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The SMARTS way for micro-calculations

Written by: Hassan LâasriPublished on: Dec 8, 2021No comments

The SMARTS way for micro-calculations

Until recently, companies used rules to comply with a sectoral regulation, implement a business strategy, or automate a business process. At Sparkling Logic, we were among the few pioneers who helped customers to use business rules for micro-calculations.


By micro-calculations (analogy to Michael Ross and James Taylor’s “micro-decisions”), we mean all those simple but plentiful granular calculations that businesses often codify into large spreadsheets and use to score, rate, or price items. Think about it when you click on your smartphone on a digital bank’s app to apply for a loan, or when you go to an auto insurer’s website to find a price for your new car. To you, things seem simple, but behind the scenes there is complexity…

Indeed, as for any complex system, it is the interaction between its elements, however simple, that gives rise to its complexity. The price of insurance is not a simple numerical formula but a combination of rules and calculations, all based on data. Data is either provided by you or is internal to the insurance company. A slight change in a rule or a calculation may seem inconsequential at the micro-level but can result in incredibly significant side-effects at the macro-level. Imagine the consequences of a miscalculation in the pricing engine of the insurance company. A single miscalculation could translate into huge losses for the insurance company if the error were not visible in the rule that performs the first micro-calculation but propagates to other rules which use the result of this micro-calculation.

The SMARTS way

Based on a 20+ year experience in implementing or guiding customers to implement sophisticated data-based micro-calculation systems, the founders of Sparkling Logic have developed a way to manage the complexity of such systems. To reduce or even eliminate the potential errors that calculations can generate when modernizing or implementing a new system, they produced the following steps:

1) Start with existing data, representing past transactions. If they worked, they should continue working or at least guide the new system. It comes with a built-in engine that automatically turns data spreadsheets into a database which an application can query to perform its micro-calculations.

Take again the example of the auto insurance company. The spreadsheet may contain thousands of past transactions which, depending on the age of the primary driver, the mileage of the car, and other criteria, provides the price for this configuration. But often this price is the same for other configurations, and parts of the configuration at hand appear in other configurations, making direct use of the spreadsheet a complicated exercise. With SMARTS, the user or the application only queries the engine with a configuration and in record time it gives back the corresponding price.

2) Experiment with different representations to visualize the chain of micro-calculations that leads to the final score, rating, or price. SMARTS offers different graphical representations to express a calculation flow: tables, trees and graphs, and rules. Moreover, users can choose and switch between different representations without leaving the graphical user interface. And they can do so until they select the most appropriate representation based on the task at hand as well as the steps that they are familiar with when designing or reviewing the calculation flow.

3) Integrate testing when authoring rules. Decision logic is not software code. As a result, you cannot be satisfied with testing tools and techniques that software developers use. Testing decisions requires a different approach, different techniques, and different tools. Granted, ensuring that your decision service can compile is useful. But the end goal is really to ensure that your decision logic complies with your business objectives. To this end, SMARTS comes with an integrated dashboard where the user can define metrics against which to evaluate the rules —rule by rule, a set of rules, or the entire system.

4) Run A / B simulations. There is no such thing as a timeless business strategy. Economic conditions change suddenly, as does scoring, rating, or pricing. SMARTS allows users to run A / B tests (called Champion / Challenge simulations in credit and risk management) at any time, to evaluate the performance of different strategies, until they find the one that best responds to economic changes. Think of the eligibility criteria, the increase or decrease in prices, and all the parameters that go into the rules and calculations. For these, SMARTS supports big data simulations to experiment with different scenarios of a given strategy, using real data streams.

5) Monitor in real-time. Despite all the care you would put into designing or reviewing your spreadsheets, a typo or error might still not appear until a long time after you put your system into production. To help you manage these cases, SMARTS comes with a real-time decision analytics capability that displays measurements and triggers notifications and alerts when certain KPIs cross thresholds, or the application detects certain patterns. SMARTS pushes notifications by email or generates a ticket in a corporate management system.


  • Decision management and business rules are also suitable for micro-calculations, the type of computations that businesses often codify into large spreadsheets and use to score, rate, or price items.
  • A minor change in a rule or a calculation may seem inconsequential at the micro-level but can result in significant side-effects at the macro-level. A single miscalculation could translate into huge losses.
  • Sparkling Logic has developed a way to manage the complexity of such systems: Start with existing data, experiment with different representations, integrate testing when authoring rules, run A / B simulations, and monitor in real-time.

If you are planning to upgrade or build a system with micro-calculations, SMARTS can help. The Sparkling Logic team has been involved in projects with scoring / rating / pricing data in the form of large spreadsheets.

Most often, these spreadsheets contained thousands and sometimes tens of thousands of rows. And customers were not just concerned with performance, but also maintainability. The pace of change was high and required additional monitoring and careful management to roll these rates over disparate geographies and time periods. So, just contact us or request a free trial.

Learn more

Implementing Rating Engines with Business Rules and Lookup Models, an online seminar where you will learn how SMARTS manages not only simple rating engines, but also complex pricing engines with a combinatorial explosion of specific cases and continuous evolution over time.

Best-in-class Series: Testing your Decisions, an online seminar to explore what makes decisions different, how to evaluate them, and how to automate regression tests in SMARTS.


Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful decision management platform, accessible to business analysts and ‘citizen developers.’ Sparkling Logic SMARTS customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.

Sparkling Logic SMARTSTM (SMARTS for short) is an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.

Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at

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