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Digital Detectives: Fraud Detection With Machine Learning

Machine learning is a powerful technology for finding patterns. That power can be harnessed to spot suspicious activity and prevent fraud.

Nate Dow

By Nate Dow

Solutions Architect Nate Dow helps BairesDev teams provide the highest quality of software delivery and products with creative business solutions.

10 min read

Cybersecurity is important, especially in an interconnected reality like the one we are living in. With the surge of cloud computing and online features, the venues of attack for cybercriminals have grown exponentially, and we can no longer rely on the traditional methods to keep us safe. 

Captchas, for example, have been around since 1997, and though it took a while to garner popularity, they’ve been one of the go-to security systems to prevent data scraping, brute force attacks, and automatic scripts. They are also a burden on the user (I have failed so many captchas in my life that I’m starting to question my own humanity), and with the use of OCR models, they’ve been beaten time and again.

Unfortunately, technology isn’t the weakest link; it’s us, the humans. Most cyberattacks are actually inside jobs, and scams like phishing and ransomware distribution often rely on human engineering to catch unsuspecting victims. Can we use AI to cover our weak points? Is fraud detection machine learning reliable?

How to Use Machine Learning for Fraud Detection

One of the key strategies of fraudsters is finding patterns of operation that are extremely difficult to detect. For example, someone using a stolen credit card could trigger a flag by spending a huge amount of money in very little time. But it’s a lot harder to spot if the perpetrator makes smaller transactions in well-known online stores.

The keyword here is patterns. While we could theoretically create software based on rules to spot fraudulent operations, once the criminals understand what behaviors trigger a flag, they’ll adapt and develop new strategies to avoid raising suspicion. It’s a momentary measure at best, and one that only works if we already understand their methodology.

On top of it all, finding hidden patterns in transaction data is extremely difficult and requires a certain level of expertise that’s hard to come by. Fraud experts spend years training and studying to be able to perceive suspicious behavior, and there are certainly more frauds than experts. So, what’s the alternative?

One promising avenue is machine learning. By training a computer to spot these patterns, we can monitor millions of transactions in a fraction of the time it would take to review them manually. Better yet, the computer can notice patterns that might otherwise go undetected. It’s like having a futuristic detective at your side. But how does it work?

AI and Machine Learning in Fraud Detection

Basically, there are three types of machine learning models: supervised, unsupervised and semi-supervised. In supervised machine learning, we feed a dataset with input and output variables to a model that learns which inputs lead to which outputs. 

For example, imagine that we have a data set of credit card transactions with the value of each transaction, the time, the pattern of consumption, and other variables. We have previously flagged which ones are fraudulent and which are legit. The computer learns which pattern corresponds to each type of transaction so it can correctly predict whether future transactions are fraudulent or not. 

In unsupervised models, we don’t have an output variable, so the model tries to classify data by finding patterns. The two most common methods are clustering and density estimation. The first (and most common method) is used for classification tasks, grouping data by similar patterns. The latter summarizes the distribution of the data.

For example, with a data set of credit card transactions, we use a self-learning model to classify the data based on patterns detected by the AI, and then the engineer can check each grouping and flag any suspicious activity, or the AI can be programmed to automatically report outliers for further investigation.

In semi-supervised learning we have some data with the output variable and some without it, so we use a combination of the techniques mentioned above to build the model.

Supervised vs. Unsupervised Models

Since supervised models are based on data in which we have already flagged fraudulent transactions, they tend to be extremely reliable. Unfortunately, that’s also their weak point. These models will be at their best when they find patterns similar to those that have been spotted in the past. In other words, as patterns change, their reliability plummets.

Unsupervised models, on the other hand, can be extremely useful for exploring new data. But since we can’t be sure if suspicious activity is in fact a fraud without further inspection, the model is more likely to find false positives (detect fraud when there is none). Remember, the model can only tell us if a certain transaction has a similar pattern as other data entries, not what the pattern means.

Still, it might be a bit of a necessary evil, and with the right customer support services, a false flag could be nothing more than a slight hassle. 

Something to keep in mind is that machine learning cannot be the only fraud prevention measure in our system. Two-factor authentication and user validation can go a long way in helping us minimize the risk of fraud and avoid the headaches of false flagging.

Some Machine Learning Models for Fraud Detection

  • Logistic regression: One of the classic regression models: based on a series of data entries, the model makes a choice between two possible results. In this case, fraudulent or not. This is a great example of traditional supervised learning.
  • Decision trees and random forests: Decision trees use examples to find a series of rules that can be applied in a downstream process to classify data. Random forests are an extension of decision trees where uncorrelated trees each make a decision and cast a vote. The output is decided by a democratic process: the choice with the most votes wins. This model is especially useful for cases where we don’t have enough information to make assumptions about our data (for example, we don’t know if it follows a normal distribution).
  • Neural networks: Another extremely popular model that emulates human learning: a series of nodes are trained with data to find patterns, and the model configures itself with the most optimal pathways. While powerful, it’s one of the most resource-intensive models, at least during the training phase.
  • K-nearest neighbor: A supervised model in which new cases are classified based on their proximity (similarity) with other cases on the data set.

Do You Need a Digital Sherlock Holmes?

From credit card fraud to identity theft, every form of cybercrime is a threat to our users and to our business. With the right machine learning model, you too can protect yourself and your community from malcontent third parties. Most of these models are actually very easy to implement. Services like AWS and Azure already offer applications for fraud detection machine learning, so you can either outsource your solution or build an in-house system. 

Whichever route you take, you can sleep easy knowing that your digital detective is on the case.

Nate Dow

By Nate Dow

As a Solutions Architect, Nate Dow helps BairesDev provide the highest quality software delivery and products by overcoming technical challenges and defining internal teams. His creative approaches help solve clients' business problems with technology.

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