Winning | Three ways real-time analytics drives business value in financial services

Businesses around the world have arrived at a new frontier of data management and analytics. We’re entering the era of super-fast data, where big ideas are fuelled by insights gleaned from time series data analysed in real-time. For those able to take full advantage, the potential business impact is significant.

In the Financial Services industry, particularly but not exclusively in capital markets, we’re witnessing an arms race where established players and new entrants are all vying to develop and launch new products and services faster than the rest to retain competitive advantage. Within that, time series data and real-time analytics play powerful roles in driving greater commercial and operational performance, on one hand through greater levels of more granular insights for revenue generation and on the other efficient compute and memory utilization for cost reduction. In business jargon, that's a win/win.

Working with CEBR, we identified three key takeaways to understand where the value of real-time data analytics is most strongly felt in Financial Services to understand the financial impact that can be realized from investing in real-time data analytics.

Enhanced end user experiences:

We showed that implementing real-time data analytics has a sizeable, positive impact on the experiences of customers and the end users of financial services organizations. Every single organization polled in the UK recorded at an increase in positive customer feedback after embracing real-time data analytics. Recorded benefits include faster service delivery, increased sales, better product quality and reduced prices. Specifically for the UK financial services sector, ‘faster services’ was the most widely reported positive increase overall (61% of organizations) And 50% of organisations saw reduced prices. Clearly, harnessing real-time data can help banks and financial institutions gain a competitive edge through improved operational efficiencies and the positive impacts they have on customer experience.

Winning!

Faster anomaly identification and remediation: AI in Action

Real-time data platforms help combine real-time analytics, time-series databases, (complex) event processing, statistics, machine learning models - training and trained -, model parameter estimation and calibration, and data visualization tools into single points of access to all data regardless of system, format, and location.This avoids the time-consuming and costly need for users to manually piece together data from disparate platforms to deliver analytics and provide solutions. Time, memory and compute costs get saved with reducing data capture and reporting times and overheads, while increasing analytical performance concurrently benefits lines of business. Cost savings are common to all, while benefits can vary, for example generating alpha among trade-savvy participants or deriving beta for investment strategies, or delivering rapid risk insights to those providing risk-based decisioning across the lending, credit and insurance sectors.

This is AI in action - automation, big streaming data and machine learning!

Let's explore some tangible use cases. Anomalous data – data points, events or observations outside of a dataset’s normal behaviour – can indicate something has gone wrong somewhere in the business, ranging from common mispricing through to fraudulent trade practices such as spoofing and occasional outright fraud such as money laundering. Whatever the case, the ability to detect and respond to anomalous incidents quickly is critical, particularly because gaining the ability to react with with context and confidence in real time can limit the costs anomalies incur.

This is something financial organizations know too well. According to the Cebr report, in the UK, almost six in ten financial institutions suffer at least a moderate financial impact from anomalies. The implementation of real-time data analytics, however, delivers positive results. In the UK, 65% of financial institutions saw a moderate reduction of anomalies, while 23% saw a significant reduction. Additionally, 92% of financial services firms saw at least some reduction in costs – whether financial or time-based. This demonstrates that the implementation of real-time data not only reduces operational problems, but also associated costs.

Lower costs, risks mitigated and opportunities realized through better anomaly detection. Winning x 3! 

Reduced operating costs

Whether an organisation has already invested in real-time capabilities or is looking to build a business case for implementing new systems, it’s clear to see that a broad range of general business benefits can be achieved. But most compelling of all is the financial case for real-time.

By implementing a real-time data system, firms can reduce their data management costs - work with data where it resides rather than have the expense of exporting it to a distant vendor-controlled data warehouse where queries and analytics get performed sub-optimally incurring yet more expense.

In addition, where data scientists and quants can directly work with live data streams, their productivity significantly improves, through finding actionable insights more immediately, proactively alongside line of business subject matter experts, reactively from rapid alerts from "always on" automated analysis, or delivering production analytics for mission-critical projects much faster.

The research shows that for the financial services sector globally, significant savings can be achieved from processing and managing data, optimizing data architectures, and reducing operational costs (non people costs). There was also a clear positive impact on revenues. In total, and across the six markets surveyed, the total increase in revenue for the financial services sector attributable to real-time analytics was a cool $857 million.

Winning - both underlying infrastructure costs AND people time.

Inspire Change. Don't be the one who says "We've Always Done It This Way!"

The value of implementing real-time data is significant and is only set to increase as more businesses understand how real-time data can be used effectively and reduce their dependence on expensive, legacy relational databases. The "we've always done it this way" culture will be beaten. Make that change today! From process improvements to cost reductions and tangible impact on business revenues, the benefits of real-time time series analytics are wide-reaching. The task now is for firms to get the right capabilities in place and ensure they’re not only implementing real-time but utilizing it to its full and rich potential.  

Winning!