Transaction Anomaly Detection

In today’s digital age, financial and claim transactions occur at lightning speed, and detecting anomalies in these transactions is crucial for preventing fraud and ensuring security. To address these issues, ORMB offers machine learning techniques to analyze historical transaction data and detect anomalies within current datasets. This will help the business in various ways:

• Identify suspicious activities and behaviors which will help to take immediate actions and prevent monetary losses.

• Flag inconsistencies and errors in data at an early stage.

• Identify potential breaches in real-time, thereby minimizing damage or lost opportunities.

• Streamline processes by quickly addressing errors before they compound.

The transaction anomaly detection feature built using machine learning is a predictive analytics tool. It is designed to read historical transaction data to identify unusual patterns or behaviors, and thereby detect anomalies in the transactions before billing them to the customers. ORMB enables you to detect the following types of anomalies:

• Transaction Level Anomaly – Here, the system enables you to detect anomalies in the data (i.e., values) received in a transaction. For example, the data received in the TXN_​VOL, TXN_​AMT, UDF_​AMT_​1, UDF_​AMT_​2, UDF_​AMT_​3, UDF_​AMT_​4, UDF_​AMT_​5, UDF_​AMT_​6, UDF_​AMT_​7, UDF_​AMT_​8, UDF_​AMT_​9, UDF_​NBR_​1, UDF_​NBR_​2, UDF_​NBR_​3, UDF_​NBR_​4, UDF_​NBR_​5, UDF_​NBR_​6, UDF_​NBR_​7, UDF_​NBR_​8, and/or UDF_​NBR_​9 columns.

• Daily Aggregate Anomaly – Here, the system enables you to detect anomalies in the daily aggregated transaction amount.

• Daily Count Anomaly - Here, the system enables you to detect anomalies in the daily aggregated transaction count.