The risks of big data: Risk mitigation with advanced analytics
Big data means big risks in financial services. With ever-larger volumes of data requiring processing at ever-greater velocities, risk mitigation is becoming a clear and present concern for firms in the financial sector. Today, big data solutions and advanced analytics open new approaches for financial risk mitigation as well as significant competitive advantage.
Big data management presents a number of challenges and risks for firms in the financial sector, including:
- Unorganized, siloed data: For the most part, big data is stored in isolated silos, a fact that many firms only begin to understand when they try to use the information for financial risk mitigation. When analysts do get to the necessary data, they often spend a significant amount of time cleaning it and integrating it with other sources. The New York Times estimates that data scientists spend between 50 and 80 percent of their time working on mundane "janitorial" tasks. While these tasks are necessary, they take valuable time away from the processes that provide tangible business value.
- Information overload: Many business users spend their time looking at data in spreadsheets, where trends, threats and opportunities are hidden among the rows and columns. To be used effectively, data must be sorted into an easy-to-read format, where relevant patterns are automatically highlighted.
- Outdated analytics: Another common issue occurs when data lags too far behind to be useful. According to RingLead, 48 percent of organizations believe their data errors are related to outdated information. Data needs to be near real-time and coupled with analytical tools to be effective.
However, these obstacles are not insurmountable. Once firms understand and overcome challenges to get data collection and analysis right, they are rewarded with valuable insights into financial risk that are clear and digestible enough for even a busy CEO to grasp.
New approaches to financial risk mitigation
When financial institutions have data that is integrated, up-to-date and succinctly organized, analytics software to help them assess risks across the organization and the industry.
For instance, Depósito Central de Valores S.A. (DCV), a prominent Chilian financial firm, sought to use its data to pinpoint current and future risks across its business. The goal of this initiative was to improve customer satisfaction by proving that DCV was handing customers' money and transactions in a safe and efficient manner.
"Satisfying our customers with secure, risk-free financial services is our main objective," explains Claudio Herrera, assistant director of risk management at DCV. "But if our own internal risk management processes were manual, time-consuming and error-prone, how could we achieve this?"
After implementing advanced risk analytics from IBM, DCV was able to monitor inherent, residual and concrete risks within the company. Through predictive modeling, the company can prepare for future incidents that threaten business continuity. These new processes accelerated risk reporting at DCV by 99 percent and allowed the company to assess risk for all its applications.
Industry notes potential of analytics
Analytics gives financial companies the ability to see the future better than ever before, and this potential hasn't gone unnoticed. State Street recently announced that it is partnering with the University of California, Berkeley, to launch the Consortium for Data Analytics in Risk (CDAR). The end goal of this strategic partnership is to develop cutting-edge techniques for using data analytics to minimize economic and financial risk.
"CDAR has significant potential to forge new pathways in the fields of data science and risk management and improve insights that can be actionable for our clients," explained Jessica Donohue, executive vice president and chief innovation officer at State Street.
These trends show that the top financial institutions see data analytics as an efficient and effective way to mitigate risks and better serve their clients.
Learn more how advanced risk analytics can help your firm successfully mitigate the risk, and optimize the competitive advantages, of big data.