Duration
14 hours (usually 2 days including breaks)
Requirements
- Experience in the banking industry
Overview
Blockchain is a decentralized database system which stores data in ledgers distributed across many nodes.
In this instructor-led, live training, participants will learn the fundamentals of blockchain and its applications in the banking industry.
By the end of this training, participants will be able to:
- Understand blockchain, its foundations, and how it works
- Learn how blockchain can be applied to issues in the banking industry
- Explore the different blockchain platforms available for banking
Audience
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
Overview of the Blockchain Technology
- Understanding Distributed Ledgers
- What is Blockchain?
- Understanding How Blockchain Networks Work
- Understanding the Benefits of Blockchain
- Overview of Blockchain Use Cases
Understanding the Foundations of Blockchain Technology
- Symmetric Information
- Historical Integrity
- Extensible Security
- Democratic Authenticity
- Decentralization and Fault Tolerance
Exploring Blockchain Use Cases in Banking
- Clearing and Settlement
- Payments
- Cross-Border Transactions
- Share Trading
- Identity Management
- Syndicated Loans
- Smart Contracts
- Loyalty and Rewards
- Mobile Branches
- Mobile Money
Exploring the Different Blockchain Platforms for Finance
- Stellar
- About Stellar
- Benefits of Stellar
- Understanding How Stellar Works
- Learning Major Stellar Concepts
- Integrating Stellar
- Understanding the Stellar Network Structure
- Overview of Stellar Features
- Stellar Use Cases
- Linq by Nasdaq
- About Linq
- How Linq Works
- Benefits and Features of Linq
Case Study: Fraud Reduction
Case Study: Know Your Customer (KYC)
Case Study: Trading Platforms
Case Study: Payments
Challenges of Blockchain Applicability to Banking
Keys to Adoption of Blockchain in Banking
The Future of Banking with Blockchain
Summary and Conclusion
Closing Remarks
Duration
28 hours (usually 4 days including breaks)
Requirements
- Basic experience with R programming
- General familiarity with financial and banking concepts
- Basic familiarity with statistics and mathematical concepts
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use R to create deep learning models for banking
- Build their own deep learning credit risk model using R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
Understanding the Fundamentals of Artificial Intelligence and Machine Learning
Understanding Deep Learning
- Overview of the Basic Concepts of Deep Learning
- Differentiating Between Machine Learning and Deep Learning
- Overview of Applications for Deep Learning
Overview of Neural Networks
- What are Neural Networks
- Neural Networks vs Regression Models
- Understanding Mathematical Foundations and Learning Mechanisms
- Constructing an Artificial Neural Network
- Understanding Neural Nodes and Connections
- Working with Neurons, Layers, and Input and Output Data
- Understanding Single Layer Perceptrons
- Differences Between Supervised and Unsupervised Learning
- Learning Feedforward and Feedback Neural Networks
- Understanding Forward Propagation and Back Propagation
- Understanding Long Short-Term Memory (LSTM)
- Exploring Recurrent Neural Networks in Practice
- Exploring Convolutional Neural Networks in practice
- Improving the Way Neural Networks Learn
Overview of Deep Learning Techniques Used in Banking
- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentimental Analysis
Exploring Deep Learning Case Studies for Banking
- Anti-Money Laundering Programs
- Know-Your-Customer (KYC) Checks
- Sanctions List Monitoring
- Billing Fraud Oversight
- Risk Management
- Fraud Detection
- Product and Customer Segmentation
- Performance Evaluation
- General Compliance Functions
Understanding the Benefits of Deep Learning for Banking
Exploring the Different Deep Learning Packages for R
Deep Learning in R with Keras and RStudio
- Overview of the Keras Package for R
- Installing the Keras Package for R
- Loading the Data
- Using Built-in Datasets
- Using Data from Files
- Using Dummy Data
- Exploring the Data
- Preprocessing the Data
- Cleaning the Data
- Normalizing the Data
- Splitting the Data into Training and Test Sets
- Implementing One Hot Encoding (OHE)
- Defining the Architecture of Your Model
- Compiling and Fitting Your Model to the Data
- Training Your Model
- Visualizing the Model Training History
- Using Your Model to Predict Labels of New Data
- Evaluating Your Model
- Fine-Tuning Your Model
- Saving and Exporting Your Model
Hands-on: Building a Deep Learning Credit Risk Model Using R
Extending your Company’s Capabilities
- Developing Models in the Cloud
- Using GPUs to Accelerate Deep Learning
- Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis.
Summary and Conclusion
Duration
28 hours (usually 4 days including breaks)
Requirements
- Experience with Python programming
- General familiarity with financial and banking concepts
- Basic familiarity with statistics and mathematical concepts
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
Understanding the Fundamentals of Artificial Intelligence and Machine Learning
Understanding Deep Learning
- Overview of the Basic Concepts of Deep Learning
- Differentiating Between Machine Learning and Deep Learning
- Overview of Applications for Deep Learning
Overview of Neural Networks
- What are Neural Networks
- Neural Networks vs Regression Models
- Understanding Mathematical Foundations and Learning Mechanisms
- Constructing an Artificial Neural Network
- Understanding Neural Nodes and Connections
- Working with Neurons, Layers, and Input and Output Data
- Understanding Single Layer Perceptrons
- Differences Between Supervised and Unsupervised Learning
- Learning Feedforward and Feedback Neural Networks
- Understanding Forward Propagation and Back Propagation
- Understanding Long Short-Term Memory (LSTM)
- Exploring Recurrent Neural Networks in Practice
- Exploring Convolutional Neural Networks in practice
- Improving the Way Neural Networks Learn
Overview of Deep Learning Techniques Used in Banking
- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentimental Analysis
Exploring Deep Learning Case Studies for Banking
- Anti-Money Laundering Programs
- Know-Your-Customer (KYC) Checks
- Sanctions List Monitoring
- Billing Fraud Oversight
- Risk Management
- Fraud Detection
- Product and Customer Segmentation
- Performance Evaluation
- General Compliance Functions
Understanding the Benefits of Deep Learning for Banking
Exploring the Different Deep Learning Libraries for Python
Setting Up Python with the TensorFlow for Deep Learning
- Installing the TensorFlow Python API
- Testing the TensorFlow Installation
- Setting Up TensorFlow for Development
- Training Your First TensorFlow Neural Net Model
Setting Up Python with Keras for Deep Learning
Building Simple Deep Learning Models with Keras
- Creating a Keras Model
- Understanding Your Data
- Specifying Your Deep Learning Model
- Compiling Your Model
- Fitting Your Model
- Working with Your Classification Data
- Working with Classification Models
- Using Your Models
Working with TensorFlow for Deep Learning for Banking
- Preparing the Data
- Downloading the Data
- Preparing Training Data
- Preparing Test Data
- Scaling Inputs
- Using Placeholders and Variables
- Specifying the Network Architecture
- Using the Cost Function
- Using the Optimizer
- Using Initializers
- Fitting the Neural Network
- Building the Graph
- Training the Model
- The Graph
- The Session
- Train Loop
- Evaluating the Model
- Building the Eval Graph
- Evaluating with Eval Output
- Training Models at Scale
- Visualizing and Evaluating Models with TensorBoard
Hands-on: Building a Deep Learning Credit Risk Model Using Python
Extending your Company’s Capabilities
- Developing Models in the Cloud
- Using GPUs to Accelerate Deep Learning
- Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis
Summary and Conclusion
Duration
21 hours (usually 3 days including breaks)
Requirements
- Experience with Python programming
- Basic familiarity with statistics and linear algebra
Overview
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
- Difference between statistical learning (statistical analysis) and machine learning
- Adoption of machine learning technology and talent by finance and banking companies
Different Types of Machine Learning
- Supervised learning vs unsupervised learning
- Iteration and evaluation
- Bias-variance trade-off
- Combining supervised and unsupervised learning (semi-supervised learning)
Machine Learning Languages and Toolsets
- Open source vs proprietary systems and software
- Python vs R vs Matlab
- Libraries and frameworks
Machine Learning Case Studies
- Consumer data and big data
- Assessing risk in consumer and business lending
- Improving customer service through sentiment analysis
- Detecting identity fraud, billing fraud and money laundering
Hands-on: Python for Machine Learning
- Preparing the Development Environment
- Obtaining Python machine learning libraries and packages
- Working with scikit-learn and PyBrain
How to Load Machine Learning Data
- Databases, data warehouses and streaming data
- Distributed storage and processing with Hadoop and Spark
- Exported data and Excel
Modeling Business Decisions with Supervised Learning
- Classifying your data (classification)
- Using regression analysis to predict outcome
- Choosing from available machine learning algorithms
- Understanding decision tree algorithms
- Understanding random forest algorithms
- Model evaluation
- Exercise
Regression Analysis
- Linear regression
- Generalizations and Nonlinearity
- Exercise
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Exercise
Hands-on: Building an Estimation Model
- Assessing lending risk based on customer type and history
Evaluating the performance of Machine Learning Algorithms
- Cross-validation and resampling
- Bootstrap aggregation (bagging)
- Exercise
Modeling Business Decisions with Unsupervised Learning
- When sample data sets are not available
- K-means clustering
- Challenges of unsupervised learning
- Beyond K-means
- Bayes networks and Markov Hidden Models
- Exercise
Hands-on: Building a Recommendation System
- Analyzing past customer behavior to improve new service offerings
Extending your company’s capabilities
- Developing models in the cloud
- Accelerating machine learning with GPU
- Applying Deep Learning neural networks for computer vision, voice recognition and text analysis
Closing Remarks
Duration
28 hours (usually 4 days including breaks)
Requirements
- Programming experience with any language
- Basic familiarity with statistics and linear algebra
Overview
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.
Audience
- Developers
- Data scientists
- Banking professionals with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
- Difference between statistical learning (statistical analysis) and machine learning
- Adoption of machine learning technology by finance and banking companies
Different Types of Machine Learning
- Supervised learning vs unsupervised learning
- Iteration and evaluation
- Bias-variance trade-off
- Combining supervised and unsupervised learning (semi-supervised learning)
Machine Learning Languages and Toolsets
- Open source vs proprietary systems and software
- R vs Python vs Matlab
- Libraries and frameworks
Machine Learning Case Studies
- Consumer data and big data
- Assessing risk in consumer and business lending
- Improving customer service through sentiment analysis
- Detecting identity fraud, billing fraud and money laundering
Introduction to R
- Installing the RStudio IDE
- Loading R packages
- Data structures
- Vectors
- Factors
- Lists
- Data Frames
- Matrixes and Arrays
How to Load Machine Learning Data
- Databases, data warehouses and streaming data
- Distributed storage and processing with Hadoop and Spark
- Importing data from a database
- Importing data from Excel and CSV
Modeling Business Decisions with Supervised Learning
- Classifying your data (classification)
- Using regression analysis to predict outcome
- Choosing from available machine learning algorithms
- Understanding decision tree algorithms
- Understanding random forest algorithms
- Model evaluation
- Exercise
Regression Analysis
- Linear regression
- Generalizations and Nonlinearity
- Exercise
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Exercise
Hands-on: Building an Estimation Model
- Assessing lending risk based on customer type and history
Evaluating the performance of Machine Learning Algorithms
- Cross-validation and resampling
- Bootstrap aggregation (bagging)
- Exercise
Modeling Business Decisions with Unsupervised Learning
- When sample data sets are not available
- K-means clustering
- Challenges of unsupervised learning
- Beyond K-means
- Bayes networks and Markov Hidden Models
- Exercise
Hands-on: Building a Recommendation System
- Analyzing past customer behavior to improve new service offerings
Extending your company’s capabilities
- Developing models in the cloud
- Accelerating machine learning with additional GPUs
- Applying Deep Learning neural networks for computer vision, voice recognition and text analysis
Closing Remarks
