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Home > Data > Machine Learning > Machine Learning for Finance
Machine Learning for Finance
Machine Learning for Finance

Machine Learning for Finance: Principles and practice for financial insiders

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Machine Learning for Finance

Chapter 2. Applying Machine Learning to Structured Data

Structured data is a term used for any data that resides in a fixed field within a record or file, two such examples being relational databases and spreadsheets. Usually, structured data is presented in a table in which each column presents a type of value, and each row represents a new entry. Its structured format means that this type of data lends itself to classical statistical analysis, which is also why most data science and analysis work is done on structured data.

In day-to-day life, structured data is also the most common type of data available to businesses, and most machine learning problems that need to be solved in finance deal with structured data in one way or another. The fundamentals of any modern company's day-to-day running is built around structured data, including, transactions, order books, option prices, and suppliers, which are all examples of information usually collected in spreadsheets or databases.

This chapter...

The data

The dataset we will work with is a synthetic dataset of transactions generated by a payment simulator. The goal of this case study and the focus of this chapter is to find fraudulent transactions within a dataset, a classic machine learning problem many financial institutions deal with.

Note

Note: Before we go further, a digital copy of the code, as well as an interactive notebook for this chapter are accessible online, via the following two links:

An interactive notebook containing the code for this chapter can be found under https://www.kaggle.com/jannesklaas/structured-data-code

The code can also be found on GitHub, in this book's repository: https://github.com/PacktPublishing/Machine-Learning-for-Finance

The dataset we're using stems from the paper PaySim: A financial mobile money simulator for fraud detection, by E. A. Lopez-Rojas, A. Elmir, and S. Axelsson. The dataset can be found on Kaggle under this URL: https://www.kaggle.com/ntnu-testimon/paysim1.

Before we break it down...

Heuristic, feature-based, and E2E models

Before we dive into developing models to detect fraud, let's take a second to pause and ponder over the different kinds of models we could build.

  • A heuristic-based model is a simple "rule of thumb" developed purely by humans. Usually, the heuristic model stems from having an expert knowledge of the problem.

  • A feature-based model relies heavily on humans modifying the data to create new and meaningful features, which are then fed into a (simple) machine learning algorithm. This approach mixes expert knowledge with learning from data.

  • An E2E model learns purely from raw data. No human expertise is used, and the model learns everything directly from observations.

In our case, a heuristic-based model could be created to mark all transactions with the TRANSFER transaction type and an amount over $200,000 as fraudulent. Heuristic-based models have the advantage that they are both fast to develop and easy to implement; however, this comes with a pay-off, their...

The machine learning software stack

In this chapter, we will be using a range of different libraries that are commonly used in machine learning. Let's take a minute to look at our stack, which consists of the following software:

  • Keras: A neural network library that can act as a simplified interface to TensorFlow.

  • NumPy: Adds support for large, multidimensional arrays as well as an extensive collection of mathematical functions.

  • Pandas: A library for data manipulation and analysis. It's similar to Microsoft's Excel but in Python, as it offers data structures to handle tables and the tools to manipulate them.

  • Scikit-learn: A machine learning library offering a wide range of algorithms and utilities.

  • TensorFlow: A dataflow programming library that facilitates working with neural networks.

  • Matplotlib: A plotting library.

  • Jupyter: A development environment. All of the code examples in this book are available in Jupyter Notebooks.

The majority of this book is dedicated to working with the Keras...

The heuristic approach

Earlier in this chapter, we introduced the three models that we will be using to detect fraud, now it's time to explore each of them in more detail. We're going to start with the heuristic approach.

Let's start by defining a simple heuristic model and measuring how well it does at measuring fraud rates.

Making predictions using the heuristic model

We will be making our predictions using the heuristic approach over the entire training data set in order to get an idea of how well this heuristic model does at predicting fraudulent transactions.

The following code will create a new column, Fraud_Heuristic, and in turn assigns a value of 1 in rows where the type is TRANSFER, and the amount is more than $200,000:

df['Fraud_Heuristic '] = np.where(((df['type'] == 'TRANSFER') &(df['amount'] > 200000)),1,0)

With just two lines of code, it's easy to see how such a simple metric can be easy to write, and quick to deploy.

The F1 score

One important thing we must consider is the...

The feature engineering approach

The objective of feature engineering is to exploit the qualitative insight of humans in order to create better machine learning models. A human engineer usually uses three types of insight: intuition, expert domain knowledge, and statistical analysis. Quite often, it's possible to come up with features for a problem just from intuition.

As an example, in our fraud case, it seems intuitive that fraudsters will create new accounts for their fraudulent schemes and won't be using the same bank account that they pay for their groceries with.

Domain experts are able to use their extensive knowledge of a problem in order to come up with other such examples of intuition. They'll know more about how fraudsters behave and can craft features that indicate such behavior. All of these intuitions are then usually confirmed by statistical analysis, something that can even be used to open the possibilities of discovering new features.

Statistical analysis can sometimes turn...

Preparing the data for the Keras library

In Chapter 1, Neural Networks and Gradient-Based Optimization, we saw that neural networks would only take numbers as inputs. The issue for us in our dataset is that not all of the information in our table is numbers, some of it is presented as characters.

Therefore, in this section, we're going to work on preparing the data for Keras so that we can meaningfully work with it.

Before we start, let's look at the three types of data, Nominal, Ordinal, and Numerical:

  • Nominal data: This comes in discrete categories that cannot be ordered. In our case, the type of transfer is a nominal variable. There are four discrete types, but it does not make sense to put them in any order. For instance, TRANSFER cannot be more than CASH_OUT, so instead, they are just separate categories.

  • Ordinal data: This also comes in discrete categories, but unlike nominal data, it can be ordered. For example, if coffee comes in large, medium, and small sizes, those are distinct...

Creating predictive models with Keras

Our data now contains the following columns:

amount, 
oldBalanceOrig, 
newBalanceOrig, 
oldBalanceDest, 
newBalanceDest, 
isFraud, 
isFlaggedFraud, 
type_CASH_OUT, 
type_TRANSFER, isNight

Now that we've got the columns, our data is prepared, and we can use it to create a model.

Extracting the target

To train the model, a neural network needs a target. In our case, isFraud is the target, so we have to separate it from the rest of the data. We can do this by running:

y_df = df['isFraud']
x_df = df.drop('isFraud',axis=1)

The first step only returns the isFraud column and assigns it to y_df.

The second step returns all columns except isFraud and assigns them to x_df.

We also need to convert our data from a pandas DataFrame to NumPy arrays. The pandas DataFrame is built on top of NumPy arrays but comes with lots of extra bells and whistles that make all the preprocessing we did earlier possible. To train a neural network, however, we just need the underlying data...

A brief primer on tree-based methods

No chapter on structured data would be complete without mentioning tree-based methods, such as random forests or XGBoost.

It is worth knowing about them because, in the realm of predictive modeling for structured data, tree-based methods are very successful. However, they do not perform as well on more advanced tasks, such as image recognition or sequence-to-sequence modeling. This is the reason why the rest of the book does not deal with tree-based methods.

Note

Note: For a deeper dive into XGBoost, check out the tutorials on the XGBoost documentation page: http://xgboost.readthedocs.io. There is a nice explanation of how tree-based methods and gradient boosting work in theory and practice under the Tutorials section of the website.

A simple decision tree

The basic idea behind tree-based methods is the decision tree. A decision tree splits up data to create the maximum difference in outcomes.

Let's assume for a second that our isNight feature is the greatest...

E2E modeling

Our current approach relies on engineered features. As we discussed at the start of this chapter, an alternative method is E2E modeling. In E2E modeling, both raw and unstructured data about a transaction is used. This could include the description text of a transfer, video feeds from cameras monitoring a cash machine, or other sources of data. E2E is often more successful than feature engineering, provided that you have enough data available.

To get valid results, and to successfully train the data with an E2E model it can take millions of examples. Yet, often this is the only way to gain an acceptable result, especially when it is hard to codify the rules for something. Humans can recognize things in images well, but it is hard to come up with exact rules that distinguish things, which is where E2E shines.

In the dataset used for this chapter, we do not have access to more data, but the rest of the chapters of this book demonstrate various E2E models.

Exercises

If you visit https://kaggle.com, search for a competition that has structured data. One example is the Titanic competition. Here you can create a new kernel, do some feature engineering, and try to build a predictive model.

How much can you improve it by investing time in feature engineering versus model tweaking? Is there an E2E approach to the problem?

Summary

In this chapter, we have taken a structured data problem from raw data to strong and reliable predictive models. We have learned about heuristic, feature engineering, and E2E modeling. We have also seen the value of clear evaluation metrics and baselines.

In the next chapter, we will look into a field where deep learning truly shines, computer vision. Here, we will discover the computer vision pipeline, from working with simple models to very deep networks augmented with powerful preprocessing software. The ability to "see" empowers computers to enter completely new domains.

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Key benefits

  • Explore advances in machine learning and how to put them to work in financial industries
  • Gain expert insights into how machine learning works, with an emphasis on financial applications
  • Discover advanced machine learning approaches, including neural networks, GANs, and reinforcement learning

Description

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.

Who is this book for?

This book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.

What you will learn

  • Apply machine learning to structured data, natural language, photographs, and written text
  • Understand how machine learning can help you detect fraud, forecast financial trends, analyze customer sentiments, and more
  • Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow
  • Delve into neural networks, and examine the uses of GANs and reinforcement learning
  • Debug machine learning applications and prepare them for launch
  • Address bias and privacy concerns in machine learning
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Table of Contents

10 Chapters
Neural Networks and Gradient-Based Optimization Chevron down icon Chevron up icon
Neural Networks and Gradient-Based Optimization
Our journey in this book
What is machine learning?
Supervised learning
Unsupervised learning
Reinforcement learning
Setting up your workspace
Using Kaggle kernels
Using the AWS deep learning AMI
Approximating functions
A forward pass
A logistic regressor
Optimizing model parameters
Measuring model loss
A deeper network
A brief introduction to Keras
Tensors and the computational graph
Exercises
Summary
Applying Machine Learning to Structured Data Chevron down icon Chevron up icon
Applying Machine Learning to Structured Data
The data
Heuristic, feature-based, and E2E models
The machine learning software stack
The heuristic approach
The feature engineering approach
Preparing the data for the Keras library
Creating predictive models with Keras
A brief primer on tree-based methods
E2E modeling
Exercises
Summary
Utilizing Computer Vision Chevron down icon Chevron up icon
Utilizing Computer Vision
Convolutional Neural Networks
Filters on color images
The building blocks of ConvNets in Keras
More bells and whistles for our neural network
Working with big image datasets
Working with pretrained models
The modularity tradeoff
Computer vision beyond classification
Exercises
Summary
Understanding Time Series Chevron down icon Chevron up icon
Understanding Time Series
Visualization and preparation in pandas
Fast Fourier transformations
Autocorrelation
Establishing a training and testing regime
A note on backtesting
Median forecasting
ARIMA
Kalman filters
Forecasting with neural networks
Conv1D
Dilated and causal convolution
Simple RNN
LSTM
Recurrent dropout
Bayesian deep learning
Exercises
Summary
Parsing Textual Data with Natural Language Processing Chevron down icon Chevron up icon
Parsing Textual Data with Natural Language Processing
An introductory guide to spaCy
Named entity recognition
Part-of-speech (POS) tagging
Rule-based matching
Regular expressions
A text classification task
Preparing the data
Bag-of-words
Topic modeling
Word embeddings
Document similarity with word embeddings
A quick tour of the Keras functional API
Attention
Seq2seq models
Exercises
Summary
Using Generative Models Chevron down icon Chevron up icon
Using Generative Models
Understanding autoencoders
Visualizing latent spaces with t-SNE
Variational autoencoders
VAEs for time series
GANs
Using less data – active learning
SGANs for fraud detection
Exercises
Summary
Reinforcement Learning for Financial Markets Chevron down icon Chevron up icon
Reinforcement Learning for Financial Markets
Catch – a quick guide to reinforcement learning
Markov processes and the bellman equation – A more formal introduction to RL
Advantage actor-critic models
Evolutionary strategies and genetic algorithms
Practical tips for RL engineering
Frontiers of RL
Exercises
Summary
Privacy, Debugging, and Launching Your Products Chevron down icon Chevron up icon
Privacy, Debugging, and Launching Your Products
Debugging data
Debugging your model
Deployment
Performance tips
Exercises
Summary
Fighting Bias Chevron down icon Chevron up icon
Fighting Bias
Sources of unfairness in machine learning
Legal perspectives
Observational fairness
Training to be fair
Causal learning
Interpreting models to ensure fairness
Unfairness as complex system failure
A checklist for developing fair models
Exercises
Summary
Bayesian Inference and Probabilistic Programming Chevron down icon Chevron up icon
Bayesian Inference and Probabilistic Programming
An intuitive guide to Bayesian inference
Summary
Farewell
Further reading

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While going over supervised learning and unsupervised learning, the book also covers NLP with textual data and time series methods. The book is long but that is because it has many diagrams and much code.
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About the authors

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Profile icon James Le
James Le
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James Le is currently a master's student studying Computer Science at Rochester Institute of Technology. He is pursuing a research path in Computer Vision using Deep Learning. Moreover, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.
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Profile icon Jannes Klaas
Jannes Klaas
Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies. Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.
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USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela