Deep Learning for Banking (with Python) Training Course

Course Code



28 hours (usually 4 days including breaks)


  • Experience with Python programming
  • General familiarity with financial and banking concepts
  • Basic familiarity with statistics and mathematical concepts


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


  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline


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



Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £5800

Private Remote

From £5200 (99)

Public Classroom

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