September 26, 2023
Explore the nature, payoffs, and applications of this technology in common fraud scenarios, along with some guidelines to streamline its adoption. Finally, find out how machine learning development experts can help you protect your company and customers from ever-evolving fraud threats.
of organizations experienced some form of fraud over the previous 24 months
PwC
of organizations already leverage AI and ML to detect and deter fraud
ACFE
of organizations plan to adopt AI and ML for fraud detection in the next 2 years
ACFE
ML-enabled anomaly detection applications differ from traditional software in terms of detection technique:
ML-based fraud detection systems are trained with large amounts of labeled data, previously annotated with certain labels describing its key features. This can be data from legitimate and fraudulent transactions described with "fraud" or "non-fraud" labels, respectively. These labeled datasets, which require rather time-consuming manual tagging, provide the system with both the input (transaction data) and the desired output (groups of classified examples), allowing algorithms to identify which patterns and relationships connect them and apply such findings to classify future cases.
These algorithms are fueled with unlabeled transaction data and have to autonomously group these transactions into different clusters based on their similarities (shared behavioral patterns) and differences (typical vs unusual patterns which can correspond to fraudulent activity). This approach, typically associated with deep learning, is computationally demanding but can be the only choice when facing fraud attempts that have never been met before and therefore unlabelled.
This trial-and-error approach involves multiple training iterations in which the algorithm performs a fraud detection task in different ways several times until it can accurately identify fraudulent and non-fraudulent attempts. Since it does not require labeled inputs, reinforcement learning can be applied without prior knowledge of the current fraud scenario. However, it requires considerable computing power.
Scheme title: Supervised vs unsupervised learning
Data source: medium.com — An Executive’s View: Introduction to Machine Learning
A subset of AI focusing on systems that can learn and improve with experience. Identifying patterns and anomalies is one of their most common tasks and a key enabler for fraud detection. “Traditional” ML solutions can be trained with a relatively small amount of data and don’t need substantial computational power, at least compared to deep learning-based software.
A machine learning method typically associated with complex sets of algorithms known as deep neural networks. Such structures, comprising connected layers of artificial neurons that mimic the human brain, can process data flowing from one layer to the next and spot the most subtle patterns and features hidden within an immense amount of data points. This particular process makes neural networks very effective in detecting fraud, but it also requires enormous computational power and large data sets for proper training.
A supervised learning algorithm which calculates the probability of one event out of two alternatives, such as "fraud" and "non-fraud" based on a set of relevant parameters.
Another algorithm of the supervised learning subset is a tree-like decision-making model in which every bifurcation represents the analysis of a certain metric or condition (spending threshold, location, etc.) to determine whether an operation is fraudulent.
A combination of several decision trees to further expand the amount of data types and conditions examined and identify non-linear relations among multiple variables.
Several systems rely on this supervised learning algorithm for credit card fraud detection because of its excellent performance with large datasets despite it being computationally demanding.
This supervised learning algorithm, which proves quite accurate but difficult to interpret when making a certain decision, can frame the nature of an event (be it fraud or non-fraud) by comparing it with similar occurrences recorded in the past.
Complex, multi-layered architecture and superior big data analysis capabilities make neural networks the go-to algorithms when it comes to spotting non-linear relations and dealing with unprecedented fraud scenarios through supervised, unsupervised and reinforcement learning.
Machine learning can be deployed to keep fraudsters and cybercriminals at bay in a variety of scenarios. Let’s take a look at some popular use cases of this technology.
Financial institutions have come to understand the synergistic potential between the stock market and machine learning and the benefits of predictive analytics in finance, considering the large sums involved and the required compliance with increasingly strict regulations.
ML-driven systems can help prevent financial fraud, such as churning, spoofing, and wash trading, by spotting anomalies in stock traders' activity and cross-checking transactions and brokers' data to detect inconsistencies in the information provided.
Applying machine learning in banking has proved useful to track anomalous transactions that could be a sign of criminal activity, such as large sums of money exchanged among a group of newly-established companies registered in tax havens.
Machine learning models can be trained with data relating to three possible scenarios: lawful transactions, money transfers flagged as suspicious by bank alert systems, and potential money laundering cases reported to the authorities. For each case, machine learning systems analyze the senders’ and receivers' background or their previous transaction histories. This way, they can spot the same patterns once they recur in future scenarios and therefore distinguish between legitimate activities and criminal actions.
Electronic payment fraud represents a widespread crime taking many forms and encompassing almost any industry, from banking to ecommerce. However, machine learning systems in retail have a good chance of containing this escalation by detecting abnormal account behavior that could be a sign of fraud. Some of the factors to consider are a growing transaction frequency (especially to purchase premium goods), payment on a card carried out significantly before the due date, associations with different high-risk accounts, and multiple payment methods added in a short time.
Machine learning-driven systems for payment fraud detection can also update card users' behavioral profiles after each transaction, making future predictions more precise and avoiding false positives.
Another type of crime strictly connected to payment card fraud and remarkably widespread in many scenarios, including fraudulent loan applications and ecommerce scams, is identity theft. This involves a scammer exploiting the personal information of another individual, including their name and credit card number, without their consent to commit a crime. To deal with this kind of fraud, we can count once again on machine learning-driven analysis of users' habits and transaction data.
Furthermore, we can leverage machine learning-powered computer vision to analyze identity documents or add additional verification mechanisms such as face recognition and biometrics.
Machine learning solutions can also strengthen fraud detection in the insurance sector, especially in health insurance. Algorithms can identify false and duplicate claims, pointing out a customer who reported an incorrect diagnosis or exaggerated medical coverage costs.
One of the most valuable machine learning techniques in healthcare is natural language processing, which ensures an in-depth analysis of unstructured data such as medical reports. These solutions leverage machine learning to scan documents written by doctors, insurers, or clients, searching for suspicious inconsistencies.
Public authorities can use machine learning capabilities in identifying unusual patterns to enhance audit and tax compliance. For example, ML can examine the general ledger for anomalous entries which could be the signs of attempted fraud.
Algorithms can spot a wide range of clues easier and faster than human auditors, taking into account various parameters. These include monthly variations in companies' gross sales, relations among different taxpayers, inconsistencies among purchases, or the itemized deductions in an income peer group.
