So, what are risk analytics exactly? The financial meaning of the word doesn’t change much from the way we use it in our everyday lives. Risk goes hand in hand with reward — or the lack thereof. Thus, in modern financial predictive analytics, metrics have been generated to consider both estimated revenue as well as weight and audit financial statements for variables associated with a given investment or purchase.
Now, just as with any technological and data-driven adaptation to the world of business and finance, legacy platforms are a common obstacle. For example, many companies who have taken into account the valuable information derived from risk assessment have this entire process isolated or “siloed” from their financial assessment. This means that each of the models is being trained in isolation, each with its own data set, machine learning and analytics platform, and reporting generators. So, what’s the issue? What is there to gain from transforming the way your data is analyzed from isolated silos to a uniformed risk management approach?
When it comes to big data and analytics in the field of financial institutions and risk, the underlying cost that is often overlooked falls under the umbrella costs of IT. Whether your servers are localized, private, or public clouds, there’s no doubt that, in one way or another, your company is paying hefty costs depending on how much data you are collecting and utilizing for financial and risk assessment.
If your company is still utilizing a non-unified approach to financial reports and risk assessment, I’m willing to bet you’ve got the same set or sets of data stored in more than one location. It may not seem like much, but as your company grows, so will the amount of data that needs to be stored, replicated, and processed in each of these separate locations. Imagine the cost benefits of only having to store and process data in a single location and having it available to be pulled and run through a risk analysis or internal audit. When it comes to a company’s bottom line, sometimes a bit of investment upfront in data consolidation balances out with future cost reduction.
When your company decides it's time to move toward a uniform platform, there are a few key things to keep in mind during the transition. The first step is deciding who to appoint as the chief data officer, the person who has the final say and who takes responsibility for the whole process, for better or worse. They need to establish baseline processes and norms to be incorporated into the architecture of the unified platform. Upon considering regulatory requirements, you must design the platform that your company will utilize.
The key idea here is that if you’re going to make a change, don’t hold back — do your research and take advantage of newly developed technologies that may not have been accessible in your former system. For example, consider in-memory computing, which allows for the integration of all information in addition to analysis to be processed in memory — hence, ad hoc analytics and calculations can be delivered in real-time. From a technology perspective, it’s never made more sense to move towards a unified platform and realize the benefits of a unified approach to risk assessment and management.
The days in which a bank manages its performance using finance-only metrics such as net income are history. In the modern world of finance, those who fail to consider risk assessment or risk management as factors fail to optimize their banking.
