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

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Aug 14, 2017, 19:00 ET

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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