Machine learning is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. Part of the appeal of machine learning is that it is highly adaptive and able to evolve over time as algorithms and statistical models are trained and retrained on new datasets.
How does machine learning work?
Machine learning operates through a process that mimics the way humans learn. At a high level, machine learning follows four simple steps:
Large sets of data are fed into a machine learning system; in the context of fraud detection and prevention, these datasets might include transactional data, user data, and historical fraud reports
The machine learning system uses these datasets to train models. Training involves an algorithm “learning” from the data by finding patterns and features that correlate with certain outcomes — for example, distinguishing fraudulent transactions from legitimate ones
Once the model is trained, it’s tested on a new, previously unseen dataset. This testing is intended to assess how well the model can apply its learned patterns to new data and scenarios
After evaluation, the model is deployed to make predictions or decisions based on new data. Over time, as more data is collected and feedback is received, the model can be refined and improved, enhancing its accuracy and reliability
What is the difference between supervised learning and unsupervised learning?
Though there are a wide variety of machine learning techniques, they all fall into one of two categories: supervised learning or unsupervised learning.
Supervised learning is a technique in which a machine learning model is trained on a labeled dataset. This means that each example within the training set is paired with an output label, with the output serving as an example for the model to learn from. The model receives direct feedback on its predictions to adjust and improve its accuracy, with the goal of mapping from inputs to outputs for predictions on new data. Supervised learning is commonly used for classification (where the output is a category) and regression (where the output is a continuous value) tasks. Conversely, unsupervised training involves training a model on data that does not have labeled outputs. Instead, the model infers the natural structure present within a set of data points on its own. Common use cases for unsupervised learning include clustering (grouping similar examples) and associations (identifying patterns of co-occurrence) tasks.
What is the difference between machine learning and artificial intelligence?
As noted, machine learning is a subset of AI. AI aims to create machines capable of performing tasks that would typically require human intelligence, such as interpreting complex data, recognizing patterns, making decisions, and learning from experiences. AI is not limited to a single technique or approach but rather encompasses a variety of methodologies and technologies, including machine learning. Machine learning models enable AI systems to learn from data, adapt to new scenarios, and improve over time.
Why are traditional fraud detection and prevention methods ineffective?
As fraudsters’ schemes grow more sophisticated, the limitations of traditional fraud detection and prevention methods — many of them static and rules-based — become increasingly apparent.
Let’s look at the pitfalls of traditional approaches to fraud management and how machine learning can help organizations overcome them:
Rules-based limitations: Rules-based fraud management systems struggle with adaptability due to their reliance on fixed rules that quickly become outdated in the face of emerging and evolving fraud tactics. These systems require frequent manual updates and revisions to remain effective, which can be time-consuming and resource-intensive. As new forms of fraud emerge, the lag time in updating these systems can create a window of opportunity for fraudsters to exploit. Machine learning models provide a dynamic solution to these limitations by efficiently and continuously learning and adapting to new data patterns. These models detect and adjust to new and sophisticated fraud schemes in real time, uncovering complex patterns that rules-based systems often miss. Newer innovations such as generative AI or outside-the-box approaches to rules engines are also helping to improve security, ensuring that fraud management keeps pace with the constantly evolving threat landscape.
False positives: Traditional systems are often rigid in their thinking and can be hyper-sensitive, contributing to higher rates of false positives, where legitimate transactions are flagged as fraudulent. These false positives are frustrating and inconvenient for customers and require considerable time and effort to review, jeopardizing customer satisfaction and wasting valuable resources.
Compared to traditional rule-based systems, machine learning models are capable of complex pattern recognition and continuously learn and adapt to new data. These qualities enable them to detect nuances in customer behavior, making them far less likely to misclassify legitimate transactions.
False positives are a particularly tricky area to navigate, which is why ACI takes a multilayered approach to fraud management and false positives. We utilize machine learning in coordination with global consortium data — one of the largest repositories of data in the financial sector — along with specialized models including account take over and anomaly detection to accurately predict fraudulent behavior.
Limited scalability: Traditional systems rely on rule-based methods, which require manual input and constant updates. The manual effort required to update and maintain these systems is substantial — especially as transaction volumes increase over time — resulting in slower processing and severely limiting their scalability. Machine learning can solve this problem by utilizing automated pipeline support — such as self-learning algorithms or human intervention — to help the models improve and adapt as they process more data. Machine learning’s dynamic ability to analyze vast quantities of transaction data in real time is ideal for sustainably scaling fraud detection and prevention.
Delayed response times: Traditional fraud detection systems are dependent on batch processing, which can significantly delay response times — in some cases, by the time the system detects fraudulent activity, the damage is already done. Manual reviews introduce further delays, as human fraud analysts need time to sift through alerts to identify genuine cases of fraud. Taken together, these factors result in a reactive approach, which can lead to potential losses and damage to consumer trust.
A computer programmer or hacker prints a code on a laptop keyboard to break into a secret organization system.
By comparison, machine learning algorithms continuously analyze transaction data as it comes in, instantly identifying anomalous and potentially fraudulent behavior. This shift to real-time processing enables organizations to detect and respond to fraudulent activity immediately, minimizing risk. Machine learning-based fraud management systems also reduce dependency on manual reviews, automatically assessing the risk level of transactions and optimizing the decision-making process.
Resource intensive: Traditional fraud prevention methods require large teams of fraud analysts to manually sift through numerous false positives to identify fraudulent activity, a process that’s both time consuming and error prone. Moreover, the need for continuous updates to the rules that govern traditional systems further taxes available resources, making it difficult for organizations to keep up with evolving fraud tactics.
Organizations can automate much of the decision-making process by using machine learning models. These models can analyze large datasets rapidly and with remarkable accuracy, identifying potential fraud without the need for constant human oversight. This real-time detection reduces response times and frees up fraud analysts to focus on other value-adding tasks.
How does machine learning for fraud detection and prevention work?
The process for training a machine learning model to support fraud detection and prevention entails:
Data collection: Gather data relevant to transaction and user behavior from various sources, including customer relationship management systems, payment processing systems, and transaction records. During this first stage, an organization gathers data relevant to transaction and user behavior from various sources, including customer relationship management systems, payment processing systems, and transaction records. This data will serve as the foundation for the entire machine learning workflow providing the raw material from which it derives fraud detection and prevention insights.
Data cleansing: Cleanse data before using it for model training. This step of the process ensures the accuracy and consistency of data by removing duplicate entries, correcting errors, and filling in any missing values. By cleansing data, organizations improve the reliability of the models they build.
Feature engineering: Transform raw data into meaningful attributes or features for recognizing patterns related to fraud. Feature engineering might include creating new variables such as transaction frequency or account age, or complex features such as geographical distance between transaction locations. The quality and relevance of these features will directly impact the machine learning model’s performance.
Sampling: Oftentimes, full datasets are too large to train machine learning models on. Instead, organizations must select a subset of data that is smaller and more manageable while still being representative of the general distribution of data. Known as sampling, this approach ensures efficiency and scalability in the model-building process and can help reduce biases.
Model selection: It’s important to choose the right machine learning model based on the type of fraud it’s meant to detect and the characteristics of the data it will analyze. Common machine learning models for fraud detection and prevention include logistics regression, decision trees, neural networks, and ensemble methods such as random forests and gradient-boosting machines.
Model training: Train the selected model on labeled historical data. The goal here is to train the model to accurately distinguish between fraudulent and legitimate transactions, optimizing for both high detection rates and a low rate of false positives.
Model testing: Test the machine learning model on a different dataset than the training sample to evaluate its ability to generalize new data.
Deployment: After testing and refinement, integrate the new machine learning model into the transaction processing workflow, where it will provide a risk score for each transaction in real time, indicating the likelihood of fraud. This score then enables the organization to decide whether the transaction should be permitted, reviewed, or blocked.
Continuous monitoring: Post-deployment, continuously monitor the machine learning model to ensure it remains accurate over time. This involves tracking the performance of the model and identifying any new forms of fraud that may not have been present in the training data.
Model updating: To ensure a model’s continued relevance and accuracy, periodically update it with new data that reflects the latest fraud trends and tactics. Retraining the model will help it adapt to changes and maintain high performance.
What are some examples of the different types of machine learning fraud detection and prevention models organizations can use?
The type of machine learning model an organization chooses to use for its fraud detection and prevention strategy depends on several factors, including the type of fraud and the characteristics of the dataset. Here are three of the most common machine learning models used for fraud detection and prevention, and their optimal use cases:
Neural networks
A neural network is a series of algorithms that recognize the underlying relationships within a dataset through a process that mimics the way the human brain operates. Neural networks are composed of layers of interconnected nodes, known as artificial neurons. Each of these nodes performs simple computations using input data and then outputs the results of those computations to the next layer. The network “learns” by adjusting the weights of connections based on the errors in predictions during the training process.
Neural networks are especially effective at analyzing complex, highly dimensional datasets, such as labeled images, making them the preferred model for identifying document fraud.
Random forests
A random forest is an ensemble learning method often used for classification and regression. With this model, an algorithm constructs multiple decision trees during training and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Random forests compensate for decision trees’ habit of overfitting to their dataset by creating an ensemble of trees on different subsets of data and using the average to improve predictive accuracy. These decision trees work together to refine predictions, which makes the random forest model highly effective for diverse datasets, including numeric and categorical data. For this reason, institutions in the financial services industry often use random forests to detect fraudulent activity in transaction data.
Anomaly detection systems
An anomaly detection system is used to identify unusual patterns within data that do not conform to expected behavior. One of the most common examples of an anomaly detection system is an isolation random forest, which is also an adaptation of the random forest method.
An isolation random forest works by isolating anomalies instead of profiling normal data points. It also isolates observations by randomly selecting a feature and then randomly selecting a split value between the minimum and maximum value of that feature. Anomalies should, in theory, be few and easy to distinguish, making them easier to isolate. As a result, they have shorter paths within the random tree structure, allowing for more efficient anomaly detection, even in large datasets.
Anomaly detection systems can be used when there are no labeled fraud examples, and fraud is treated as an outlier; in this case, the algorithm detects any transactions that deviate significantly from the norm.
Which types of organizations can benefit most from machine learning-based fraud management?
Any organization that handles large-volume transactions, processes sensitive user data, or operates in a high-risk sector stands to benefit from using machine learning for fraud detection and prevention. This may include financial institutions, merchants, healthcare providers, government agencies, or any other organization that faces an elevated risk of fraud.
Machine learning’s real-time analysis and processing capabilities enable organizations to respond to fraudulent activity swiftly and decisively — an absolute necessity in industries where the integrity of transactions not only impacts financial outcomes but also consumer trust and regulatory compliance.
How can organizations use machine learning with their existing fraud management teams?
Machine learning is the perfect supplement to any fraud management team capable of automatically identifying both complex and subtle fraud patterns that might otherwise be difficult for humans to detect. By integrating machine learning tools into existing workflows and using them to rapidly analyze large datasets, organizations can free up fraud analysts to investigate more sophisticated cases and refine overall fraud management strategies. This not only increases efficiency, but also empowers analysts to deploy their expertise where it’s needed most, optimizing team performance and improving the organization’s security posture.
What are the limitations to using machine learning for fraud detection and prevention?
While machine learning is a powerful tool for fraud detection and prevention, it isn’t foolproof. There are some key limitations to be aware of, including:
Data dependency: Machine learning models require large volumes of high-quality data to perform as intended. If data is sparse, outdated, or biased, it can compromise the model’s accuracy, so it’s imperative that organizations invest in robust data collection processes and thoroughly cleanse all data before feeding it into machine learning models.
Model complexity: Certain models are more complex than others and can easily become “black boxes,” where the decision-making process is not transparent or understandable to users. These black boxes are difficult to troubleshoot, and their specific detections or predictions are often inexplicable. When selecting machine learning models, it’s essential to strike the right balance between accuracy and interpretability — for example, a benefit of random forest models is how they use decision trees to illustrate how they make predictions. Additionally, techniques such as SHAP values (Shapley Additive explanations), feature importance, and partial dependence can help explain and contextualize model decisions, making them relevant to stakeholders.
Overfitting: Models can overfit the data on which they’re trained, making them accurate in training scenarios, but inaccurate when analyzing previously unseen data. Using robust sampling techniques enables models to generalize better in production and avoid potential bias. It’s also critical for organizations to understand what biases exist in their data and adapt their sampling processes accordingly.
Bias: If training data contains biases, models can inadvertently perpetuate or even amplify them. This can lead to unfair treatment, such as a higher rate of false positives for a specific demographic. Organizations can mitigate bias within models by using diverse and representative training datasets and regularly auditing model decisions to ensure fairness and prevent discrimination.
Changing tactics: Fraudsters are continuously changing their tactics to stay one step of fraud management systems, which means machine learning models must be continuously trained and retrained to recognize emerging threats.
Privacy concerns: Machine learning models are specifically designed to process and analyze large datasets — datasets that may include consumers’ personal data, which has led to concerns about user privacy and the misuse of personal data. Organizations can address these privacy concerns by implementing robust data anonymization techniques and ensuring strict compliance with data protection laws and regulations when processing and analyzing personal data.
Lack of accountability: Since machine learning models are highly automated, it can be difficult to determine accountability for the decisions they make, such as false positives or blocking legitimate transactions. To establish clear accountability for model outcomes, organizations must painstakingly document model behavior and decision-making frameworks.
Lack of transparency: There is a growing need for organizations to obtain consent from individuals whose data they use to train machine learning models and to be transparent about how they intend to use that data and why models come to certain conclusions.
Compliance: Adhering to legal standards and maintaining compliance with international laws concerning data protection and fraud management can be challenging, especially when training ML models on cross-border datasets. Therefore, it’s important that organizations keep close tabs on existing and emerging data protection laws and rigorously review and adjust their machine learning practices ensuring they align with current legal standards.
How does ACI Worldwide leverage machine learning for fraud detection and prevention?
At ACI Worldwide, machine learning is an integral component of all our fraud management solutions. We employ incremental machine learning, which refreshes models in real time based on streaming data, as a key tool for preventing model degradation. Also, our network intelligence technology enables organizations to securely share and feed industry-wide fraud signals to their models alongside proprietary data.
All these solutions are part of our dynamic, innovative approach to fraud detection and prevention.
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