Banks will always use traditional relational databases throughout their IT infrastructure, where they can function as valuable systems of record. In a digital economy, however, where the end-customer experience is everything, banks will increasingly look to create and integrate IoT, mobile and AI applications. These apps require a database to match, which can also function as a system of engagement. This is where non-relational (NoSQL) database technology enters the market. Unlike traditional relational databases (SQL), NoSQL is particularly well suited to tasks that require fast access to data, from a variety of sources as well as systems that can adapt to changing market conditions. Not only can NoSQL databases scale out at a moment’s notice they can resolve issues of data management with little cost in terms of time and budget.
the database is also key in solving issues of replication, content management and real-time availability of big data. Choosing the right database will allow theFS industry to move with the times without the worry of detracting from it score offerings or requirements. Instead, businesses need to support their growing data requirements with flexible and scalable data management solutions to increase and maximise their profits with minimal investment.
Going back to the issue of fraud, effective fraud detection requires an FS organisation to process a huge amount of customer purchasing data. With the help of detection algorithm rules, customer information, transaction information, location and time of day data,banks can quickly and effectively pinpoint transactions that appear to be out-of-character. However, in order to sift through this data, regardless of seasonal peaks in purchasing or time of day, FS companies must have a database with low latency and huge scalability.
the database is also key in solving issues of replication, content management and real-time availability of big data. Choosing the right database will allow theFS industry to move with the times without the worry of detracting from it score offerings or requirements. Instead, businesses need to support their growing data requirements with flexible and scalable data management solutions to increase and maximise their profits with minimal investment.
Going back to the issue of fraud, effective fraud detection requires an FS organisation to process a huge amount of customer purchasing data. With the help of detection algorithm rules, customer information, transaction information, location and time of day data,banks can quickly and effectively pinpoint transactions that appear to be out-of-character. However, in order to sift through this data, regardless of seasonal peaks in purchasing or time of day, FS companies must have a database with low latency and huge scalability.
