Author Description

Risk analytics has seen a spike in interest and demand with the quantification of risks and global regulatory requirements. Financial institutions like banks, in particular, have to show evidence of having measured credit, market and operational risks using numbers and models rather than qualitative judgments. These corporations already have massive databases but automating the process to translate data into risk parameters remains a desire in most of them. In the past, this was partly due to the lack of cost-effective tools to accomplish the task. Modeling was done using software with output codes not readily processed by databases. Data have to be manually extracted and run on the models with results input into the databases manually again. With the increasing acceptance of open source languages, database vendors have seen the value of integrating modeling capabilities into their products. That has made it possible to insert models developed using R, Python or other languages directly into SQL scripts used for database transactions. As R or Python are free, there is no additional cost involved. Nevertheless, deploying solutions developed to automate the process remains a challenge. While not comprehensive in dealing with all facets of risks, the author with his wealth of consulting experience, aims to contribute to the development of risk professionals who will be able to progress beyond theories and concepts to create solutions that can support planning and automated decision-making.