Fraud detection technologies can be used to add value, improve operational efficiency, and increase the security of IoT services.
This vendor-written piece has been edited by Executive Networks Media to eliminate product promotion, but readers should note it will likely favour the submitter's approach.
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Automated fraud detection systems are used in the financial services industry to detect various kinds of financial fraud. With millions of transactions occurring across a multitude of channels, including online banking, there are now many solutions that can detect unusual patterns and anomalous transactions in real time, based on transactional data and historical analysis of customer behavior. Financial institutions can now stop fraud before it happens, protect their customers more cost effectively, and comply with mounting regulations and industry mandates.
The emergence of IoT is only expected to compound the need for such systems. IoT devices generate massive volumes of data. It is practically impossible to manually analyse the data in real time to detect anomalies, making it imperative to plan for backend systems with some form of automated data collection and analysis capabilities. The fraud detection technologies can be used to add value, improve operational efficiency, and increase the security of IoT services.
The online fraud detection systems use data analysis to detect suspicious activities. The analysis techniques used generally can be categorised into statistical analysis techniques and artificial intelligence. Many systems may use combination of both technologies to provide a layered approach to fraud detections.
It is important to realise that fraud prevention is not a one-time activity. It is an on-going cycle that involves continuous monitoring across different channels, unification of all available data into the analytical process, detection and decision making in real time, and learning. Thus, IoT vendors need technologies that can ingest data at speed and volume, use sophisticated context-based decision models to react and predict threats in real time, and more importantly, self-learn and adapt from complex data patterns and usage. For example, if several devices, geographically close to each other, fail to work at the same time, it can be the result of an attack, but this could also be due to power failure or consumption issues. Being able to qualify the fraud patterns and translate them into an appropriate algorithm or logic that can process a huge amount of historical data is a crucial function of the fraud monitoring and detection systems.
From a data collection standpoint, whether it is a financial application or an IoT one, most of the data will be collected in a similar fashion. But, based on the risk assessment levels and use cases, rules or smart models can be set up differently to assess if the detected anomalies or dysfunctions are related to device malfunctions or fraudulent attacks. Depending on the use case, same system can be put in place with different models, to target different goals. A self-learning model would probably better fit the IoT use cases as compared to rules-based models used in the financial industry today.
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