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

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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

1. Executive Summary
Key Findings
2. Market Definitions
Definitions
3. 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
4. Introduction—Machine Learning
Machine Learning—Definition and Techniques
ML in Financial Services Value Chain
Smarter Decisions—Realigning Output
ML—Implementation in Financial Services
5. 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
6. 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
7. Adoption of Machine Learning in Financial Services—Drivers and Restraints
Market Drivers
Market Restraints
8. 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
9. The Last Word
3 Big Predictions
Legal Disclaimer
10. The Frost & Sullivan Story


List of Figures & Charts

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



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