Measuring risk has never been an exact science. However, risk analytics allow you to analyze the impact of potential risks, enabling financial services firms to implement forward-thinking strategies for risk management and drive true digital transformation.
Having risk management at the core of the digital transformation process allows organizations to look beyond traditional risk and define the controls needed as they consider the nature and level of digitizing. Additionally, risk analytics allow you to understand and act on data to accelerate the decision-making process for more meaningful and actionable insights. By using analytics to identify, categorize and address risk exposure along the transformation journey, risk managers will realize efficiencies and cost-effective results.
Successful risk management requires an effective data management strategy that elevates data assets, analytics, daily business processes, and people towards business goals and outcomes.
Data is Key to Risk Management
By addressing data as the fundamental starting point, risk managers have a unique vantage point to capitalize on the vast amount of information that they capture to evaluate, monitor, and mitigate risk. However, it can often be difficult to find accurate data throughout your organization. As a result, data assets proliferate, compounding the problem and creating inefficiencies and delays.
An analytic platform helps bring together all of an organization’s data, make it searchable, and converge it with automated processes that can be shared across departments and with people. From there, leaders can devise strategies based on metrics that drive growth, ensure the data’s good, and capitalize on revenue.
One of the major challenges facing those in financial services (or any industry for that matter) is getting the right data right.
When facing the challenge of having accessible and accurate data, it is important to have tools that handle high volumes of data across multiple sources, including quantitative and qualitative data.
To be effective, risk analysts need the flexibility to access all data, regardless of data type, and getting a full analysis requires pulling data from multiple sources. According to a Grant Thornton study, “85% of banks believe that many (and additional) efficiencies could be realized in data and risk information management in their organization.”
Why Does True Data Transformation Remain Elusive?
All decision making within organizations should be driven by insights that are obtained from the analysis of data. The analytic process is how the insights are surfaced to the decision maker; however, one of the biggest shortcomings in the analytic process is that it rarely is a true process; rather, it is a set of silos that perform their specific tasks.
Usually after the organization identifies a problem that needs to be solved or has questions that need to be answered in order to make decisions, analysts have to go to IT to determine which data sources they need to access. The data experts prepare the data for analysis, and then the data is handed to a business intelligence (BI) team that may build reports and dashboards that show the analysis of the data. If it involves advanced or predictive analysis, data scientists may be part of the process and the insights are delivered to the decision makers. If this entire sequence of events takes too long to suit their needs, decision makers will likely work off a snapshot of data using analytics tools that they have access to, which, more often than not, are spreadsheets. Such scenarios happen often within organizations.
One way to address this issue is to introduce automation into the analytic workflow to simplify the delivery of insights to the decision maker where possible.
5 Steps to Digital Transformation*
- Have the right leaders in place
- Build capabilities for the workforce of the future
- Empower people to work in new ways
- Give day-to-day tools a digital upgrade
- Frequent communication
*Unlocking success in digital transformations, McKinsey
Automation That Transforms Data, Process, and People
As organizations continue to turn to data to transform their business, automation will be critical in enabling a new form of data interaction, usage, and education. Manual processes can be time consuming and introduce considerable risk of errors. Automating the steps associated with data capture and cleansing, coupled with creating reports in real-time, allows risk teams to measure and mitigate risk more accurately — and efficiently.
“As a global bank, our clients count on us for solutions and advice. To meet these demands, data is critical to our digital transformation initiatives. Integrated automation platforms enable us to not only accelerate our analytics and data science to provide the best quality service and automate many processes, they also upskill thousands of people across the bank and allow them to add even more value.”
— Nick Bignell, Director Data Science, UBS
Risk Officers Bring a Unique Vantage Point
In today’s competitive business environments that increasingly rely on the use of data to create competitive differentiation, the role of risk officers has become more important for transforming massive amounts of structured and unstructured data into business value.
However, a common challenge that many leaders face today is bringing their teams and data together to make data-driven decisions. You’ve probably faced the same struggle, and know the fix isn’t as simple as adding analytic tools to the tech stack or simply having insights. It requires converging data, processes, and people together. The organizations that do it best align everything together, encourage a company culture that backs each decision with good data, and frees up the time to do so. Through that process, they also establish a culture built around analytics. They speed up their time to insight by bringing everyone together under the same roof and empowering them to focus on outcomes.
Where are Organizations Finding Success in Using Analytics to Address Their Risk Management?
Successful risk analytics can be applied across the entire organization — analytics, finance, operations, compliance — and can be used in the following manner (and then some):
- Using analytics to address regulatory risk through repeatable workflows can result in streamlined reporting requirements.
- Assessing credit risk analysis to identify businesses likely to need PPP loans can increase SMB revenues.
- Using data from banks and law enforcement agencies can automate fraud monitoring and detect patterns to generate real-time alerts and reports to mitigate fraud.
Unified software platforms that provide data analytics, data science, and process automation capabilities across the whole digital transformation capability continuum are crucial for risk managers as they look to automate the risk function. Don’t leave yourself further exposed, focus on building risk analytics and transform your risk organization.