Financial institutions are demanding real-time analytics at their point of customer interactions. For years, we have been advising our clients to connect their front-end customer facing systems with real-time pricing analytical capabilities, or at least lay the foundations to enable this capability in the near future.

According to Gartner Research (1) “Between 2016 and 2019, spending on real-time analytics will grow three times faster than spending on non-real-time analytics.” “Injecting” real time scoring and optimization, as an example, at the right points in the customer interaction process can deliver great value. Yet to me what is most interesting, is that the demand for real-time pricing engines is coming from supplementary stakeholders in the market.

These stakeholders include vendors of front-end systems who are increasingly approaching us to enable the integration of their systems with their client’s back-end pricing structures. These are providers of insurance rating engines and underwriting solutions (including providers of core systems), as well as revenue management and onboarding systems in banking.

It seems that the driver for this vendor interest, is explicit demand from the banks and insurance companies themselves. These institutions are increasingly investing in off-line pricing analytics such as advanced statistical models and advanced optimized decision-making.

Why is this happening now?
The rush to utilize real-time analytics in customer facing processes and decisions is not unique to the pricing function nor to the financial services industries. It has been growing for several years as part of the broader big data and advanced analytics trends.

Banks and insurers are now raising real-time pricing analytics as a requirement from suppliers of pricing systems, and have been defining such capabilities (or connectivity to such systems) as must have “add-ons” in RFPs for core and front-end systems (CRMs, Underwriting, Onboarding, Rating/Pricing Engines etc.). Of course, the level of demand for such pre-integration differs between countries and sub-industries, and it is highly influenced by regulatory requirements, however in most segments we have noticed the pull in this direction.

Moving from off-line analytics to real-time analytics
Today it is even easier for financial organizations to get the budget to move to real time analytics. Many LoBs have already completed their major multi-year investments in “defensive” areas such as regulation, post-crisis restructuring and other such burning must-dos, and are now moving to the next batch of important projects/investments in growth and profitability.

In the same vein, replacement of core systems is accelerating as more resources are available to buy and implement these systems. This is enabling companies to re-evaluate all related processes (including pricing). Coupled with the surge in analytical know-how and advances in analytics related technologies including real-time capabilities and faster optimization, real time analytics is becoming more widely feasible.

But the underlying benefits of real-time analytics is what is really driving the demand. Financial institutions realize that connecting their offline analytics to the customer facing process brings uplift not only in numbers but in the customer experience itself. According to Gartner reserach (2) “Analytics and BI modernization programs are increasingly using real-time analytics to facilitate faster, more accurate decisions, especially for complex digital business initiatives”. Below, are some of the benefits we have seen customers enjoying after migrating to real-time analytics:

1. The ability to react quickly to aggressive competition, especially given the rise of direct channels and players.
2. Improve the efficiency of price execution processes as well as reduce time-to-market of new pricing strategies.
3. Improve customer facing decisions. Once a company has a system in place to analyze real-time data their ability to understand the customer significantly increases, translating into improvement in KPIs such as lift as well as being able to anticipate and meet customer expectations.

Is real-time analytics on your roadmap for 2018 or beyond?
Regardless of what the reasons might be, we have been receiving more and stronger indications that real time analytics is catching on in the insurance and banking markets in which we operate. “Regular” offline advanced analytics are already mainstream investments in financial organizations, and the focus seems to be progressing very practically to the next logical extension of real-time application of these analytics. Implementing real time analytics that is connected to customer facing execution systems (in pricing and beyond) requires forethought and planning. Even if this is something you are considering doing three years from now, the planning and the laying of the “infrastructure” should start today.

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Notes:
1: Gartner Research: How to Move Analytics to Real Time, Published: 27 September 2016
2: Gartner Research: Four Steps to Successful Real-Time Analytics, Published: 20 December 2016