In today’s increasingly interconnected world, it is essential to ensure effective cyber security. Traditional security measures are no longer sufficient to protect against the ever-growing number of potential targets posed by cloud computing and other online technologies.
Captchas have been used as a security measure since 1997 to protect against data scraping, brute force attacks and automated scripts. Whilst they can be an inconvenience for the user, they are an essential part of maintaining security. Unfortunately, OCR models have been developed which are capable of bypassing Captchas.
It is unfortunate to note that humans are the weakest link when it comes to cyberattacks, with most originating internally. Social engineering is a major factor in the success of malicious activities such as phishing and ransomware. One has to wonder if Artificial Intelligence (AI) can be used to cover up any vulnerabilities. Furthermore, it is uncertain whether Machine Learning (ML) can be effectively used to detect fraudulent activities.
Understanding Fraud Detection Using Machine Learning
Fraudsters utilise methods of operation that are almost undetectable as a key strategy. For example, when an individual makes a substantial purchase using a stolen credit card, it may trigger cautionary signals. However, if the criminal is making smaller payments at widely-recognised online stores, they become more difficult to identify.
Patterns are key to detecting fraudulent transactions. While it is theoretically possible to create rule-based software to detect fraud, criminals will adapt their behaviour if they become aware of which actions trigger alerts. This type of solution is only a temporary measure unless we have a thorough understanding of their methods.
Uncovering concealed patterns within business transactions requires a significant amount of time and expertise. Unfortunately, there is a greater number of frauds than fraud specialists, who have spent years training and researching in order to recognise suspicious activity. What other options are available?
Machine learning is a promising avenue of exploration. By teaching a computer to recognise patterns in large volumes of data, we can effectively monitor millions of transactions in a fraction of the time that it would take to evaluate them manually. Furthermore, a machine can detect patterns that could easily be overlooked by the human eye. Imagine having a detective from the future on your side! However, a lack of understanding remains as to how this technology actually works.
Using Artificial Intelligence and Machine Learning to Identify Potential Fraud
Machine learning models can be categorised as supervised, unsupervised, or semi-supervised. In supervised machine learning, a dataset comprising of input and output variables is provided to the model, allowing it to identify the relationships between them.
Imagine having access to a database containing data on individuals’ credit card purchases, including the amounts spent, when they were bought, and how they were spent. The database is already marked to distinguish between genuine and fraudulent transactions. A computer is programmed to identify the distinct patterns associated with different types of purchases, enabling it to accurately identify future fraudulent transactions.
For unsupervised models, there is no predetermined target value to attain. Therefore, the model must rely on identifying underlying features. Cluster analysis and density estimation are the two main approaches used to achieve this. Cluster analysis is the most common approach, and involves grouping data into categories based on shared characteristics. Density estimation is another technique used to provide an overview of the data’s distribution.
If we have a dataset of credit card transactions, for instance, we can utilise a self-learning model to categorise the data based on patterns identified by the AI. An engineer can then review each classification and highlight any suspicious activity, or the AI can be trained to automatically report any outliers for further analysis.
Semi-supervised learning involves the use of a combination of the aforementioned methods to create the model, as we have some data which includes the output variable, and some that does not.
Supervised Models vs. Unsupervised Models
Having previously identified potentially fraudulent transactions in the data used by supervised models, we can have confidence in their accuracy. However, this is also a potential weakness. As these models discover patterns that are similar to those already identified, they are likely to perform optimally. Therefore, the credibility of patterns is likely to decline as they change over time.
Unsupervised models can be very beneficial in uncovering unknown information. However, due to the fact that a confirmed case of fraud requires further investigation, the model can be more prone to producing false alerts (i.e. detect fraud when there is actually none). It is important to bear in mind that the model can only show whether a transaction follows the same pattern as prior data input, and not necessarily what that pattern means.
Still, it might be a necessary evil, and with adequate customer support services, a false flag could just be an inconvenience at most.
It is essential to remember that machine learning alone will not suffice to prevent fraud within our system. By implementing user validation and two-factor authentication, we can significantly reduce the risk of fraudulent activity and the inconvenience of erroneous alerts.
Models for Identifying Fraud Using Machine Learning
Applying the Principles of Logistic regression:
This is an example of supervised learning, a type of regression model which predicts one of two outcomes based on a given set of data. In this case, the model predicts whether an event is a hoax or not.Using Random Forests and Decision Trees
Decision trees utilise examples to determine a set of rules for classifying data in the subsequent step. Random forests are a type of decision tree in which multiple independent trees produce individual decisions and then vote on the most suitable outcome. This is then selected democratically, with the most preferred choice being implemented. This model is especially beneficial when there is insufficient understanding of the data to make assumptions, such as if it follows a normal distribution.Connected brains:
The use of artificial neural networks is a widely-utilised method of simulating the learning process of humans. This process involves feeding data into a network of nodes and then training it to recognise patterns. This model is incredibly effective, yet can be quite resource-intensive, particularly during the training phase.A K-Nearest Neighbor Search:
Case-based reasoning is a supervised learning approach which involves classifying unseen data points into groups based on their similarity to existing cases in the dataset.
Do You Require a Virtual Holmes?
Cybercrime, including credit card fraud and identity theft, can create significant risks to our customers and our company’s success. Machine learning models can help to protect customers and our community from outsiders. Implementing most of these models is straightforward – you can outsource the problem to a service such as Amazon Web Services or Microsoft Azure, which both provide apps for fraud detection using machine learning, or you can create your own system.
With your digital detective on the case, you can rest comfortable regardless of the path you choose.
