A Guide to Using Brain.js for Financial Security: Detecting Fraud in Real-Time

Explore how Brain.js is revolutionizing fraud detection in real-time for financial institutions. Delve into the world of AI-powered security, how it safeguards your transactions and keeps your money safe.

Financial fraud is a huge problem that costs companies and consumers billions of dollars each year. Things like stolen credit cards, fake accounts, and identity theft can wreak havoc on people's finances and reputations. Thankfully, new AI tools like Brain.js are making it possible to detect fraud in real-time before major damage can be done.

In this guide, I'll walk you through step-by-step how to use Brain.js to build a fraud detection system. By the end, you'll have the skills to apply AI to protect your own applications and users. Let's get started!

Setting Up the Project

The first thing we need to do is set up a new Node.js project. This will include initializing NPM, installing Brain.js, and structuring our files. Here are the steps:

  1. Create a new directory for the project and initialize NPM:
mkdir brainjs-fraud-detection
cd brainjs-fraud-detection 
npm init -y

This will create a package.json file with default settings.

  1. Install the Brain.js package:
npm install brain.js
  1. Create an index.js file that will contain our main code.

At this point, our project structure looks like:

  - node_modules
  - package.json
  - index.js

Perfect! We're ready to start coding.

Loading Training Data

In order for Brain.js to detect fraud, we need to train it with example data first. This will teach the neural network what normal vs fraudulent transactions look like.

For this demo, let's use a CSV dataset of credit card transactions with both legitimate and fraudulent cases. Here's how to load it into our Node.js app:

Require the Brain.js module and load the filesystem module:

const brain = require('brain.js');
const fs = require('fs');

Load the CSV file into a variable:

const trainingData = fs.readFileSync('creditcard_transactions.csv', 'utf8');

The creditcard_transactions.csv file would contain columns representing the different features of each transaction, like:

  • Amount: The dollar amount
  • Location: Where the transaction took place
  • Time: Timestamp of transaction
  • Category: Type of business
  • AccountAge: How long account has been open
  • NumTransactions: Number of past transactions
  • Fraud: The label, 1 for fraud or 0 for legit

Convert the data into a JSON array:

const data = csvjson.toObject(trainingData); 

The data variable now contains an array of objects, with each object representing one credit card transaction.

Preprocessing the Data

Before training, we need to clean and normalize the data. This will improve the accuracy of the neural network.

Here are some key steps:

Convert strings to numbers:

data.forEach(item => {
  item.Amount = +item.Amount; 

Scale values to between 0-1:

const minMax = (data, field) => {
  const min = Math.min(...data.map(d => d[field]));
  const max = Math.max(...data.map(d => d[field]));
  data.forEach(d => {
    d[field] = (d[field] - min) / (max - min); 

minMax(data, 'Amount');

One-hot encode the class label:

const encodeClass = (data, field) => {
  const classes = [...new Set(data.map(d => d[field]))];
  data.forEach(d => {
    d[field] = classes.indexOf(d[field]);

encodeClass(data, 'Class');

The output of the preprocessed data would be an array of objects, like:

    Amount: 0.83, 
    Location: 0.6,
    Time: 0.3,
    Category: 0.1,
    AccountAge: 0.7, 
    NumTransactions: 0.9,
    Fraud: 1 
    Amount: 0.75,
    Location: 0.72,
    Time: 0.21,
    Category: 0.3, 
    AccountAge: 0.67,
    NumTransactions: 0.8, 
    Fraud: 0

Where all values are normalized between 0-1 and the Fraud label is one-hot encoded.

This prepares our data for optimal training. Now on to the fun part!

Training the Neural Network

Brain.js makes training a neural network incredibly easy. We just need to:

Initialize a neural network instance:

const net = new brain.NeuralNetwork();

Fit the model with our preprocessed data:


And that's it! Brain.js will automatically configure and optimize the network architecture.

The key parameters we can tweak are:

  • Hidden layers: net.train(data, {hiddenLayers: [3, 4]})
  • Learning rate: net.train(data, {learningRate: 0.01})
  • Epochs: net.train(data, {epochs: 25})

But the defaults work pretty well for most problems. Once training is complete, we can start making predictions.

Making Predictions

To classify new transactions as fraudulent or legitimate, we pass the data into the trained network like so:

const newTransaction = {
  Amount: 0.64, 
  // other features

const output = net.run(newTransaction); 

This will return an array with probabilities for each class. The index with the highest value is the predicted class.

For example, [0.3, 0.7] indicates a 70% probability of being in class 1 (fraudulent). We can write a classify function to clean this up:

function classify(input) {
  const output = net.run(input);
  return output.indexOf(Math.max(...output)); 

const prediction = classify(newTransaction); 

Finally, the output of making predictions on a new transaction would be an array of probabilities like:

[0.2, 0.8] 

Where the first value is the probability it's legitimate and the second is the probability it's fraud. The classify function would return 1 or 0 depending on which index is higher.

And that's it! We now have a neural network that can detect fraudulent transactions in real-time.

Integrating with Web Applications

So how do we actually integrate this into a real web application? Here are a couple options:


  1. Export the trained model JSON from Brain.js.
  2. Import the JSON into the client-side app (React, Vue, etc).
  3. Pass transaction data to model and classify.


  1. Train model in Node.js server.
  2. Expose prediction API endpoint.
  3. Call endpoint from client and handle response.

Both methods work well! The server-side approach keeps the model logic private while the client-side approach can reduce latency.

For a real production system, you'll probably want to retrain the model periodically with new data. Brain.js makes this easy by letting you save and load models. Just be sure to evaluate performance over time and tweak hyperparameters as needed.

And that covers the basics of implementing real-time fraud detection with Brain.js! The full power of deep learning can now be used to protect your finances and data from malicious attacks. Let me know if you have any other questions! I'm always happy to help fellow coders.

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