Analysis of the Impact Machine Learning will have on Bank Operations and Payments, 2017 Study

This report provides an analysis of the current state of machine learning with a deep dive into existing technologies and breakthroughs that represent new deployment opportunities, such as deep learning, adversarial networks, and transfer learning. The report identifies the incredible breadth of business processes that are impacted by machine learning and recommends areas that should be targeted first. It recommends an approach to enterprise deployment and identifies the important differences between deploying a machine learning solution and deploying traditional software and provides recommendations that will prevent silos of machine learning that would limit the ability of machine learning tools to collaborate.

This report provides an analysis of the current state of machine learning with a deep dive into existing technologies and breakthroughs that represent new deployment opportunities, such as deep learning, adversarial networks, and transfer learning. The report identifies the incredible breadth of business processes that are impacted by machine learning and recommends areas that should be targeted first. It recommends an approach to enterprise deployment and identifies the important differences between deploying a machine learning solution and deploying traditional software and provides recommendations that will prevent silos of machine learning that would limit the ability of machine learning tools to collaborate.

The report, "Bringing AI into the Enterprise: A Machine Learning Primer" provides an analysis of the impact machine learning will have on bank operations and payments and how it is already shifting consumer behavior. Consumers increasingly expect their smartphone will answer their questions, give them directions, and warn them when accidents will slow them down. Over time, machine learning will become as prevalent within banks as software systems are today. Eventually every software application will be reconstructed to accommodate machine learning - it's simply a matter of time.

Highlights of the report include:

Key Topics Covered:

1. Executive Summary

2. Introduction

3. Bringing Machine Learning into the Enterprise

4. Fraud Solutions

5. Machine Learning Creates New Consumer Interaction Models

6. Machine Learning and AI Terminology

7. The Statistical Roots of Machine Learning

8. Common Solutions: Prediction, Categorization, and Regression Analysis

9. Machine Learning Can Solve Problems That Traditional Software Can't

10. Machine Learning in the Cloud

11. From Neurons to Neural Networks: Constructing the Machine Learning Algorithm

12. The Breakthrough: Deep Learning and Convolutional Networks

13. Deployment of Machine Learning Solutions Is Specialized

14. The Limitations of Machine Learning

15. Are Machine Learning Solutions Patentable?

16. Recommendations

List of Tables
Table 1: Typical Third-Party Packages Adopting Machine Learning for Deployment in Financial Institutions
Table 2: Pretrained and Packaged Machine Learning Models Available in the Cloud

List of Figures
Figure 1: The Confusing Vocabulary of Machine Learning
Figure 2: A Regression Analysis vs. a Machine Learning Analysis of Customer Data
Figure 3: The Difficulty of Recognizing Handwritten Numbers
Figure 4: Fintech Startups That Utilize Machine Learning
Figure 5: Activating a Neuron Is a Complex Event
Figure 6: Functions Enable Neurons to Activate Based on a Probability
Figure 7: Interconnected Nodes in an Artificial Neural Network (ANN) Are the Brain of Machine Learning
Figure 8: A Complex Web of Neurons Makes Up the Google Inception Image Recognition Engine
Figure 9: A Machine Learning Image Recognition Test
Figure 10: Deep Learning and Convolutional Neural Networks
Figure 11: Establishing a Working Model and Continuing to Train It Has an Impact on Deployment

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/research/wk4534/bringing_ai_into

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