infrastructure

12 DISCOVERING Descriptive Predictive

13 ANALYSE - PREDICTIVE Predictive Analytics Descriptive Analytics Data collection / IT infrastructure Data collection / IT infrastructure

14 ANALYSE - PREDICTIVE T Descriptive Analytics Crash Available data Today Predictions (unknown) T-1 Descriptive Analytics Data mining technique Independent variables Today T-1 Dependent Variable (known) Today

15 ANALYSE - PREDICTIVE Probability? Crash Churn Purchase Fraud Default

16 BIG DATA

17 BIG DATA Web/clickstream Social Text Predictive Analytics Descriptive Analytics Big Data Mobile sensors Data collection / IT infrastructure

18 BIG DATA - TELEMATICS Predictive performance Perfect Driving behaviour + Claim history (bonus malus, years without claim, ) Customer specific (age, driving experience, ) Car specific (kilowatt, brand, ) Claims +50% Random Baecke P. & Bocca L. (2017) - The value of vehicle telematics data in insurance risk selection processes

19 BIG DATA - TELEMATICS Descriptive driver feedback

20 BIG DATA -SOCIAL Network of Friends Pseudo-Network of Friends A B vs C D Tobback E. & Martens D. - Credit scoring for microfinance using Facebook data

21 BIG DATA -SOCIAL Predictive performance Perfect Default Friends Likes Comments + 29 traditional variables Random Tobback E. & Martens D. - Credit scoring for microfinance using Facebook data

22 BIG DATA -SOCIAL Predictive performance Perfect Friends Likes Comments + 29 traditional variables Default +50% Random Tobback E. & Martens D. - Credit scoring for microfinance using Facebook data

23 PRESCRIPTIVE ANALYTICS Prescriptive Analytics Predictive Analytics Descriptive Analytics Data collection / IT infrastructure

24 PRESCRIPTIVE ANALYTICS Human input replaced by business rules or optimisation algorithms

25 ROBO-ADVISER Risk assessment (based on 8 questions) Automatic portfolio suggestion (mainly ETFs) Automatic dividend reinvestments Automatic tax loss harvesting Convenient dashboard

26 ARTIFICIAL INTELLIGENCE Artificial intelligence Prescriptive Analytics Web/clickstream Text Social Predictive Analytics Descriptive Analytics Big Data Mobile sensors Data collection / IT infrastructure

27 ARTIFICIAL INTELLIGENCE 1997 Chess Kasparov vs IBM Deepblue 2011 Jeopadry Ken, Brad vs IBM Watson

28 COGNITIVE COMPUTING Descriptive Predictive Analytics Analytics : Jeopardy Human vs machine

29 COGNITIVE COMPUTING Descriptive Predictive Analytics Analytics Create corpus Human Q&A training

30 COGNITIVE COMPUTING Descriptive Predictive Analytics Analytics US Cities

31 COGNITIVE COMPUTING Descriptive Predictive Analytics Analytics Not perfect yet

32 CUSTOMER CARE Descriptive Predictive Analytics Analytics Phase 1: Phase 2:

33 ARTIFICIAL INTELLIGENCE 1997 Chess Kasparov vs IBM Deepblue 2011 Jeopadry Ken, Brad vs IBM Watson 2016 Go Lee Sedol vs Google Alpha Go

34 DEEP LEARNING 100 billion neurons Axon Neuron + able to detect very complex patterns -Needs a lot of data observations to be trained well

35 DEEP LEARNING 0 no damage 0.25 light damage 0.5 moderate damage 0.75 heavy damage 1 total destruction Karoon Rashedi Nia (2017) - Automatic Building Damage Assessment Using Deep Learning and Ground-Level Image Data

36 DEEP LEARNING Facial analytics to predict life expectancy

37 DEEP LEARNING

38 BIG DATA

39 Algorithms Algorithms

40 ADOPTION BARRIERS ENABLERS People Culture Tools, systems, processes Customer, privacy

41 CUSTOMER & PRIVACY

42 Interest to collaborate? Prof. dr. Philippe Baecke Associate Professor of Marketing Programme director: Creating Business Value with Big Data Strategic Data Driven Marketing Vlerick Business School Reep Gent, Belgium be.linkedin.com/in/philippebaecke philippe.baecke@vlerick.com Thank you