This is a piece I wrote for Chris Pratt’s quarterly financial institutions newsletter
The use of technology to automate and manage decisions, especially high volume decisions essential to day-to-day operational execution, is expanding rapidly. Beginning with the consumer credit business, use of decision engines and decision management has spread to all aspects of financial services and increasingly beyond. The primary drivers for this expansion are threefold–the increasing capability of decision engines, the increasing importance of analytics to businesses and the recognition of customer centricity as a core competence.
Decision engines have become increasingly capable over the last few years. Not only are vertically specialized decision engines more numerous and better established, general purpose platforms suitable for building decision engines are taking great strides. Business rules management systems have matured and improved. The ease with which large numbers of rules can be managed and governed, the extent to which rules can be safely exposed to non-technical users to edit, the simulation of the business impact of a rule change and the integration with business analytics for risk or opportunity modeling have all seen dramatic improvement across multiple vendors. Several purpose-built decisioning platforms have gained market traction, offering built-in support for champion/challenger testing as well as analytic integration. Both kinds of platform have been specialized into vertical specific decision engines and both offer tremendous value “filling the gaps” between commercially available decision engines for originations, fraud etc. Institutions can now realistically manage all the operational decisions that matter across their lines of business – the technology is ready.
As these platforms improve and are more broadly adopted it is clear that one way in which they offer tremendous value is in the effective deployment and application of business analytics. We are seeing increasing investments in high-end business analytics (data mining, risk scoring, predictive analytics and so on). More and more organizations are making analytics central to their overall growth and profitability plans. The power of analytics to simplify data while amplifying its value and to turn uncertainty about the future into usable probabilities make analytics compelling to every aspect of how a company runs its business. When analytics must be applied to the front line, the deployment of those analytics becomes a critical success factor and decision engines have proven themselves again and again in this regard. With over 50% of models not making it into production and recent surveys still showing most companies spending 6-12 months deploying models, the use of decision engines to deploy analytics is rapidly gaining acceptance as the best way to address these challenges.
Finally decision engines are playing a central role in the move to customer centricity. When a company wants to make decisions about marketing offers, about pricing or about retention based on the value of the customer they face numerous challenges. Once the data from multiple channels and product lines is pulled together and analyzed, the need to push the resulting customer-centric analytic models and decisions out to every channel becomes critical. Once again decision engines, especially some of the newer decisioning platforms focused on customer treatment, make it easier to deploy, monitor and manage these customer-centric analytics.
Decision engines have been around a long time, handling complex yet high volume decisions. Increasing capability, a focus on analytics and the need to become customer centric make decision engine technology a must-have for companies of all sizes. Adopting decision management, getting serious about improving and automating operational decisions, is no longer optional.