Real-time operational tools and case-based reasoning can help solve current and future problems, potentially helping prevent trading glitches and errors that could hurt the equity and other trading markets.
That’s the opinion of Tabb Group, a market consultancy, which wrote in its latest report, “Predictive Analytics: A Case for Capital Markets,” there’s a need for improved real-time operational analytics tools and presents a case for case-based reasoning as a viable solution to help traders.
Case-based reasoning (CBR) is a type of predictive analytics that uses machine learning to solve current problems with knowledge gained from past experience. A CBR-driven predictive analytics engine seeks patterns by automatically and continuously comparing real-time data streams of multiple heterogeneous data types. A self-learning case library directs the user to the most appropriate decision or action. To pre-emptively recommend the best solution, a self-learning case library adapts past solutions to help solve a current problem and recognizes patterns in data that are similar to past occurrences.
TABB Group principal and head of derivatives research Andy Nybo and contributing analyst Gabe Lowy wrote that CBR technology has multiple use cases in financial services. According to Nybo, the most significant opportunity may be for CBR to serve as an early warning system for market operators and participants to prevent disruptions caused by trading errors, improper systems oversight or other compliance violations.
“Capital markets institutions can deploy CBR to monitor and prevent abnormal client behavior, detect risk exposures through internal or external fraudulent activities (in areas such as trading or client interaction), improve IT operational efficiency in the back office and uncover customer-facing opportunities to generate new revenue,” Nybo wrote.
Unfortunately, despite heavy investment in data management and monitoring platforms, the financial services industry still lacks real-time operational intelligence to enable better business decision-making and prevent systems and service failures and catastrophic trading errors. These shortcomings expose firms to undue risk and compliance violations while potentially causing substantial losses and regulatory fines. They also undermine investor confidence and damage firm reputation.
“The need for improved visibility and insights gleaned from operational data is driven by the increased complexity of distributed computing and cloud-based architectures,” added Nybo. Greater market complexity and the unabated growth of new forms of unstructured data – or “big data” – exacerbate these challenges. The sheer volume of data is overwhelming legacy systems, resulting in overlooked information and missed opportunities to uncover hidden patterns, relationships and dependencies.
But as Nybo explained, predictive analytics enables firms to harness operational data to gain real-time intelligence and deliver business value quicker than ever. In turn, he says, “organizations can be more adaptable to unexpected internal or external challenges as a predictive analytics platform improves uptime, allowing companies to derive greater value from existing IT investment and be more preventive – rather than merely reactive – to potentially damaging failures.”
