Financial institutions worldwide spend billions of dollars every year investigating and recovering money lost to fraud. As cybercrime becomes more sophisticated, money-handling companies need to incorporate strong fraud-prevention mechanisms into their strategies to protect their customers and themselves from unnecessary expenses.

Anomaly Detection utilizes the ever-growing amount of data captured by financial institutions to identify fraudulent transactions and behaviors in real time. By automatically classifying data into normal distribution and outliers, companies can immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraud otherwise.

Pavlo Sidelov, the CTO of SDK.finance, said, “Modern fraud prevention is expensive. Digital ID checks cost around $2 per document and companies spend millions on KYC and AML. Card-based payment systems worldwide generate fraud losses of up to $30 billion per year. That’s why financial institutions have to prevent financial losses and fraud at the earliest stage. Manual anomaly detection is not scalable to millions of transactions consumers make every day. This is a big automation challenge. Only Machine Learning based anomaly detection tools can identify potential anomalies on the fly.“

History is full of examples of human labor being replaced with the help of automation. Take telephone switchboards operated by workers at the beginning of the 20th century, for example. Could someone then imagine that this process would be automated?

Data science and automation tools take loss prevention from fraud in payments to a new level. Anomaly Detection provides simple binary answers for financial institutions. If a transaction looks suspicious and potentially fraudulent, the system may ask the customer to verify details or go through additional verification steps. Any flagged transaction past or present is accessible to authorized personnel via a dashboard with in-depth information and metrics. Anomaly Detection can be applied to flag technical outages, glitches, and potential opportunities such as a positive change in consumer behavior by analyzing multiple data points.


Anomaly detection Dashboard powered by SDK.finance

As the global pandemic demonstrated, even the most well-established peaks in the business cycle can change. The skyrocketing volume of online payments and a fall in in-store purchases brought static anomaly detection systems into disarray because training datasets did not have remotely similar patterns. The new feature incorporates Machine Learning (ML) to reduce the uncertainty associated with modeling future behavior from past datasets.

By tracking and processing location, time, device, purchase data, and many other variables in real-time simultaneously, ML-enabled Anomaly Detection can process much larger datasets with much better accuracy. ML algorithms can find the very subtle and usually hidden events and correlations in user behavior that may signal fraud.

Anomaly Detection monitors consumer behavior to reduce the number of verification steps that impede the consumer purchasing journey and reduce false positives, drastically improving user experience. By eliminating the delay between spotting the problem and resolving it, payments and finance companies maximize the efficiency of their anti-fraud strategies.

The Anomaly Detection feature is the latest addition to SDK.finance, a white-label digital payment platform with everything banks and financial institutions need to build payment products in the shortest time possible.