Disruption in Global Financial Services, 2017—Machine Learning is Imperative

Disruption in Global Financial Services, 2017—Machine Learning is Imperative

Realigning Customer Engagement with Predictive Analytics and Customization

RELEASE DATE
21-Jul-2017
REGION
Global
Research Code: MD13-01-00-00-00
SKU: IT03421-GL-MR_20444
$3,000.00
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Description

Technology is disrupting the financial services industry. Also termed fintech, tech-enabled products and services in the industry are further enhanced by advanced technologies such as cloud, IoT, analytics, artificial intelligence (AI), and machine language (ML). This research service explores the impact of ML on the financial services industry. The objectives of the study are to understand the following:
• The evolution of the financial services industry
• ML and its impact on the financial services value chain
• The ML ecosystem and different stakeholders
• ML solutions and their implementation
• Providers and use cases of ML

Shared economy and connected devices have made Big Data ubiquitous, and analytics has improved the outcomes of data analysis. To ensure that all the available data is utilized to come up with insights, an increase in the adoption of ML is expected, which would several processes and increase the ease of data gathering and analysis. Companies are experimenting with and adopting new ML-enabled business models, solutions, and services, and entering new markets. Fraud prevention, robo-advisory services and credit scoring are some of the largest growth opportunities for the application of ML in financial services. As proofs of concept and use cases come to the fore, myriad applications of ML are expected to alter the financial services industry as it is known today.

Different stakeholders in the industry use diverse methods to implement it, including the following:
• Start-ups are introducing innovation into the system by offering financial services that are cost-effective, faster, automated, and take into
account consumer behaviour.
• Large tech companies such as Amazon and Apple realize the potential and are already offering payment solutions to consumers.
• IT companies responsible for the vast IT systems in financial institutions are upgrading their offerings with innovative and advanced
technologies.
• With connectivity playing an important role in creating an ecosystem that makes financial services available to consumers 24x7, telecom
companies are also increasing their presence by updating their offers and including ML.

Following are some of the key questions the study answers:
• What are the challenges within the financial services industry that ML can help mitigate?
• What are the current trends in ML adoption?
• What drivers will encourage ML in financial services?
• What are the restraining factors that may affect the growth of ML adoption?
• What are the growth opportunities for ML in financial services?

ML in financial services is forecast to become mainstream in a few years, as many factors are driving adoption. Notwithstanding all the challenges, the importance of ML is clear, and the inclusion imperative for financial services to successfully meet consumer demands and create an efficient and effective system.

Table of Contents

Key Findings

Definitions

Financial Services—Obsolete Approach to Decision Making

Financial Services—IT Needs to Move Beyond Maintenance

Financial Services—Challenges Faced by IT Departments

Financial Services—Driving Investment in Technology

Financial Services—Big Data and Analytics (BDA) Adoption Trend

Financial Services—Technology-enabled Evolution

Machine Learning—Definition and Techniques

ML in Financial Services Value Chain

Smarter Decisions—Realigning Output

ML—Implementation in Financial Services

ML in Financial Services—TechWheel Critical to Ecosystem

Technology Driven Ecosystem—Participants Collaborate

New Ecosystem—Contribution of Tech Majors

Company Profile

New Ecosystem—Contribution of Telecom Companies

Company Profile

New Ecosystem—Contribution of ML Start-ups

Company Profile

New Ecosystem—Contribution of IT Companies

Company Profile

Stakeholder Contribution Analysis

ML Solutions for Financial Services

ML Solutions—Applications in Financial Services

Predictive Analytics—Trends

Fraud Detection and Identity Management—Trends

Chatbots—Trends

Pattern Recognition—Trends

Information Discovery and Extraction—Trends

ML Technology Trends in Financial Services

5 Major Growth Opportunities

Growth Opportunity 1—Fraud Prevention

Growth Opportunity 2—Credit Scoring

Growth Opportunity 3—Robo-advisory

Growth Opportunity 4—RegTech

Growth Opportunity 5—Cybersecurity

Strategic Imperatives for Success and Growth

3 Big Predictions

Legal Disclaimer

List of Figures
  • 1. Financial Services: Stakeholder Potential to Impact Industry, Global, 2017
  • 2. ML Adoption in Financial Services: Key Market Drivers, Global, 2017–2021
  • 3. ML Adoption in Financial Services: Key Market Restraints, Global, 2017–2021
List of Charts
  • 1. Financial Services: Challenges Faced by the Sector, Global, 2017
  • 2. Financial Services: Strategic Adoption of Technology, Global, 2017
  • 3. Financial Services: IT Department Challenges, Global, 2016–2018
  • 4. Financial Services: IT Expenditure Drivers, Global, 2016–2018
  • 5. Financial Services: Use of Big Data and Analytics, Global, 2016–2018
  • 6. Financial Services: Technology Introduces New Trends, Global, 2017
  • 7. Financial Services: ML-enabled Decision-making to Achieve Business Goals, Global, 2017
  • 8. Financial Services: Sample Implementation Scenario of Using ML, Global, 2017
  • 9. Financial Services: ML Ecosystem*, Global, 2017
  • 10. Financial Services: Collaboration between Ecosystem Participants, Global, 2017
  • 11. Financial Services: Accelerator and Partnership Use Cases, Global, 2017
  • 12. Financial Services: Investment and Acquisition Use Cases, Global, 2017
  • 13. Financial Services: Tech Majors Participation Trends, Global, 2017–2025
  • 14. Financial Services: Telecom Company Participation Trends, Global, 2017–2025
  • 15. Financial Services: ML Start-up Companies Participation Trends, Global, 2017–2025
  • 16. Financial Services: Participation Trends of IT Companies, Global, 2017–2025
  • 17. Financial Services: ML Solutions, Global, 2017
  • 18. Financial Services: Applications across Functions, Global, 2017
  • 19. Financial Services: Predictive Analytics-Trends, Global, 2017
  • 20. Financial Services: Fraud Detection and Identity Management Trends, Global, 2017
  • 21. Financial Services: Chatbots Trends, Global, 2017
  • 22. Financial Services: Pattern Recognition Trends, Global, 2017
  • 23. Financial Services: Information Discovery and Extraction Trends, Global, 2017
  • 24. Financial Services: Current & Future Global Trends
Related Research
Technology is disrupting the financial services industry. Also termed fintech, tech-enabled products and services in the industry are further enhanced by advanced technologies such as cloud, IoT, analytics, artificial intelligence (AI), and machine language (ML). This research service explores the impact of ML on the financial services industry. The objectives of the study are to understand the following: • The evolution of the financial services industry • ML and its impact on the financial services value chain • The ML ecosystem and different stakeholders • ML solutions and their implementation • Providers and use cases of ML Shared economy and connected devices have made Big Data ubiquitous, and analytics has improved the outcomes of data analysis. To ensure that all the available data is utilized to come up with insights, an increase in the adoption of ML is expected, which would several processes and increase the ease of data gathering and analysis. Companies are experimenting with and adopting new ML-enabled business models, solutions, and services, and entering new markets. Fraud prevention, robo-advisory services and credit scoring are some of the largest growth opportunities for the application of ML in financial services. As proofs of concept and use cases come to the fore, myriad applications of ML are expected to alter the financial services industry as it is known today. Different stakeholders in the industry use diverse methods to implement it, including the following: • Start-ups are introducing innovation into the system by offering financial services that are cost-effective, faster, automated, and take into account consumer behaviour. • Large tech companies such as Amazon and Apple realize the potential and are already offering payment solutions to consumers. • IT companies responsible for the vast IT systems in financial institutions are upgrading their offerings with innovative and advanced technologies. • With connectivity pl
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Table of Contents | Executive Summary~ || Key Findings~ | Market Definitions~ || Definitions~ | Evolution of the Financial Services Industry~ || Financial Services—Obsolete Approach to Decision Making~ || Financial Services—IT Needs to Move Beyond Maintenance~ || Financial Services—Challenges Faced by IT Departments~ || Financial Services—Driving Investment in Technology~ || Financial Services—Big Data and Analytics (BDA) Adoption Trend~ || Financial Services—Technology-enabled Evolution~ | Introduction—Machine Learning~ || Machine Learning—Definition and Techniques~ || ML in Financial Services Value Chain~ || Smarter Decisions—Realigning Output~ || ML—Implementation in Financial Services~ | Machine Learning Ecosystem~ || ML in Financial Services—TechWheel Critical to Ecosystem~ || Technology Driven Ecosystem—Participants Collaborate~ || New Ecosystem—Contribution of Tech Majors~ || Company Profile~ ||| Google~ ||| IBM~ || New Ecosystem—Contribution of Telecom Companies~ || Company Profile~ ||| Orange~ ||| Swisscom~ || New Ecosystem—Contribution of ML Start-ups~ || Company Profile~ ||| Onfido~ ||| Darktrace~ ||| AdviceRobo~ ||| Rasa.ai~ ||| Klarna~ || New Ecosystem—Contribution of IT Companies~ || Company Profile~ ||| Infosys~ ||| SAP~ || Stakeholder Contribution Analysis~ | Machine Learning Solutions and Implementation~ || ML Solutions for Financial Services~ || ML Solutions—Applications in Financial Services~ || Predictive Analytics—Trends~ || Fraud Detection and Identity Management—Trends~ || Chatbots—Trends~ || Pattern Recognition—Trends~ || Information Discovery and Extraction—Trends~ || ML Technology Trends in Financial Services~ | Adoption of Machine Learning in Financial Services—Drivers and Restraints~ || Market Drivers~ || Market Restraints~ | Growth Opportunities and Companies to Action~ || 5 Major Growth Opportunities~ || Growth Opportunity 1—Fraud Prevention~ || Growth Opportunity 2—Credit Scoring~ || Growth Opportunity 3—Robo-advisory~ || Growth Opportunity 4—RegTech~ || Growth Opportunity 5—Cybersecurity~ || Strategic Imperatives for Success and Growth~ | The Last Word~ || 3 Big Predictions~ || Legal Disclaimer~ | The Frost & Sullivan Story~
List of Charts and Figures 1. Financial Services: Stakeholder Potential to Impact Industry, Global, 2017~ 2. ML Adoption in Financial Services: Key Market Drivers, Global, 2017–2021~ 3. ML Adoption in Financial Services: Key Market Restraints, Global, 2017–2021~| 1. Financial Services: Challenges Faced by the Sector, Global, 2017~ 2. Financial Services: Strategic Adoption of Technology, Global, 2017~ 3. Financial Services: IT Department Challenges, Global, 2016–2018~ 4. Financial Services: IT Expenditure Drivers, Global, 2016–2018~ 5. Financial Services: Use of Big Data and Analytics, Global, 2016–2018~ 6. Financial Services: Technology Introduces New Trends, Global, 2017~ 7. Financial Services: ML-enabled Decision-making to Achieve Business Goals, Global, 2017~ 8. Financial Services: Sample Implementation Scenario of Using ML, Global, 2017~ 9. Financial Services: ML Ecosystem*, Global, 2017~ 10. Financial Services: Collaboration between Ecosystem Participants, Global, 2017~ 11. Financial Services: Accelerator and Partnership Use Cases, Global, 2017~ 12. Financial Services: Investment and Acquisition Use Cases, Global, 2017~ 13. Financial Services: Tech Majors Participation Trends, Global, 2017–2025~ 14. Financial Services: Telecom Company Participation Trends, Global, 2017–2025~ 15. Financial Services: ML Start-up Companies Participation Trends, Global, 2017–2025~ 16. Financial Services: Participation Trends of IT Companies, Global, 2017–2025~ 17. Financial Services: ML Solutions, Global, 2017~ 18. Financial Services: Applications across Functions, Global, 2017~ 19. Financial Services: Predictive Analytics-Trends, Global, 2017~ 20. Financial Services: Fraud Detection and Identity Management Trends, Global, 2017~ 21. Financial Services: Chatbots Trends, Global, 2017~ 22. Financial Services: Pattern Recognition Trends, Global, 2017~ 23. Financial Services: Information Discovery and Extraction Trends, Global, 2017~ 24. Financial Services: Current & Future Global Trends~
Author Deepali Sathe
Industries Information Technology
WIP Number MD13-01-00-00-00
Is Prebook No