Anomaly detection involves identifying abnormal events, changes, or shifts in datasets. It aims to identify events or items that do not match the expected pattern. Unfortunately, anomaly detection has been made more difficult by big data.
Banks store and process huge amounts of data every day. With the transition from traditional banking to online banking, the need to safeguard this data is a priority for bankers for the well-being of customers and themselves.
Identifying unusual activities since they differ largely from normal activities is important in the banking industry. These anomalies can result from technical glitches, consumer behavior changes, accidents, malicious attacks, or incompetence. Anomaly detection raises alarm on suspicious incidents, such as money laundering, network intrusion, identity theft, account takeover, or fraud.
Human experts - no matter how well-trained - cannot practically cope with the ever-changing massive data points. Training machines, a process known as machine learning, is beneficial to an organization since it can handle huge data, do real-time comparisons, and is less expensive.
For an anomaly detection system to be effective, it should handle varying magnitudes of data, whether large-scale or small-scale. It should also account for frequency, referring to the rate at which data is likely to change, and whether the system used is static or dynamic. Three, conciseness or whether the system result should be at each metric level or the whole picture result is another consideration.
The anomaly detection system must have the ability to provide real-time results. A decision should be made on what period the anomaly is reported after detection. Whether immediately or after some given time. The last characteristic is whether the anomalies are defined or known prior and whether they can be grouped in the future.
In anomaly detection, there are three ways of going about it: unsupervised model, hybrid system, and manual system. An unsupervised model employs artificial intelligence and machine learning algorithms to pinpoint abnormal patterns without human assistance. In the current world of instant payments, machine learning is the most efficient for detecting strange patterns and returning real-time results.
The hybrid system is a strategy that employs both humans and machines. With experts defining what is normal and what is not. The machine picks what it has been trained as abnormal. The downside of this is the dynamic nature of data and associated threats.
For the manual system, a data professional helps to study charts, trends, graphs, meters, and other information, and apply industry knowledge to flag suspicious patterns. This method is time-consuming, prone to error, and unsustainable.
In the above three methods, using artificial intelligence, machine learning, and analytics in the banking industry is the most effective method in anomaly detection. This helps deal with new fraud patterns in multiple streams effectively. This is made effective, especially by task automation which saves banks valuable time and reduces the required personnel. Consequently saving costs.
Artificial intelligence also helps lower false positives. This is a situation where a genuine transaction is flagged as false. This situation irritates consumers and may hurt the bank's reputation. However, when artificial intelligence, if properly executed, can lower cases of false positives.
