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 outages expose institutions to undue risk and compliance violations that can cost organizations millions of dollars in financial losses and regulatory fines. They also undermine investor confidence and damage firm reputation.
Modern financial markets have become more complex that ever fueled by the globalization of capital markets, including a variety of new securities, derivatives and indexes, the evolution of high-frequency trading platforms with millisecond execution windows, more stringent regulations and higher levels of interconnection among different players. This increased complexity is overwhelming legacy systems, resulting in overlooked information and missed opportunities to uncover hidden patterns, relationships and dependencies. Markets can quickly and easily be destabilized by external or internal shocks that spread rapidly through massive electronic communication and transaction systems. The fact that participants often try to disguise their strategies only adds to market complexity.
The availability of intricate layers of market data has markedly lifted the quality of trading and risk management capabilities in recent years, enabling clearer identification of problems and faster resolution of market exposures considered far too unacceptable just a few years ago. Yet despite the wealth of data and content, most business users seek to gain even easier access to information they need in a timely fashion to stay ahead of their competition.
The sum-of-the-parts approach of traditional analytics methodologies that breaks systems into their component parts for individual analysis is ineffective. Analyzing or mitigating risk in only one component of the system sometimes feels like progress but does little to prevent truly disastrous events or failures. In fact, errors or failures can be amplified, as one component affects another and then another, spreading risk throughout the system or market. To better understand the properties of the components and their place in the overall system requires a higher-level assessment of the relationships to each other as well as to the wider system and environment.
Predictive analytics platforms enable organizations to leverage all enterprise data – from historical structured market data to newer forms of unstructured big data – to drive faster, more informed decision-making and provide preemptive warnings of systems failure. Users build sophisticated mathematical models to explore the relationships among these variables to uncover previously hidden patterns in the data, identify classifications, make associations and perform segmentation. While many of these techniques are not new, advances in underlying technologies – from multi-core and parallel processing to faster and larger data stores that can keep entire databases in-memory – are enabling real-time analysis on massive data sets of current and past activity to predict future scenarios.
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. To pre-emptively direct the user to the most appropriate decision or action, 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.
Using CBR, systems can learn from the past and become more adaptive. For example, if a system begins exhibiting a pattern of anomalous trading behavior, CBR searches for past cases of similar patterns and issues an alert for action before the pattern escalates into a full-blown hazardous event. A deeper understanding of past cases provides the context for markets and participants to prevent technical failures instead of just responding to them.
CBR technology has multiple use cases in financial services. 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 outages, trading errors, improper systems oversight or other compliance violations. Capital markets institutions can deploy CBR to monitor and deter 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.
