Muutke küpsiste eelistusi

Fintech: The New DNA of Financial Services [Pehme köide]

3.89/5 (18 hinnangut Goodreads-ist)
,
  • Formaat: Paperback / softback, 546 pages, kõrgus x laius: 240x170 mm, kaal: 1046 g, 70 Illustrations, black and white
  • Ilmumisaeg: 03-Dec-2018
  • Kirjastus: De Gruyter
  • ISBN-10: 1547417080
  • ISBN-13: 9781547417087
Teised raamatud teemal:
Fintech: The New DNA of Financial ServicesSuurem pilt
  • Formaat: Paperback / softback, 546 pages, kõrgus x laius: 240x170 mm, kaal: 1046 g, 70 Illustrations, black and white
  • Ilmumisaeg: 03-Dec-2018
  • Kirjastus: De Gruyter
  • ISBN-10: 1547417080
  • ISBN-13: 9781547417087
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781547417087.html

This extraordinary book, written by leading players in a burgeoning technology revolution, is about the merger of finance and technology (fintech), and covers its various aspects including data science, technology, algorithms and how they impact each discipline within the financial services industry. It is an honest and direct analysis of where each segment of financial services will stand in the next few years.

Fintech: The New DNA of Financial Services provides an in-depth introduction to fintech and a broad understanding of the various fintech areas and terminology. Contributions from fintech innovators discuss banking, insurance and investment management applications, cutting edge products, as well as delve into applications in the health and consumer tech sectors. Readers will also learn about the legal and human resource implications of fintech in the future.

Part 1: An Overview of Fintech 1(34)
Chapter 1 Fintech and the Disruption of Financial Services
3(6)
The Ecosystem of Financial Services Intermediaries
3(2)
Basic Skills of the Fintech Revolution
5(1)
The Evolution of Financial Services Activities
5(2)
Compliance Processes
5(1)
Transaction Processing
5(1)
Insurance Calculations
6(1)
Investment and Risk Management Decisions
6(1)
Investment Solutions
6(1)
Financing Solutions
6(1)
The Journey of Evolution for Financial Services Organizations
7(2)
Chapter 2 Fintech in the Context of the Digital Economy
9(12)
Fintech Startups
9(1)
The 10 Stacks of a Digital Economy
10(8)
#1 Trusted Digital Identity
10(1)
#2 Trusted Digital Data
11(1)
#3 Customer Consent Architecture
11(1)
#4 Public Infrastructure for the Digital Economy
12(1)
#5 Data Residency Policies
13(1)
#6 Scaled Computing
13(1)
#7 Open Architecture
13(2)
#8 Digital Literacy, Talent and Entrepreneur Growth
15(1)
#9 Policy Making by Experimentation and Empirical Data
16(1)
#10 Cyber-security
16(2)
The Impact of Policymaking on the Journey of Fintech
18(1)
Challenges in the Fintech Journey
19(2)
Implementation of New Technology to Develop New Products and Services
19(1)
Deployment of New Products and Services within the Physical Ecosystem
19(1)
Speed of Adoption and Consumption of Fintech-based Products and Services
19(2)
Chapter 3 The Landscape of Fintech
21(14)
Why Is the World Interested in Fintech Now?
21(2)
Landscape and Trends
23(5)
Funding Trends
23(4)
Investor Trends
27(1)
How Banks Are Responding
28(5)
Banks Are Driving a "Technology-First" Agenda
29(2)
The Move Toward Greater Digitalization and Ecosystem Platforms
31(2)
Conclusion
33(2)
Part 2: Enablers of a Digital Economy 35(190)
Chapter 4 Digital Identity
37(12)
Why We Need Digital Identities
37(1)
Components of Digital Identity
38(1)
The Market for Personal Digital Identity Management
39(1)
Problems Solved by Digital Identity
39(1)
Enhanced Efficiency and Reduced Risk
39(1)
Power Complete Digitization
40(1)
The Impact of Digital Identity on Business Models
40(1)
Issues Concerning Digital Identity Management
41(1)
A Digital Identity Application Use Case: Aadhaar
41(8)
Why Was Aadhaar Conceived?
41(1)
The Evolution of Aadhaar
42(2)
Aadhaar-based Authentication
44(1)
The Role of Technology in the Authentication Process
45(1)
The Issues and Concerns Regarding Aadhaar
46(1)
How Successful Has Aadhaar Been?
46(3)
Chapter 5 The Importance of Cloud Computing
49(22)
It Can Rain Too
49(1)
Governmental Access
50(1)
The Cloud and Data Science
50(1)
The Cloud Services
51(2)
The Private Cloud
53(1)
The Public Cloud
54(1)
Hybrid Clouds
54(1)
Why Implement a Cloud?
55(1)
Why Not Use the Cloud?
56(4)
Mitigating Risk
58(2)
The Cloud Life Cycle
60(1)
Cloud Architecture
61(5)
Serverless Computing
63(1)
DevOps
63(3)
The DevOps Maturity Model
66(1)
Compliance
66(1)
Cloud Security
67(4)
Levels of Security
68(3)
Chapter 6 Data Science and Big Data
71(34)
Applications of Data Science and Big Data
72(4)
What Is Data Science?
73(2)
Machine Learning vs. Artificial Intelligence
75(1)
What Data Science Is Not
75(1)
What Is Big Data?
76(1)
Data Science and Big Data in Industry Practice
76(20)
Step 1-Defining the Problem
77(2)
Step 2-Data Collection
79(3)
Step 3-Data Preparation
82(6)
Step 4-Modeling
88(6)
Step 5-Experimentation
94(2)
Big Data Technology Stack
96(5)
Data Collection Toolkit
97(2)
Data Processing Toolkit
99(1)
Data Workflow Toolkit
100(1)
Challenges and Lessons from Data Science Projects
101(1)
Conclusion
102(3)
Chapter 7 Blockchain and Distributed Ledger Technology 2.0
105(14)
Emerging from the Shadows of the Internet
106(2)
Blockchain Technology Architecture
108(2)
Blockchain-How It Works
110(2)
Private vs Public Blockchains-A Closer Look
112(4)
Private Blockchain Technology-What Is Next?
116(1)
The Way Forward with Blockchains
116(3)
Chapter 8 Use Cases of Blockchain Technology in Financial Services
119(18)
What Is Currently at Stake?
119(2)
Use Case: Payments
121(2)
Problem Statement
121(1)
Application of Blockchain Technology
122(1)
Implementation Examples
123(1)
Use Case: Workflow Tracking and Supply Chain Management
123(3)
Problem Statement
123(1)
Application of Blockchain Technology
124(1)
Implementation Examples
125(1)
Use Case: KYC (Know Your Customer) Process
126(2)
Problem Statement
126(1)
Application of Blockchain Technology
126(2)
Implementation Examples
128(1)
Use Case: Tokenization of Investment, Consumption and Physical Assets
128(3)
Problem Statement
128(1)
Application of Blockchain Technology
129(1)
Implementation Examples
130(1)
Use Case: Exchanges and Post-trade Settlement
131(2)
Problem Statement
131(1)
Application of Blockchain Technology
131(2)
Implementation Examples
133(1)
Use Case: Parametric Insurance
133(1)
Problem Statement
133(1)
Application of Blockchain Technology
133(1)
Implementation Examples
134(1)
Looking Ahead
134(3)
Chapter 9 Cryptoassets
137(18)
Introducing Cryptoassets
137(4)
What Is a Cryptoasset?
137(2)
Traditional Financial Assets vs. Cryptoassets
139(1)
Cryptoasset Terminology
139(2)
Evolution of Cryptoassets
141(4)
Blockchain 1.0
141(2)
Blockchain 2.0
143(1)
Blockchain 3.0
144(1)
Real World Assets on the Blockchain
145(1)
Initial Coin Offerings (ICO): A New Way of Fundraising?
145(2)
Drawbacks of Cryptoassets: "Blockchain, not Bitcoin"
147(2)
Why Are Tokens Necessary?
148(1)
Possibilities of Tokenization
148(1)
The Cryptoasset Ecosystem
149(3)
Cryptofinance
150(1)
Case Study: QCP Capital and Trading Cryptoassets
150(1)
Online Communities
151(1)
Emerging Blockchain Hubs
152(1)
What's Next?
152(3)
Chapter 10 Open Banking: Digital Payments Systems
155(24)
A Changing Landscape
155(4)
What Is Open Banking?
156(1)
Open Banking Regulation and Adoption
157(2)
Essentials for Operating in the Open Banking Space
159(7)
Compliance and Competitive Threats
159(3)
Open Banking Adoption Challenges
162(2)
Open APIs
164(2)
Leveraging the Open Banking/Digital Payments Opportunity
166(8)
API Strategy
166(2)
Collaboration and Aggregation
168(3)
Open Banking Ecosystem
171(1)
Life Stage Management
172(2)
Digital Payments for the Digital Customer
174(5)
New Technologies Positively Impact Customers
174(2)
Changes to the Payments Landscape
176(3)
Chapter 11 Theories of Artificial Intelligence and : Machine Learning
179(26)
AI Techniques and Tools
181(18)
Search Algorithms
181(3)
Genetic Algorithm
184(3)
Artificial Neural Networks (ANNs)
187(1)
Fuzzy Logic Systems (FLS)
188(1)
Natural Language Processing (NLP)
189(1)
Expert Systems (ES)
190(2)
Robotics
192(1)
Reinforcement Learning (RL)
193(2)
Deep Learning (DL)
195(4)
AI in Financial Services: Present and Future Applications
199(6)
AI Algorithmic Trading
200(1)
Robo-Advisors
200(1)
Chatbots
201(1)
Fraud Detection
201(1)
Loan/Insurance Underwriting
201(4)
Chapter 12 A Practical Approach to Machine Learning (ML) and Artificial Intelligence (AI)
205(20)
Are AI and Machine Learning the Same Thing?
205(7)
Machine Learning Covers a Lot
206(1)
Neural Networks Are a Special Type of Machine Learning
207(2)
Should We Always Use Neural Networks?
209(2)
Reasons Not to Use Neural Networks Every Time
211(1)
The Simple Alternatives to Neural Networks
212(2)
Linear Is Straightforward
212(1)
Trees Are Understandable
213(1)
Putting It into Practice
214(1)
Tools of the Trade
214(1)
Frameworks to Focus on Solutions
214(1)
Creating Your First ML Solution
215(4)
Automation of Automation
219(8)
How Software Is Currently Created
220(1)
How (Simple) Machine Learning Can Help You Create Better Software
221(1)
Teach Logic to Your Software
221(1)
Automation of Machine Learning Itself
222(3)
Part 3: Fintech Innovations and Disruptions 225(216)
Chapter 13 Disruption in Asset Servicing
227(14)
The Asset Management Sector Is Ripe for Disruption
227(1)
Disruptive Technologies
228(8)
Blockchain
228(4)
Robotic Process Automation (RPA)
232(2)
Cognitive Technology
234(2)
Preparing for the Wave of Disruption
236(1)
Possible Outcomes
237(4)
Chapter 14 Disruption in the Capital Markets
241(18)
How Did We Get Here?
242(2)
What's Happening Now?
244(10)
Equities
245(2)
Foreign Exchange
247(2)
Fixed Income
249(3)
Open Banking
252(2)
The Future
254(2)
Case Study-Saxo
256(3)
Chapter 15 Disruption in Investment Management
259(16)
The Transition Toward Outcome-Oriented Absolute Return Products
260(1)
Transition Toward Allocation as the Central Investment Problem
261(1)
Implementation of Multiple Concurrent Allocation Investment Processes
262(1)
Diversity in Allocation Investment Processes
262(2)
Change in the Asset Owner Portfolio Structure
264(1)
Change in the Asset Owner Portfolio Process
265(1)
Redefinition of the Concept of Asset Class Risk Premium
266(1)
The Transition to an Exposure-based Framework
267(1)
Creation of Large Number of Indices as Passive Product Benchmarks
268(1)
Redefinition of Risk Measures to Include Intra-horizon Risk
269(1)
Asset Management Distribution to Change from Product-centric to Client-centric
270(1)
Incorporation of Technology in the Investment Model
271(2)
Implications of the New Investment Model
273(2)
Chapter 16 Alternative Data in Portfolio Management
275(22)
A Paradigm Shift in Active Investing
275(5)
The History: 30 Years of Quantitative Investing
276(4)
The Future of Active Investing-Big Data Evolution
280(2)
Using Satellite Imagery Data in Sales Forecasting
282(5)
A Brief Introduction
282(4)
An Example: Chipotle Mexican Grill, Inc. (CMG)
286(1)
Natural Language Processing and Management Presentation
287(7)
Readability Index and Language Complexity
289(2)
Sentiment or Tone Analysis Based on Lexicons
291(3)
Conclusion
294(3)
Chapter 17 Online Marketplace Lending
297(12)
US
297(1)
China
297(1)
Institutional Investors
298(1)
New Borrowers
298(1)
Requirements for Online Marketplace Lending
299(1)
Borrower Data
299(1)
Historical Default Rates
299(1)
Risk Framework
300(1)
Machine Learning Platform
300(1)
Case Study: Applying Machine Learning to Online Marketplace Lending
300(3)
Business Case and Data Model
301(1)
Identifying Patterns
301(1)
Deployment and Iteration
302(1)
Comparison of US and Chinese Online Marketplace Lending
303(6)
Customer Segmentation
304(1)
Customer Acquisition
305(1)
Charge-offs
305(1)
Cost of Funding
306(3)
Chapter 18 Lending and Crowdfunding
309(18)
Crowdfunding and Its Entry into Lending
309(1)
Importance of Debt-based Crowdfunding
310(1)
Lower Cost to Serve
310(1)
Greater Accuracy and Access
310(1)
Impact across the Globe and in Southeast Asia
311(1)
Digital Crowdfunding Technology
312(2)
The Early Days
313(1)
Moving Past the Minimum Viable Product into Growth
313(1)
Key Pieces of Technology in a Digital Crowdfunding Firm
314(5)
Applications Developed at Funding Societies/Modalku
317(2)
A Competitive and Evolving Market
319(4)
Evolution in Digital Crowdfunding Technology
320(2)
From Crowdfunding to Digital Financing Everywhere
322(1)
Conclusion
323(4)
Chapter 19 Robo-Advisory and Multi-Asset Allocation
327(20)
What Is Robo-Advisory About?
327(3)
Pain Points Addressed by Robo-Advisory
328(1)
A Brief History of Robo-Advisory (including B2B vs B2C vs B2B2C)
329(1)
Why Is Robo-Advisory Important?
330(2)
Bringing Significant Changes to the Wealth Management Industry
330(1)
Helping People in Personal Financial Management
330(2)
How Does Robo-Advisory Work?
332(4)
Introduction to Robo-Advisors' Technology
332(1)
Digital Financial Advice
332(1)
Automated Fund Management
333(1)
Limitations
334(1)
How Can Robo-Advisors Reduce Costs?
335(1)
How Can Robo-Advisors Improve Quality?
335(1)
Applications: The StashAway Case
336(4)
Competitive Landscape
337(1)
The US Landscape
337(2)
The Rest of the World
339(1)
How Is This Technology Likely to Evolve?
340(2)
Is Robo-Advisory Here to Stay?
340(1)
How Will the Products and Technology Change?
341(1)
Implications for Current Business Models and Processes
342(2)
Impact on Existing Models
342(1)
Options Available for Incumbents
343(1)
How to "Skills Future-proof" the Workforce
343(1)
Conclusions
344(3)
Chapter 20 WealthTech
347(26)
Asia and Greater China
347(1)
WealthTech versus Traditional Wealth Management
348(1)
The Changes Taking Place in Asia
349(8)
Customer Demand
350(1)
Regulatory Change
351(2)
New Product Evolution
353(1)
Operational Efficiency
354(3)
The Cutting Edge
357(6)
Artificial Intelligence
357(2)
Big Data
359(2)
Blockchain
361(1)
Cloud Computing
362(1)
How Wealth Management Business Models Are Changing
363(6)
The Rise of Bionic Advisory
363(2)
Client Acquisition and Engagement
365(1)
Provision of Advice
366(1)
Looking to China: New Ecosystems
367(2)
Looking into the Digital Crystal Ball
369(4)
Chapter 21 RegTech: We are coming out of Fintech!
373(22)
Putting RegTech in Context
373(3)
RegTechs to the Rescue
374(2)
Ecosystem and Trends
376(7)
Landscape of RegTechs
376(5)
Ecosystem: Who Are the Stakeholders and Why?
381(2)
FI Applications & Adoption
383(4)
Case Example: Silent Eight
384(1)
Adoption
385(1)
i Challenges to Adoption
386(1)
The Future: At the Tipping Point of Adoption
387(6)
RegTech Associations
392(1)
Conclusion
393(2)
Chapter 22 Digitalizing the Client Lifecycle and KYC/AML with RegTech
395(26)
What's Wrong with Client Lifecycle Management Today?
396(4)
The Future of CLM Is Digitalization
400(17)
What Does Digitalization Actually Mean?
400(1)
So Where Are We in Tech Evolution?
400(1)
Pivot versus Disruptive
401(1)
Pivot Innovation
402(2)
Centralize for Re-Use
404(5)
Embracing Disruptive Technologies
409(2)
Five Ways to Apply Disruptive Technology to AML/KYC
411(3)
Robotics Process Automation (RPA)
414(2)
Key Success Factors for Digitalizing Client Lifecycle Management
416(1)
Conclusion
417(4)
Chapter 23 InsurTech: Using China as an Example
421(20)
Introduction
421(1)
What Is InsureTech?
421(2)
Trends and Challenges
423(1)
Technology Enablers and Applications
424(9)
Digital
425(1)
Artificial Intelligence and Big Data
426(3)
Blockchain
429(3)
Mobile & IoT
432(1)
Market Landscape
433(4)
Future of InsurTech
437(4)
Part 4: The Impact of Fintech 441(48)
Chapter 24 Technology and the Dislocation of the Fast Moving Consumer Goods Industry
443(10)
Chapter 25 Legal Implications of Fintech
453(22)
The Challenge: How and Why to Regulate Fintech
453(1)
The Digital Customer Journey
454(14)
Due Diligence
456(1)
Marketing and Design Considerations
457(4)
Pricing/Quoting
461(1)
Advisory Services
462(1)
Purchasing the Product or Service
463(2)
Data Storage
465(2)
Customer Complaints
467(1)
Ongoing Customer Relationship
467(1)
Smart Nations: Collaboration and Competition Between Jurisdictions
468(3)
Collaboration
468(1)
Competition
469(2)
Future Developments
471(1)
Conclusions
472(3)
Chapter 26 Talent Development and HR Implications for Fintech
475(14)
Talent Development Infrastructure and Pipeline in Singapore
476(2)
Developing Fintech Capabilities
476(1)
Workforce Demand and Supply Gap
476(1)
International Talent Attraction Targeting Fintech Start-ups and Experts
477(1)
Talent Development in Institutions of Higher Learning
477(1)
Talent Development through Trade Associations, Organizations, and Government Agencies
478(1)
Understanding Fintech Roles, Skills, Sentiments, and Priorities of the Banking and Financial Community
478(2)
Traditional Technology and Operations Roles versus Emerging Fintech Roles
478(1)
Contextual Application and Disruptive Innovation Impact: Differential Factors for Fintech Skills in Demand
479(1)
Institute of Banking and Finance (IBF) Future-Enabled Skills
480(2)
Industry Sentiments and Priorities
480(2)
An Integrated and Multimodal Approach for Effective Fintech Skills Development
482(3)
Recognizing and Redefining the Components of a Fintech Skill
482(1)
Developing Fintech Skills
483(2)
Human Resources Trends for Fintech Talent in the Near Future
485(1)
Conclusion
486(3)
Index 489
Pranay Gupta is Adjunct Associate Professor at the College of Business (Nanyang Business School) in Singapore. He is Managing Director, Multi-Asset Strategies & Solutions at Blackrock Inc., New York, and has over 25 years of experience in Europe, UK, US, and Asia in managing portfolios across all liquid asset class investments. Prior to this, as Head of Multi-Asset Strategies at Fullerton Fund Management (a subsidiary of Temasek Holdings), Pranay was responsible for strategic and tactical allocation globally for the liquid assets portfolio. Previously, he was Chief Investment Officer at Lombard Odier and ING Investment Management Asia Pacific, where he led investment teams of over 300 investment professionals across 12 countries, to manage over US$85bn of institutional and retail assets. Pranay has held senior positions at Axial Investment Management, London, managing a US$55bn closed life insurance portfolio at APG Investments, Netherlands managing a US$25bn multi-asset multi-strategy fund, and Societe Generale, Hong Kong and JP Morgan Investment Management, New York. As a Mechanical Engineer from IIT Delhi specializing in CAD/CAM, Pranay designed automobile suspension systems for Suzuki Motor Co. Japan, fluid dynamic modelling for Dowell Schlumberger, and production planning control systems for missile systems. Using large scale databases, Pranay has designed, developed and implemented advanced analysis and portfolio management systems for various organizations., which have been used in managing over US$400bn in assets, and to re-engineer investment processes and creating customized client solutions for asset owners of all sizes and sophistication. Pranay has served on the Advisory Board of Stashaway, the first licensed retail Roboadvisor in Singapore, and has been a member of the Board of Trustees of the CFA Institute Research Foundation as the Chairman of the Finance and Investment Committee.

Tze Minn Mandy Tham is Assistant Professor of Finance, Academic Director, Master of Science in Wealth Managemen and Sino Suisse Fellow at the Singapore Management University. She obtained her Ph.D. in Business Administration (Finance) at the University of Michigan in 2008.