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SMARTS Real-Time Decision Analytics

Written by: Carlos SerranoPublished on: Jun 28, 2021No comments

Real-time decision analytics
In this post, I briefly introduce SMARTS Real-Time Decision Analytics capability to manage the quality and performance of operational decisions from development, to testing, to production.

Decision performance

H. James Harrington, one of the pioneers of decision performance measurement, once said, “Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.” This statement is also true for decision performance.

Measuring decision performance is essential in any industry where a small improvement in a single decision can make a big difference, especially in risk-driven industries such as banking, insurance, and healthcare. Improving decisions in these sectors means continuously adjusting policies, rules, prices, etc. to keep them consistent with business strategy and compliant with regulations.

Decision performance management in SMARTS

SMARTS helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation —- thereby reducing errors and bias. To this end, we added a real-time decision analytics capability to the core decision management platform.

Currently used in financial and insurance services, it helps both business analysts and business users to define dashboards, assess alternative decision strategies, and measure the quality of performance at all stages of the lifecycle of decision management — all with the same interface without switching from one tool to another.

Development. From the start, SMARTS focuses the decision automation effort on tangible business objectives, measured by Key Performance Indicators (KPIs). Analysts and users can define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture their decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. They can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.

Testing and validation. During the testing phase, analysts and users have access to SMARTS’ built-in map-reduce-based simulation environment to measure these metrics against large samples of data. Doing so, they can estimate the KPIs for impact analysis before the actual deployment. And all of this testing work does not require IT to code these metrics, because they are transparently translated by SMARTS.

Execution. By defining a time window for these metrics, business analysts can deploy them seamlessly against production traffic. Real-time decision analytics charts display the measurements and trigger notifications and alerts when certain thresholds are crossed or certain patterns are detected. Notifications can be pushed by email, or generate a ticket in a corporate management system. Also, real-time monitoring allows organizations to react quickly when conditions suddenly change. For example, under-performing strategies can be eliminated and replaced when running a Champion/Challenger experiment.

Uses cases

Insurance underwriting. Using insurance underwriting as an example, a risk analyst can look at the applicants that were approved by the rules in production and compare them to the applicants that would be approved using the rules under development. Analyzing the differences between the two sets of results drive the discovery of which rules are missing or need to be adjusted to produce better results or mitigate certain risks.

For example, he or she might discover that 25% of the differences in approval status are due to differences in risk level. This insight leads the risk analyst to focus on adding and/or modifying your risk related rules. Repeating this analyze-improve cycle reduces the time to consider and test different rules until he or she gets the best tradeoff between results and risks.

Fraud detection. An other example from a real customer case is flash fraud where decisions had to be changed and new ones rolled out in real time. In this case, the real-time decision analysis capability of SMARTS was essential so that the customer could spot deviation trends from normal situation directly in the dashboard and overcome the flood in the same user interface, all in real time.

Without this built-in capability, the time lag between the identification of fraud and the implementation of corrective actions would have been long, resulting in significant financial losses. In fact, with SMARTS Real-Time Decision Analytics, the fraud management for this client has gone from 15 days to 1 day.

Marketing campaign. The two above examples are taken from financial services but SMARTS real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules, such as in loan origination, product pricing, or marketing promotion.

In the latter case, SMARTS can help optimize promotion in real-time. Let’s say you construct a series of rules for a marketing couponing using SMARTS Champion/Challenger capability. Based on rules you determine, certain customers will get a discount. Some get 15% off (the current offering — the champion), while others get 20% (a test offering — the challenger). And you wonder if the extra 5% discount leads to more coupons used and more sales generated. With SMARTS real-time decision analytics environment, you find out the answer as the day progresses. By testing alternatives, you converge to the best coupon strategy with real data and on the fly.


As part of the decision lifecycle, business analysts obviously start by authoring their decision logic. As they progress, testing rapidly comes to the forefront. To this end, SMARTS integrates predictive data analytics with real-time decision analytics, enabling business analysts and business users to define dashboards and seamlessly associate metrics with the execution environment — using the same tool, the same interface, and just point and click.


  • SMARTS comes with built-in decision analytics — no additional or third-party tool is required
  • You can define metrics on decision results so you can measure and understand how each decision contributes to your organization’s business objectives
  • Decision metrics enable you to assess alternative decision strategies to see which should be kept and which rejected
  • SMARTS add-on for real-time decision analytics lets you monitor the decisions being made and make adjustments on the fly
  • SMARTS’ real-time decision analytics helps in any context where decision performance could be immediately affected by a change in data, models, or business rules


Sparkling Logic is a decision management company founded in the San Francisco Bay Area to accelerate how companies leverage data, machine learning, and business rules to automate and improve the quality of enterprise-level decisions.

Sparkling Logic SMARTS is an end-to-end, low-code no-code decision management platform that spans the entire business decision lifecycle, from data import to decision modeling to application production.

Carlos Serrano is Co-Founder, Chief Technology Officer at Sparkling Logic. You can reach him at

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SMARTS Decision ManagerSparkling Logic SMARTS is a decision management platform that empowers business analysts to define decisions using business rules and predictive models and deploy those decisions into an operational environment. SMARTS includes dashboard reporting that allows organizations to measure the quality of decisions both during development and post deployment. Learn more about how SMARTS can help your organization improve decisions.
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