Fraudsters are quick to take advantage of any flaw in technology or process. This has made online financial transactions vulnerable as the financial services industry struggles with the pace of digital change. Humans alone cannot keep up with the deluge of data streaming through the global network.
AI-driven machine learning tools that detect suspicious patterns in real-time is one possible solution. Guided by input from human supervisors, these algorithms go a long way in stifling fraudulent transactions in multi-layered defenses.
Here is an excerpt from a report published in DataQuest India:
Effective detection and deterrence require that fraud strategists gain a holistic view of the threat landscape and adopt a multi-layered defense system for a balanced strategy. They are striving for a balance between robust measures to counter fraud and providing a positive customer experience, where the goal is not only curbing losses resulting from fraud but also maximizing revenue A unique approach which facilitates real-time fraud detection, thereby driving fast decision-making and responses to emerging fraud threats is the need of the hour.
It comes as no surprise then to see banks and financial institutions turning towards advanced solutions that incorporate Artificial Intelligence (AI) and Machine learning (ML) technologies. It is easy to understand why ML has caused such a stir in the fraud detection domain; it is equipped to deal with large volumes of data from numerous sources and capable of spotting abnormal patterns and links that humans are not able to identify. It is therefore natural that financial institutions are deploying it as a viable tool for fraud detection.
Fraud detection methods in India today are evolving from rules-based towards pattern recognition, with ML’s ability to recognize patterns in consumer behavior. It can also be used to protect companies from insider fraud, as it can study data access from within the organization and identify any anomalies in individuals deviating from their day-to-day jobs or exposing data to outsiders. Adding AI to the mix gives ML the much-needed edge to move beyond just algorithm-based fraud detection. Machines can be programmed to self-learn in an unsupervised model with AI so that transactions that do not conform to a set pattern are identified and therefore can be actioned upon – in real-time. Especially in the enterprise context, it’s vital that a robust fraud detection tool can collect data and detect anomalies across various channels and plugin in all the gaps to prevent further misuse.
