Machine Learning in Fraud Detection — An Overview

Today, Machine Learning is utilized across various business sectors, including customer retention analysis, credit scoring, and personalized recommendations on platforms like Amazon and Netflix. This technology enables machines to perform tasks such as piloting aircraft, driving vehicles, interpreting text sentiments, and even creating music or literature. Notably, it has triumphed over humans in the popular multiplayer game DOTA2.

This technology has also shown remarkable effectiveness in combating fraud.

But what does Machine Learning mean in the realm of identifying fraudulent activities?

In simple terms,

Machine Learning is a branch of computer science that enables systems to differentiate between fraudulent and legitimate users without direct programming of specific indicators.

Let’s explore further...

The concept revolves around identifying unique traits of fraudulent transactions that set them apart from legitimate ones. Machine Learning algorithms analyze data patterns that help distinguish fraudsters from genuine clients, using thousands of data points that may seem unrelated at first glance. These algorithms seek patterns in the behaviors of fraudsters, their device specifications, and more.

Implementing Machine Learning in Business

Every time a customer makes a transaction, the Machine Learning model meticulously examines their profile for any suspicious behaviors.

Depending on the severity of any detected "fraudulent" patterns, a transaction may be accepted, blocked, or flagged for manual review, all within milliseconds.

The uniqueness of Machine Learning lies in its high accuracy in identifying fraudulent transactions. For instance, Almundo.com, a well-known Online Travel Agency in Latin America, has achieved a 70% reduction in fraud, chargebacks, and manual reviews through the use of Machine Learning.

This decrease not only enhances customer experience by reducing false positives but also optimizes operational costs and significantly boosts revenue.

Machine Learning is not intended to replace risk managers — it equips them with a more powerful tool to enhance their effectiveness!

Why is Machine Learning Important?

There are several compelling reasons for companies to integrate Machine Learning into their fraud detection frameworks. Here are some of the key points:

Online fraud has evolved into a more complex threat due to rapid technological advancements available to fraudsters. Thus, to stay ahead, organizations must analyze significantly more data to effectively identify fraudulent activities. While a skilled analyst might handle 10-20 data points, Machine Learning can analyze thousands of features in mere moments.

The traditional static rules-based approach to fraud detection has its drawbacks, making it less effective:

1. There’s a delay between recognizing the need for a new rule and its implementation — machines can adapt almost instantly.
2. Static systems rely heavily on human input, which can be costly. Expanding into new markets necessitates hiring additional risk analysts to interpret market-specific data.
3. Rules are crafted by humans based on their experience, but as fraud attacks become more sophisticated, the rules also become increasingly complex and prone to error, leading to financial losses and higher false positive rates.
4. Rule systems can become unwieldy as each new fraud scheme necessitates the creation of a new rule. Eventually, a merchant may find themselves managing hundreds of rules, making it difficult to assess their overall effectiveness over time. Machine Learning allows for quicker performance evaluations and adaptation to changing circumstances.

Lastly, Machine Learning enables organizations to formulate a business strategy grounded in key performance indicators (KPIs) and predictive analytics regarding fraud attempts. This capability allows for forecasting acceptance, rejection, or manual review rates to maximize revenue potential. For example, it enables businesses to understand at which rejection thresholds they can expect to capture certain fraudulent transactions.

How to Forecast Fraud Using Machine Learning

In this blog post, I’ll present a simplified overview of the Machine Learning process to provide a foundational understanding.

Step 1: Define Project Goals

The first step is to establish your business objectives. These may include:

1. Reducing the estimated chargeback ratio.
2. Minimizing the false positive rate (false alerts).
3. Keeping manual review costs at a manageable level.
4. Identifying client segments that generate the most revenue.

Key questions to address in this step include:

1. What does your company need?
2. What are your primary KPIs?
3. What are the sources of revenue and the main blockers?
4. What criteria define success for the project?

On a technical level, the primary goal is to predict whether a given transaction contributes to revenue or is a fraudulent attempt.

Step 2: Data Preparation

Consider this: when you want to learn something new, you seek out educational resources — books, articles, forums, and conversations with experts.

Machines operate similarly; to create profiles of fraudsters, they require historical data from past fraudulent incidents. The more features and data a company can gather for analysis, the better. Relevant data might include transaction time, frequency, value, purchase history, geolocation, chargeback reports, etc.

This raw data must be cleaned and formatted into a machine-readable structure. This step can consume 60% to 80% of the entire Machine Learning process and necessitates specific technical expertise. Thus, it is advisable to either cultivate this expertise in-house or collaborate with an external provider.

The outcome of Step 2 is a source dataset that will be utilized in the subsequent analysis (see Step 3). Below is a simplified example of what this dataset might look like. Keep in mind that actual datasets can contain hundreds or thousands of columns and millions of rows.

In our example, each transaction (row) is described by various features (columns). The final column, known as the target, indicates whether a specific transaction was fraudulent. How you label a fraud in your data is flexible; it could be marked as “1”, “F”, “Fraud”, etc. The critical point is that Machine Learning algorithms will identify patterns that differentiate the “1” class from “0”. However, the effectiveness of the algorithm hinges on the quality of the “Target” column. Moreover, Machine Learning can recognize multiple categories, such as good customers, regular customers, and fraudsters.

Step 3: Constructing a Machine Learning Model

What are we talking about?

A Machine Learning model.

This is the essence of the Machine Learning process and its ultimate output. Once provided with details about a new transaction, the model will generate a recommendation indicating whether it is a fraudulent attempt.

In constructing the model, the dataset from Step 2 is utilized to determine the features that characterize fraudulent transactions and the best predictors of fraud. Given the potentially hundreds of features involved, analyzing and deriving meaningful insights can be challenging.

This task requires appropriate technology and Data Scientists who possess the expertise to combine diverse data types, select the most suitable modeling techniques for the specific business case, and determine the optimal set of model parameters.

Step 4: Generating Predictions

So, we have a Machine Learning model… what next?

Put it to work for your business! The model should be deployed and integrated into your IT framework.

Each time a customer purchases a product or service from your online store, the transaction data is sent to the model. The model will provide a recommendation on whether to approve, block, or flag the transaction for manual review.

This process is referred to as data scoring.

However, this is not the end. During manual reviews, if a team member identifies a flagged transaction as legitimate (false positive), the Machine Learning model will incorporate this information to improve its accuracy in future decisions.

Step 5: Upgrading the Model

Models operating in a production environment continuously receive feedback from new chargebacks and are routinely retrained to recognize emerging fraudulent patterns. Just as humans require learning experiences to maintain cognitive abilities, so too do models.

As previously mentioned, fraud attacks are becoming increasingly sophisticated, necessitating more data for effective detection. For example, in-depth device characteristics (e.g., GPU specifications, processing power, connection type, virtual machine usage, or VPN connections) can yield valuable insights about consumers and enhance prediction accuracy.

It is advisable to seek new data sources or utilize existing anti-fraud systems that compile and analyze as many as 3,000 data points to create more comprehensive and accurate fraudster profiles.

Aleksander Kijek, CPO of Nethone

Originally published at nethone.com on August 18, 2017.