Deep Dive: Unsupervised Machine Learning Steps Up To Fight Fraud

Many FIs and merchants that have fallen victim to fraud traditionally respond by assessing the damage, pinpointing how the attack succeeded and implementing new measures to prevent similar schemes from happening again. Some businesses are looking for solutions that will help them stop fraud from happening in the first place as criminals become increasingly creative and aggressive in their efforts to steal data and funds.

The push for more intelligent anti-fraud solutions comes as the costs of such attacks are reaching new heights. Fraud losses hit $14.7 billion last year, according to the latest DataVisor Fraud Index Report. Account takeover (ATO) fraud proved to be particularly effective, causing $4 billion in losses. The eCommerce sector was a favorite target for fraudsters, representing 40 percent of total losses due to ATOs.

The rising threat and increasing expenses of ATO fraud indicate that the traditional fraud response model, which sees affected parties reacting to a breach, is ready for an upgrade. Legacy fraud solutions are limited because they can only react to each incident after they unfold, an approach that is no longer effective given fraudsters’ creativity and adaptability.

Companies may be doomed to repeat the cycle of responding to each attack as it unfolds if new anti-fraud measures are not implemented. New digital solutions, such as unsupervised machine learning (ML), could radically shift the conventional approach to fraud. The following Deep Dive outlines how unsupervised ML is changing anti-fraud efforts’ dynamics.

How ML Tools Learn

ML solutions use artificial intelligence (AI) to analyze human interactions and form predictive data to anticipate the most likely outcomes. This technology falls into two categories: supervised and unsupervised. Supervised ML systems rely on input and output data to learn how details are related, while unsupervised ML only requires input data. These latter tools collect data — both categorized and uncategorized — and sort it accordingly to get more information. Such solutions identify common factors and react based on commonalities or lack thereof.

Unsupervised ML’s purpose is to find patterns in unstructured data by first clustering data into groups based on various similarities, enabling a better understanding of all data points, what the information means and how data should be organized. The systems can then develop more effective strategies for responding to potentially fraudulent acts.

Visualization is another key component of unsupervised ML. These tools can take clusters of data and produce visual representations — such as charts, diagrams and graphs — of their findings to better communicate them. These images can help firms better understand the data, find areas for improvement and anticipate future issues.

Unsupervised ML can also simplify data through a process known as dimensionality reduction, which reduces the number of random variables under a fixed set of principles. Simplification such as this can make ML systems faster and improve their performance. Dimensionality reduction must be approached carefully, however. Crucial data might otherwise get lost, resulting in unreliable outputs.

Another potential benefit is association rules, which not only understand commonalities among uncategorized data but also find commonalities outside the data parameters. Possible use cases include determining how likely it is that a customer who purchased one item would buy a related product.

Unsupervised ML solutions can also help companies more easily understand data anomalies. They can detect data points that are unusual and determine if that specific information is fraudulent, such as flagging an unusual credit card transaction.

Pros And Cons Of Unsupervised ML

Companies can use unsupervised ML solutions to understand their vulnerabilities better and predict fraud trends instead of reacting to them after the damage has been done. Anti-fraud efforts are not the only potential benefits, however. Implementing unsupervised ML can also reduce demands on human employees.

Relying solely on humans for fraud reduction is a recipe for disaster, especially as they cannot keep pace with high transaction volumes. Unsupervised ML can prevent human workers from having to review billions of transactions and do so at rapid speeds to detect suspicious activity. The ability to review and categorize data in real time enables companies to react to fraud just as quickly. Companies can also save money and human resources on administrative tasks as workers are no longer needed to review potentially fraudulent transactions.

Unsupervised ML solutions promise many benefits, but FIs and merchants looking to implement them may perceive them to be costly investments. Organizations that have successfully implemented ML solutions often find that the additional revenue generated from minimizing good customer impact and the operational cost resulting from manual reviewers produces a lower total cost of ownership (TCO).

Companies cannot afford to let their guards down as fraudsters grow increasingly inventive and effective and make off with stolen data and assets. Having unsupervised ML solutions in place could flip the traditional fraud solution model, save human resources and help FIs and merchants understand new types of fraud as it unfolds.