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

  • ID: 4370510
  • Report
  • July 2017
  • Region: Global
  • 74 Pages
  • Frost & Sullivan

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  • AdviceRobo
  • Darktrace
  • Google
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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.

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1. Executive Summary

  • Key Findings

2. Market

  • 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

  • ML in Financial Services-TechWheel Critical to Ecosystem
  • Technology Driven Ecosystem-Participants Collaborate
  • New Ecosystem-Contribution of Tech Majors
  • Company Profile-Google
  • Company Profile-IBM
  • New Ecosystem-Contribution of Telecom Companies
  • Company Profile-Orange
  • Company Profile-Swisscom
  • New Ecosystem-Contribution of ML Start-ups
  • Company Profile-Onfido
  • Company Profile-Darktrace
  • Company Profile-AdviceRobo
  • Company Profile-Rasa.ai
  • Company Profile-Klarna
  • New Ecosystem-Contribution of IT Companies
  • Company Profile-Infosys
  • Company Profile-SAP
  • Stakeholder Contribution Analysis

6. Machine Learning

  • 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

  • ML Adoption in Financial Services-Market Drivers
  • Drivers Explained
  • ML Adoption in Financial Services-Market Restraints
  • Restraints Explained

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
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  • AdviceRobo
  • Contribution of IT Companies
  • Contribution of Telecom Companies
  • Darktrace
  • Google
  • IBM
  • Infosys
  • Klarna
  • New Ecosystem-Contribution of ML Start-ups
  • Onfido
  • Orange
  • Rasa.ai
  • SAP
  • Swisscom
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