Global Data Science Market Report 2020: Technology, Applications and Implementation for Financial Services

DUBLIN, May 29, 2020 /PRNewswire/ -- The "Global Data Science Market Report 2020: Technology, Applications and Implementation for Financial Services" report has been added to's offering.

The financial services sector can best leverage data science by improving technologies, increasing user acceptance/adoptions and by modernising regulatory frameworks as the industry becomes more attuned to the specific risks and opportunities presented by data analytics frameworks that are becoming more attuned to the specific risks and opportunities presented by biometric technologies.

In this report, the analyst looks at the various types of biometrics (static and behavioural), the high-level technological architecture that allows a firm to take full advantage of (among other things) the uniqueness, permanence and low circumvention of biometrics. BioAPIs and biometrics as a service are also reviewed.

In addition to reviewing the current industry adoption, the analyst looks at practical examples of financial services firms that have implemented biometrics to deal with specific use cases. The analyst then further classifies the leading biometric technology providers and suggests some questions that firms should ask when selecting a vendor.

Who Should Read this Report?

    --  Chief Information officer
    --  Chief Technology Officer
    --  Security Analyst
    --  Business Analyst
    --  Chief Operating officer
    --  Fintech business leader

Key Topics Covered:

1 Data Science Definitions
1.1 Data Science in Financial Services
1.2 Data Science Technologies
1.3 Data Science Technologies High-Level Functions
1.4 Data Science Systems

2 State of the Industry (Adoption Statistics, any Emerging Patterns)
2.1 Key Market Drivers
2.2 Key Market Restraints
2.3 Surveys
2.4 Data Science in Traditional Banking
2.5 Data Science in Insurance
2.6 Data Science in Fintech
2.7 Data Science in Asset Management and Others

3 Framework/Considerations for Use/Evaluation and Implementation
3.1 Data Science Framework
3.2 10 Challenges to Advanced Analytics
3.3 Data Quality Management (DQM)
3.4 How to Structure A Data Science Team
3.5 Data Science Model Evaluation

4 Data Science Standards
4.1 Other Standards

5 Data Science and the Law
5.1 EU Region
5.2 The UK
5.3 The US

6 The Future of Data Science
6.1 Automation of Data Processes for delivery of instantaneous analytics solutions
6.2 Evolution of Analytics platforms
6.3 New Skill Sets Required

7 Leading Companies Providing Data Science Services

Annex 1: The History of Data Science
Annex 2: List of Sources

Companies Mentioned

    --  ACT Operations Research
    --  Alibaba
    --  Apache
    --  Axa Insurance
    --  Ayata
    --  Birst
    --  BNP Paribas
    --  Fireside Analytics
    --  Goldman Sachs
    --  IBM
    --  Jaspersoft
    --  JPMorgan
    --  KEB Hana bank
    --  KNIME
    --  Kreditech
    --  Lavastorm
    --  LLamasoft
    --  Lumina
    --  MicroStrategy Inc
    --  Munich-Re
    --  NGDATA
    --  Paypal
    --  Pentaho
    --  Privatbank
    --  Progressive
    --  PWC
    --  Qlik
    --  River Logic
    --  Spago BI
    --  Tableau
    --  UBS

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