During the last 18 months, I have had the pleasure of attending numerous conferences, forums, events and webinars focused on Risk and Regulation. Although often considered as not the most ‘glamorous’ topic, I have noticed many significant changes already taking place, including increased interest and investment in new technology.
As a result, I have decided to summarise some of the key points that come up repeatedly when technology and risk are discussed together:
- How technology can add value? Most banks, insurers, and financial institutions still use Excel for model creation and validation as well as risk reporting. Most of the models are not well developed and need separate platforms to complete the data picture. Companies that want to be ahead of the curve invest, or express the desire to invest, in third party platforms that have the potential of bringing them to the forefront of technology and user experience (UX). Such technology enables industry leaders to steer the business rather than just navigate the numbers.
- Automated Reporting. Some of the companies have partnered with external providers and implemented advanced technological solutions to automate data gathering and data consolidation. As a result, they do not need static spreadsheets anymore. However, such automation often occurs only in one department / business line and does not extend to the larger group. Consequently, data gathering and consolidation on a group level poses similar problems as before, stemming from “all kind of spreadsheets” scattered across the bank/company.
- Data Issues. Many institutions express the difficulty in dealing with data, especially if the bank/company has international/global operations. Data challenges often become insurmountable. Standardizing data coming from multiple sources and legacy systems poses a big challenge, as does back testing and modelling the data. Banks would strongly benefit from the platform that allows running multiple what-if scenarios and stress tests. Integrating ad-hoc reporting would also add substantial long-term benefit to bank operations and risk management. Getting the data right is the first step.
- Data semantics and RDA (Risk Data Aggregation). Almost all G-SIBs (Global Systemically Important Banks) allude to the problems with RDA. Classical data warehouses led to data duplication. Even one risk area, for example Credit Risk, could have as much as 120 data sources. RDA projects can take up to 3-5 years. At Luxoft, we have observed two different models. First, firm wide financial data warehouses where data lake is created by financial department and include all data. Second approach is a smaller scale data store with detailed data definition or ontology approach, which works well in one department, for example a bank’s equity department. Building data mapping frameworks, which include data quality measures and scores, proves to be the right long-term sustainable approach.