Both regulatory and competitive drivers are encouraging financial institutions to take a second look at their internal processes in order to reduce operational risk. As companies restructure their business operations and work to achieve compliance, they’re finding that operational risk reduction with the help of big data can be a competitive advantage. After all, these risk management programs can serve as motivation to streamline existing procedures and reduce costs.
But how do companies actually go about reducing their operational risk? In the age of big data, there’s more information than ever to help with that.
Companies just need to know how to use their data.
But First, What is Operational Risk?
There’s financial, systematic and market-wide risk. But today we’re talking about operational risk, which is anything that comes down to human dependency and error. This includes:
- Reactions to catastrophes like natural disasters
- Failure to adhere to internal policies
- Computer hacking
- And more
As I discussed in a recent article for Finance Magnates, “Historically, operational risk management was based on historical loss events and reserving the right amount of capital to ensure that the bank was secure if these events occurred. After completing their front-to-back assessments, financial services institutions now need to look forward and embed the right analytics within their target-state operating model to drive management decisions.” These forward-looking analytics systems will allow you to ask questions like:
- Which key risk indicators (KRIs) are applicable?
- How do you standardize capturing relevant data?
- How do you set up efficient processes to repeatedly measure, analyze and visualize those KRIs?
Operational Risk: Analytical Tools
In order to ask these questions of your data to effectively reduce your operational risk, you’ll need analytical tools that are both business-analyst friendly and can support massive amounts of structured and unstructured operational data. Remember, your data may be coming from multiple sources so you’ll want to be sure your system can handle that.
There are many tools out there for big data analytics. With business structures and processes that constantly change, it’s important to leverage a platform that:
- Enables business analysts to be in control of the data. After all, they know the business best.
- Handles massive amounts of structured and unstructured operational data.
- Integrates tightly with existing application and data platforms.
- Empowers full automation and scheduling to reduce manual intervention.
- Performs during times of operational or market stress.
- Offers enterprise-grade governance and security.
It’s a lot of work to think through this process and get it right. But it’s also the perfect time to redefine your data processes and update your architectures. Not only will it help you better measure and monitor operational risk, but you can also leverage this same exact investment to find other ways to improve other areas of your business.
To learn more about managing risk and other regulatory compliance concerns for financial services, plus how big data analytics can help, download this solution brief.