Every business concern carries a risk. It could be a Market risk or Operational risk or Financial risk or all. You can go on based on industry segment and drill down to detail risk components within these broad classifications. Having said that, managing the risk begins with knowing the risk.

Until late 80’s the approaches to know the risk in the modern day’s business was done by judgmental approach by experts and occasionally is based on the empirical methods by collecting data using surveys.  

The scenario has fast changed and especially by mid 90’s, after the Information Technology revolution, there is abundant data available in electronic form, which could be transformed into business intelligence. Alongside, there are numerous new businesses available to help any organization with the external data. All this facilitates to have new approach in understanding the business and the risk associated with it.

From Information Technology point of view, the opportunities lies in building systems to gather data, consolidate the data and supply it to the risk analysts to do research and analysis. The IT can also help by bringing back the intelligence dugout by the risk analysts and incorporate into the business process.

There are lot of products and processes already available with the IT industry to augment the risk management process like: Data warehousing, Business Intelligence software, Decision Support Systems (DSS), Decision Engines, etc.

Data Warehouses: As the name suggests, these systems are the repository of historic data. The industry’s preferred option is to have relational database systems, which can store and handle large amount of historic data. There are few industries who prefer to have Mainframes using flat files format to house the data. No wonder Mainframe sales are still surging. Some leading suppliers of Warehousing systems include Oracle, Sybase, DB2, MS SQL server, etc.

The Data Warehouses are not the Realtime systems. The information is fed into the system from the operational systems that are OLTP (Online Transaction Processing) systems. The normal practice is to feed the DW system at regular intervals of time like every 2 hours, 6 hours or 24 hours. The data in the Warehouse should reflect the business transaction on the OLTP server. The structure of the warehouse should be simple and easy to modify. Especially in this mergers, acquisitions and consolidations era, where the business model and process keeps changing every now and then, it’s easy to replace the operational systems. But it’s very hard for a Data Warehouse to keep up the changes in sync with historic data.

Business Intelligence (BI) software: Traditionally BI software comes after the Warehouse. They help in data mining and analysis of the data. These are the tools used by the Statisticians and business analysts to analyze the data to build strategies and models. With the new technologies like OLAP (On Line Analytical Processing) capabilities, the BI software has expanded its sphere into the ETL (Extraction Transformation and Loading) process of Data warehousing.

The OLAP technique helps the data to be viewed in different dimensions with the drilldown capability. This helps in understanding the performance of different characteristics at a segmented approach. Also this technique helps in storing the data in the same format, also referred as cubes, which could be used to show the data in graphical format or creating reports almost instantly. Some popular BI systems available are: COGNOS, Business Objects, SAS, MS Excel, etc.

Decision Support System or Decision Engine: This is the integral part of the OLTP operational systems, which have the intended decision power, based on the outcome of the empirical analysis. There are different layers in this system like Segmenting section, Ranking if applicable, Strategy layer, etc. In simple, you can call it the brain of the system.

Generally Decision Engine resides on an isolated server and sometimes uses Artificial Intelligence (AI) architecture. Most of the Decision Engines are custom built, as they are different in different cases. StrategyWare and Strategy Manager are some of the Decision Engines used in Banking and Financial domain. Softwares like Siebel, PeopleSoft, SAP, etc can be used to accommodate the decision engines. Some sample applications include: Loan Approval process, Marketing campaign process, Air Traffic Control, etc.

Risk Management is the combination of all the layers of business from data gathering to Decision Engine. Or you can call it as understanding to mitigating the unearthed risk using the information technology. This is more of an art using science. The Risk Management process is evolving very quickly in Financial World with standardized metrics and process.

|