In any business, making informed decisions is an essential step to reaching success. It was Sun Tzu who said “Know your enemy and know yourself and you can fight a hundred battles without disaster”. When it comes to the financial world, the enemy often isn’t a rival company or regulations from the Government. It’s information. Or lack thereof.
A company may have vast amounts of data but as many people and companies have come to learn over the years, having data is very different from having information. Although all activities rely on information to function and make decisions, financial activities are probably the ones that have developed information analysis techniques most, focused on the areas of risk analysis.
Risk intelligence techniques allows analysts to gather any available data, process it, and use it to make informed decisions about risks based on historical, current data and projected views of a business. Although the actual processes change depending on the techniques and information sources available, risk analysis involves identifying, extracting and analyzing internal data from the company/institution under review and external data such as counter-party credit risk management data, liquidity numbers, operational risk data, and several risk-based performance metric data.
The first problem the IT team in charge of managing all these data sources is how to make them work together as one, as they often come from many different origins, they have different database connector architectures, so that alone can quickly become a headache, unless there’s a clear path of implementation.
The second problem is, once all data sources are working in a coordinated manner, how to implement a risk analysis function that is capable of handling large datasets, as well as providing the necessary reports in an efficient way. Early implementations of Risk Analysis tools relied on Excel or MS-Access tools to provide the “intelligence” and process all the available data. While this approach worked for small and medium-sized data sets, its performance was very poor with large datasets, and did not use the full power of multi-core or multi-processor architectures.
With the advent of Service-Oriented Architectures (SOA), new products began to appear in the market, which filled the voids left by the previous, more traditional architectures of risk analysis solutions. For instance, Quartet Financial Systems (QuartetFS), with its ActivePivot™ provides a robust and powerful object-based Online Analytical Processing (OLAP) tool that provides in-memory analytics capabilities through its transactional engine and multi-threaded processing capabilities that can be integrated into any .NET application.
Moreover, thanks to its SOA-based architecture, it can scaled to fit any dataset size and provide risk analysis services to any number of clients, making it a very flexible architecture to work with (without the burden of setting up different database connections for each client or configuring each client separately).











