IoT and big data provide sufficient storage, processing, and transmission of data which is more secure. They offer advanced analytics that extensively tracks any potential data violations
FREMONT, CA: In a research study by MarketsandMarkets, the global baking and financial services IoT market is anticipated to grow from $249.5 million to $2.03 billion by 2023. IoT technology in the retail industry is also forecasted to grow by $35 billion by the end of 2020. Hence, the disruption of IoT and big data in financial and retail services will be significant.
Here are four key IoT and big data trends in financial and retail services:
IoT will Revolutionize Point of Sale (POS) Payments
The emergence of smart POS systems will see a growing adoption of biometric POS, mobile POS payment, digital product tracing and more. IoT and big data provide sufficient storage, processing, and transmission of data which is more secure. It also offers advanced analytics that extensively tracks and alerts of any potential data violations, making POS systems more secure and reliable while improving the retail industry.
IoT will Enhance and Automate Security in Financial Institutions
Financial institutions and retailers can deploy connected smart cameras and motion sensors to improve security. These technologies can automatically respond to unauthorized third parties or intruders and also protect against environmental hazards that can be a security threat or damaging.
IoT will Streamline Risk Assessment
IoT and big data will make it easier to obtain vast volumes of data to determine a customers’ risk status. The data can then be smartly examined so that any company or individual's risk profile becomes convenient and easy. It can also be used to predict future risks for individuals and organizations.
More Efficient Inventory Management
There will be an increased adoption of automated IoT inventory management system that can make real-time tracking of inventory seamless at the point of sale. This system automatically provides data of current in-stock items and effectively evaluates the data to forecast future inventory needs.