Consumers and businesses worldwide are losing billions of dollars every year to neverending attacks from cybercriminals. Financial institutions spend billions more investigating and recovering the stolen money. As attacks become more and more sophisticated, money-handling companies need to incorporate strong fraud-prevention mechanisms into their strategies to protect their customers and themselves from unnecessary expenses.
The ever-growing amount of data captured by financial institutions makes anomaly detection an invaluable tool for identifying fraudulent transactions and behaviors.
Cybercrime complaints and reported losses 2015-2019
Source: FBI Internet Crime Complaint Center
Table of contents
What is anomaly detection?
Anomaly detection in financial transactions classifies data into normal distribution and outliers. When a transaction or a data point deviates from a dataset’s normal behavior, it can be considered potentially fraudulent.
How does anomaly detection work in payments and finance?
The anomaly detection approach for transaction data is advantageous because it provides simple binary answers. Any unexpected change from normal data patterns or an event that does not conform to model predictions is considered an anomaly. If a transaction looks suspicious and potentially fraudulent, the system may ask the customer to verify details or go through additional verification steps. By analyzing multiple data points, anomaly detection can be applied to flag technical outages, glitches, and potential opportunities such as a positive change in consumer behavior.
However, there are no universal patterns or business as usual when it comes to everyday life. The same unusually large amount of payments expected on Black Friday would stand out on any other day, and vice versa. But even the most well-established peaks in the natural business cycle can shift from time to time.
The coronavirus pandemic, for example, resulted in a skyrocketing volume of online payments and a fall in in-store purchases. Datasets used to train static anomaly detection systems didn’t have any similar historical patterns, which resulted in countless transactions being flagged as fraudulent when they were not. Many financial institutions worldwide saw their anomaly detection anti-fraud systems fail for this exact reason.
Machine Learning powered anomaly detection
Incorporating Machine Learning (ML) anti-fraud systems is an advanced approach that reduces uncertainty by automating the complex anomaly detection process. ML algorithms can be used to find the very subtle and usually hidden events and correlations in user behavior that may signal fraud. By comparing numerous variables in real-time, anomaly detection with machine learning can process large datasets to determine the likelihood of fraudulent transactions or actions.
ML has been used to spot fraudulent transactions since the 1990s. Since then, the technology has matured to track and process transaction size, location, time, device, purchase data, and many other variables simultaneously. ML-enabled anomaly detection can process much more financial data much faster than human rule-based systems. Smart algorithms that monitor consumer behavior help to reduce the number of verification steps that impede the consumer purchasing journey and reduce false positives, drastically improving user experience.
Real-time anomaly detection in financial transactions enables companies to immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraud otherwise. By eliminating the delay between spotting the problem and resolving it, payments and finance companies maximize the efficiency of their anti-fraud strategies.
Manual anomaly detection with a human monitoring a dashboard with a few KPIs is not scalable to millions of transactions consumers make every day and millions more metrics associated with them. Maintaining real-life responsiveness requires a sophisticated anomaly detection system powered by machine learning that can monitor and correlate multiple complex metrics with different amounts of variability to sift through millions of data points every second.
Source: Federal Trade Commission, Consumer Sentinel Network
Anomaly detection: build vs. buy?
The importance of fraud detection for payments and finance companies is hard to overstate. Real-time anomaly detection is already used by leading financial institutions worldwide to prevent losses from occurring in the first place. Businesses aiming to stay a step ahead of cybercriminals can either buy a complete anomaly detection system or build it from scratch.
To make the right decision that will generate the greatest return on investment, companies need to consider their size and the volume of financial data that must be processed. The budget and time to value tie in with the capacity for development and maintenance of the IT team building it. Lastly, it is essential to factor in future growth and how it will impact all of the previous factors. Real-time anomaly detection for transaction data is a sophisticated tool that requires specialist knowledge and an expert IT team to develop. Building from scratch enables complete control over the final product but includes a great deal of uncertainty. Partnering with a technology vendor minimizes risk as anomaly detection can be integrated quickly and predictably.
What is SDK.finance?
How do you do anomaly detection?
The process of identifying unexpected items or events in data sets, which differ from the norm. And anomaly detection is often applied on unlabeled data which is known as unsupervised anomaly detection. Anomaly detection has two basic assumptions:
Anomalies only occur very rarely in the data.
Their features differ from the normal instances significantly.
What are the specific ways anomalies are detected?
The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of a distribution, including mean, median, mode, and quantiles. Let’s say the definition of an anomalous data point is one that deviates by a certain standard deviation from the mean.