Disruption in Global Financial Services, 2017 - Realigning Customer Engagement with Predictive Analytics and Customization

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14 Aug, 2017, 23:00 GMT

DUBLIN, August 14, 2017 /PRNewswire/ --

The "Disruption in Global Financial Services, 2017-Machine Learning is Imperative" report has been added to Research and Markets' offering.

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.

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.

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.

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

Key Topics Covered:

1. Executive Summary

2. Market

3. Evolution of the Financial Services Industry

  • Obsolete Approach to Decision Making
  • IT Needs to Move Beyond Maintenance
  • Challenges Faced by IT Departments
  • Driving Investment in Technology
  • Big Data and Analytics (BDA) Adoption Trend
  • 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
  • Google
  • IBM
  • New Ecosystem-Contribution of Telecom Companies
  • Orange
  • Swisscom
  • New Ecosystem-Contribution of ML Start-ups
  • Onfido
  • Darktrace
  • AdviceRobo
  • Rasa.ai
  • Klarna
  • New Ecosystem-Contribution of IT Companies
  • Infosys
  • 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

For more information about this report visit https://www.researchandmarkets.com/research/56v6l8/disruption_in

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