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The SMARTS way for micro-calculations


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.

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.

Wrap-up

  • 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.

About


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 hlaasri@sparklinglogic.com.

SMARTS in credit and risk management


SMARTS in credit and risk management

If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, or a product configurator, our SMARTS decision management platform can help you. Discover it here through a selected list of use cases we consider to be representative of decision management applications in modern credit and risk management, based on data, models, and automation.

If your project is different, just contact us or request a free trial. The Sparkling Logic team enjoys nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have you satisfied, but also proud of the system you will build.

Selected use cases

In credit and risk management, SMARTS has been used in applications where many data-driven decisions were frequently invoked and decision logic often updated, in response to changes in industry regulations, market dynamics, and business strategy. For this blog post, we select some applications that our customers have built with SMARTS.

Credit origination
An American rating agency has integrated SMARTS into its origination platform to help its corporate clients manage their credit risks, from screening to closing. With SMARTS, the agency manages credit risk for 4 of top 5 telcos and 30 of top 40 banks.

Credit risk management
A Chinese financial services provider uses SMARTS as the engine for its credit risk management from customer registration and identity identification to credit scoring and amount calculation to loan approval and money transfer. With SMARTS operational, the fintech company increased loan volumes to over 38 million lending transactions with greater control over its business risk.

Deposit risk management
A consortium of US banks specializing in deposit risk management measured SMARTS simulations of 1 billion transactions on 4 cores in less than 42 minutes, enabling the consortium to execute their decisions and compute complex business metrics beyond the traditional statistical means, variances, and deviations.

Flash fraud detection
A global online payment platform used BluePen for fraud detection. Since the deployment of the model, the detection time of a fraudulent transaction has been reduced from two weeks to less than a day, and the saving amounts to $10M’s per ongoing flash fraud.

Insurance claims adjudication
A major US-based third-party administrator for long term care insurance products uses SMARTS as the decision management engine for the company’s claims adjudication system, which processes 90,000 claim decisions per month over 1.3 million policies. Development and deployment took less than 6 months.

Healthcare insurance
A global risk platform company has used SMARTS to create, test, validate and put into production COVID-19 conditions for its drug prescriptions for more than 500,000 policyholders, located in more than 10 countries. Full development from specification to production took less than 12 months.

Life insurance underwriting
A Chinese life insurance company uses SMARTS so that all the underwriting rules and nearly 70% of the claims rules are managed by business experts, without calling on the IT department to update the rules. This allowed IT to focus on the reliability and availability of the system. Additionally, updating rules now takes no more than an hour from development to production.

Benefits

As reported by our customers, credit and risk analysts were able to leverage data and scoring models to intuitively build credit and risk management applications that can easily evolve with the business activity, internal policies, and industry regulations.

They also benefited from SMARTS agility and flexibility, giving them the ability to configure and refine decision logic, test, simulate decision services, experiment, choose decision strategies, and finally publish and manage deployment. Credit and risk analysts were able to participate in the entire solution lifecycle through web forms and point-and-click interfaces, without the sole reliance on IT.

On the other hand, IT had all the required performance, security, integration, and scalability capabilities to fit their enterprise architecture and governance without additional development or changes in the current applications. SMARTS was delivered in the form of a containerized product ready to install, deploy, and run as part of an interactive system, a service to invoke in a service-oriented environment, a program to call in a message-oriented environment, or a batch processing application.

To explore more, we invite you to visit our blog, webinar, resources, and demo pages where you can learn about SMARTS capabilities, features, and tools that make it an all-in-one low-code platform for building smart decisioning applications without a heavy involvement from IT beyond first installation.

Further reading

Sparkling Logic SMARTS in 10 Questions and Answers, a recent blog post that presents SMARTS all-in-one decision management platform through the 10 most asked questions and their responses.

Sparkling Logic: Decision Making Rendered Simple and Holistic, a “30,000-foot view” of SMARTS, Sparkling Logic, Inc’s low-code digital decision-making platform by CIOReview magazine.

About

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 a cloud-based, low-code, decision technology platform that 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 hlaasri@sparklinglogic.com.

Sparkling Logic SMARTSTM in 10 Questions and Answers


Sparkling Logic SMARTS in 10 Questions and Answers

Sparkling Logic helps businesses automate and improve the quality of their operational decisions with a technology platform that is powerful and simple: SMARTS for short. In this post, we present SMARTS through 10 selected questions and answers.

Q&A

1) What is SMARTS?

SMARTS is a decision management platform for business analysts and ‘citizen developers’ to author, test, simulate, deploy, run, and change decisions autonomously, without involving developers or IT beyond first installation.

2) Is SMARTS a business rules engine?

SMARTS is more than a business rules engine. It integrates multiple decision technologies into the same platform. SMARTS provides eight execution engines: A decision flow engine to sequence tasks of a business process; a state-machine engine to orchestrate tasks; a rule set engine to sequence decisions; a sequential engine that either fires all or just the first valid decision; a Rete-NT engine for inference; a lookup engine for data search in large datasets; a PMML engine to execute predictive models; and a DMN 1.3 engine to execute decision models. Depending on the problem you have, you may choose one or the other, or even combine them in the same set-up.

3) What are the typical applications for which SMARTS is the best fit?

In the financial, insurance, and healthcare services, SMARTS often won over the competition for origination and underwriting, pricing and rating engines, account management, fraud detection, and collections and recovery. More generally, SMARTS is a good fit when there are a lot of decisions that are data-based, frequently invoked, and likely to change often.

4) What is the difference between authoring business decisions and rules with SMARTS and coding them directly in the final application?

You can code decision logic but you will need detailed specifications from business analysts. This process may take too much time when compared to SMARTS. And once the decision logic is coded, it becomes complicated for business analysts to understand and take control of. SMARTS targets business-critical decision logic that either implements business models, corporate policies or industry directives in a dynamic and continually changing economy. Think of all the financial, insurance, and healthcare regulations since the financial crisis of 2008 and the changes since the coronavirus crisis of 2020. These two crises are typical examples of complex situations where business decisions not only need to be implemented quickly and accurately, but they also need to change dynamically and continuously.

5) Does SMARTS come with a decision design process?

SMARTS not only supports but it also augments the Decision Model and Notation (DMN) standard of the OMG (Object Management Group). DMN models decision dependencies very well, but not decision sequencing, which is also a natural way experts use to describe a complete decision logic. SMARTS addresses both dependency and sequencing through the combination of Pencil, RedPen, and the decision flow.

6) What machine learning models does SMARTS support?

SMARTS supports the execution of 13 machine learning models including classification, linear and logistic regression, support vector machines (SVMs), decision trees, random forests and ensemble learning, clustering, and neural networks. SMARTS uses PMML, the standardized predictive model markup language, to import and execute whatever model your data scientists have built.

7) Does SMARTS integrate with business process management platforms?

Yes, a SMARTS decision service can be natively invoked by a business process like any other service. Also, for decision-centric processes, SMARTS provides an orchestration capability.

8) What is the difference between an RPA tool and SMARTS?

If you think of a process as a sequence of “what to do”, “how to do it”, “do it”, and “report it”, then SMARTS automates the “what to do” and “how to do it” tasks while an RPA tool automates the “do it” and “report it” tasks.

9) Is SMARTS cloud-based?

SMARTS was designed from the ground-up for the cloud. Whether you have chosen to host your application or use our SaaS solution, we provide you with the most modern tools. SMARTS comes in a container, ready to install on your premises, AWS, GCP, Azure, or Aliyun. Choose yours, change your mind, no need to recode to redeploy your application.

10) What makes you unique?

Our motto is “your decisions, our business”. We enjoy nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have clients satisfied but also to be proud of the system they built. So dare to give us a challenge and we will solve it for you in days, not weeks, or months. Just email us or request a free trial.

In this post, we introduced SMARTS through 10 selected questions and answers. If you have more, feel free to read our blog, sign up for our webinars, or contact us. We would be happy to get back to you very quickly.

About

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’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.

Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, decision technology platform that 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 hlaasri@sparklinglogic.com.

Decision Requirements and Modeling with DMN in SMARTS


DMN 1.3 support in SMARTS

In this post, we present how Sparkling Logic continues its involvement in the DMN standard, through its graphical tool SMARTS Pencil, which business analysts use to model business decisions by drawing a diagram to form a decision process.

DMN, a bit of history

The Decision Model and Notation (DMN) was formally introduced by the Object Management Group (OMG) as a v1.0 specification in September 2015. Its goal was to provide a common notation understandable by all the members of a team whose goal is to model their organization’s decisions.

The notation is based on a simple set of shapes which are organized in a graph. This allows the decomposition of a top-level decision into more, simpler ones, whose results must be available before the top-level decision can be made. These additional decisions themselves would be decomposed, and so on and so forth until the model reaches a more complete state. In addition, the implementation of the decisions can be provided, notably in the form of decision tables (which is also a very common means of representing rules).

The normalization of the graphical formalism (the DMN graph) and of the way the business logic is implemented (e.g., decision tables) allows teams to talk about their decisions, using diagrams with a limited set of shapes.

Sparkling Logic was one of the early vendors to provide a tool to edit (and execute) these decision models: Pencil Decision Modeler. It was released in January 2015, before the standard was officially approved.

Since then, the DMN standard evolved significantly, by adding new diagram elements, new constructs and new language features, while clarifying some of the existing notions. It is now at version 1.3. And we didn’t rest on our laurels either: in SMARTS Ushuaia, we made Pencil Decision Modeler part of SMARTS, as a first-class feature and added full compliance to DMN 1.3! This post describes how SMARTS supports DMN 1.3.

Basics

DMN 1.3 still defines the building blocks which were in the original standard and which I mentioned in Talking about decisions.

As a recap:

  • A Decision determines its output based on one or more inputs; these inputs may be provided by an input data element, or by another decision
  • An input data is information used as input by one or more decisions, or by one or more knowledge sources
  • A business knowledge model represents knowledge which is encapsulated, and which may be used by one or more decisions, or another business knowledge model. This knowledge may be anything which DMN does not understand (such as a machine learning algorithm, a neural network, etc.) or a DMN construct (called a “boxed expression”, see below)
  • A knowledge source represents the authority for a decision, a business knowledge model, or another knowledge source: this is where the knowledge can be obtained (be it from a written transcription or from someone)

These blocks are organized in a graph and the links between them are called requirements.

What’s new in SMARTS’ DMN Support

More building blocks

In DMN 1.3, the following elements may also be added to a graph:

  • A decision service exposes one or more decisions from a decision model as a reusable element (a service) which might be consumed internally or externally
  • A group is used to group several DMN elements visually (with whatever semantics may be associated with the grouping)
  • A text annotation is a shape which contains a label and can be attached to any DMN element

Custom types and variables

Input data, decision and business knowledge model elements all have an associated variable, which is of a given type (string, number etc., or custom). A variable is a handle to access the value directly passed by an input data element, or calculated by the implementation of a decision or a business knowledge model, from within the decision implementation.

Custom types may be defined to group multiple properties under a single type name (with structure) or to allow variables which will hold multiple values (arrays).

Boxed Expressions

A few constructs are available to provide an implementation for a decision or a business knowledge models; they are termed boxed expressions since such expressions are shown in boxes which have a normalized representation. The following types of boxed expressions are available in DMN 1.3:

  • Literal expression: this is a simple expression which can use the available variables to calculate a result
  • Context: this is a set of entries, each combining a variable and a boxed expression. Each entry in the context can use the variables of the entries defined before it, which is like using “local variables” in some languages
  • Decision table: this is a tabular representation where rows (called rules) provide the value of outputs (supplied in action columns), depending on the value of inputs (supplied in condition columns)
  • Function: a function can be called using an invocation, by passing arguments to its parameters. The result of a function is the result of the execution of its body (which is an expression that can use the values of the passed parameters). A Business knowledge model can only be implemented by a function
  • Invocation: this is used to call a function by name, by passing values to the function’s parameters
  • List: this is a collection of values calculated from each of the boxed expressions in the list
  • Relation: this is a vertical list of horizontal contexts, each with the same entries

In addition to these, SMARTS defines an additional boxed expression, called the rule set. This is a set of named rules, where each rule is composed of a condition (an expression evaluating inputs) and action (an expression providing some values to outputs).

Helping Industry Adoption

With SMARTS Ushuaia, decision models are first-class citizens. The full compliance with DMN 1.3 means that all the DMN elements and boxed expressions, as well as the ability to interchange diagrams with other tools, are part of the package.

As is usual, any model can be tested and executed in the same context as your SMARTS decision –a decision is never made in isolation, and a model is never used in isolation either. And of course, you will benefit from the great tooling we provide.

Finally, we at Sparkling Logic strongly believe that decision management technologies should be put in the hands of all business analysts. This is why we are part of the DMN On-Ramp Group, whose mission is to provide a checklist to help customers find the DMN tool to suit your needs, educate and raise awareness about DMN, and help with DMN compliance. For a great presentation of the group, check out here.

About

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

Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that 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.

Marc Lerman is VP of User Experience at Sparkling Logic. You can reach him at mlerman@sparklinglogic.com.

If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, or a product configurator, Sparkling Logic can help. Our SMARTS digital decisioning platform automate decisions by reducing manual processing, accelerating processing time, increasing consistency, and liberating expert resources to focus on new initiatives. SMARTS also improve decisions by reducing risk and increasing profitability.

Software industry trends behind the digital transformation revolution


This article presents the three software industry trends driving the digital transformation revolution: DevOps, low-code / no-code automation, vertical integration with digital decisioning.

Introduction

The pandemic changed tech priorities for many people both at work and home making a ‘hybrid’ work a top initiative. Where and how we do the work accelerated the need to improve customer digital experiences and efficiency across work, shopping, and everyday chores.

The data supports this new trend. The independent research firm Omdia compiled over 300 responses from executives at large companies indicated that working away from traditional offices will become the new norm. 58% percent of respondents said they will adopt a hybrid home/work. Even more interesting is that 68% of enterprises believe employee productivity has improved since the move to remote work.

Similarly, adoption of everyday on-line activities such as shopping, banking and entertainment further accelerated the pace of digital transformation. The need for improved applications increased the pressure on companies to relaunch efficient, friendly front-end customer apps with more intuitive UX. The back end now needs to support faster turnaround with the need to automate processes for the new on-line community of users demanding faster, cleaner, and more intelligent offerings.

To respond to this digital transformation, companies are rapidly adopting easy-to-use integrated enterprise software tools to optimize and accelerate development of these efficient digital products.

Several trends like DevOps, Low-code/automation and vertical integrations with integrated digital decisioning have emerged to help enterprises take the digital transformation journey faster and cheaper.

DevOps

DevOps is a software development concept bringing together historically disconnected functions in the lifecycle of the software development. Traditionally, business analysts would define the problem, developers would interpret the concept and build applications, and operations teams would test, report bugs and provide feedback. The disconnect between the functions, silo’d approach created inefficiencies, increased costs and slowed down application releases.

The emergence of integrated tools and processes which integrate this multiple aspect of software development and promote collaboration between these different functions supported growth of the DevOps industry.

In fact, the market data shows that these trends are supported by the investment community and exit activity. According to Venture Beat, in 2Q 2021, Venture funding for global DevOps startups reached $4 billion and the exit activity deal value was dominated by the IPOs of UiPath (robotic process automation) and Confluent, (data / application integration platform).

Low code / no code automation

Application development is also coming closer to non-developers with low/no-code approach and automation.

Software engineering, traditionally owned by IT and software engineers, has always been coveted by other, non-IT stakeholders in the enterprise. In 1991, Powerbuilder introduced a revolutionary concept of a development framework, aiming at democratizing development by allowing non-software professionals to get access to application development. Perhaps ahead of its time with clunky UX, WYSIWYG, Powerbuilder started the revolution of introducing emergence of ‘citizen-developers’, people who originally participated alongside IT in shaping the application and business models but could not code and create the applications themselves. It also introduced data integration with application logic and object-oriented concepts like inheritance and polymorphism and encapsulation, bringing software engineering to the masses.

Fast forward to 2020’s, virtually every enterprise tool platform and enterprise customer have adopted a low-code/no-code approach. The mission is the same as 30 years ago – to provide easy to use, graphical UI/UX, drag and drop concept to application development and allow business analysts, ‘citizen-developers’ and non-software engineers to create, test and even deploy enterprise applications.

Vertical integration with digital decisioning

The perennial challenge of allowing non-developers to create applications is the conundrum of how deep they can develop without coding and to what extent they can customize complex enterprise cloud applications without IT and coding.

To accelerate digital transformation, enterprise software vendors are emerging mostly from the workflow / BPA world, such as Pega and ServiceNow. They are applying a two prong approach – core tool collection and vertical integration. The workflow vendors have developed (or acquired) a collection of point tools in a core-component framework. Those components typically include AI/ML, reporting, workflow, RPA (Robotic Process Automation), case management, rules engine, decision management, knowledge bases, BPA (business process automation) and process orchestration. Those components typically feature common UI and work across a normalized data model and unified architecture.

But that is not enough. To satisfy modern rapid digital transformation needs, in case of fintech enterprise customers (i.e. banks, insurance companies and financial services) also now require pre-built workflow, data and application models. These vertical templates are higher level and more specific, providing out-of-the box, drag/drop solutions like credit card operations, loan management and payment operations. Using the low-code approach, a business analyst can graphically drag/drop pre-defined steps into a loan origination workflow with pre-defined commonly used tasks, created using best practices defined by the ‘centers of excellence’. Companies like UIPath have created a 3rd party marketplace for additional steps and templates created by analysts and consultants. (Those steps could be ‘get customer data’, ‘OCR input form’, ‘scrub customer data’, authorize user’, ‘assess risk profile’ etc.).

Beyond the top level tasks, the functionality ultimately becomes more complex and the sophisticated customer needs powerful decision capabilities to introduce their own business rules and implement proprietary features. The ‘secret-sauce’, which separtes most common steps from proprietary concepts distinguishes top corporations from the competition, requires more sophisticated digital decisioning tools. These digital decisioning tools enable non-developers to customize and manage decision logic, implement AI/ML features, run A/B testing and visualize performance results on training and production data in real time.

To satisfy most common customer base, digital workflow vendors typically provide rudimentary business rules integrated in their low-code platforms and further integrate them with the downstream workflow platforms and vertical ecosystem vendors (i.e. FiServ, Jack Henry, SAP, Salesforce and FIS in banking for example).

The most sophisticated and demanding customers, however, need a more sophisticated set of digital decisioning tools like standalone professional DM platforms. To simplify and visualize this complex decision management, a new generation of low-code digital decision management platforms like Sparkling Logic emerged. These platforms integrate historical business rules engine, data and AI, demystifying machine-learning and providing low-code approach to development and monitoring of application logic performance, continuously as the business logic and training data change and drift.

The pandemic, hybrid work and pervasiveness of the cloud computing have irreversibly changed the software application development. Enterprise customers are seeking and deploying better, faster, more integrated software tools. DevOps integration, low-code, vertical templates, integrated AI and digital decisioning are becoming a new normal while defining the next generation of applications, created not only by software engineers, but by mere mortals across the enterprise.

About

Davorin Kuchan is the CEO of Sparkling Logic, Inc, an AI-driven digital decision management enterprise tools platform. Major enterprise customers like Equifax, Centene, First American, Nike, SwissRE and Enova deploy and integrate Sparkling Logic SMARTS digital decision engine. Sparkling Logic, Inc is based in Sunnyvale, California. http://www.sparklinglogic.com

Noise reduction in digital decisioning with Sparkling Logic SMARTS


noise-digital-decisioning-explicit-decisions-dashboards-analyticsIn this post, we present how to deal with the problem of noise, which is both a source of errors and biases in digital decision-making in organizations, through explicit decision rules, dashboards, and analytics. To illustrate our point, we use the example of the Sparkling Logic SMARTS decision management platform.

Noise in organizations’ decisioning and what to do about it

In an interview with McKinsey, Olivier Sibony, one of the renowned experts in decisioning, recommends algorithms, rules, or artificial intelligence to solve the problem of noise, a generator of errors and biases in decisioning in organizations. This recommendation resonates with our vision of automating decisioning — not all of the decisioning but the operational decisions that organizations make by thousands and sometimes millions per day. Think credit origination, claim processing, fraud detection, emergency routing, and so on.

In our vision, one of the best ways to reduce noise, and therefore errors and biases, is to make decisions explicit (like the rules of laws) so that those who define the decisions can test them out, one at a time or in groups, and visualize. The consequences of these choices on the organization before putting them into production. In particular, decisions should be kept separate from the rest of the system calling those decisions — the CRM, the loan origination system, the credit risk management platform, etc.

Noise reduction with explicit decision rules, dashboards, and analytics

Our SMARTS decisioning platform helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation, reducing biases that could be a failure to comply with industry regulations, a deviation from organizational policies, or a source of an applicant disqualification. The consequences of biases could be high in terms of image or fees, and even tremendous for certain sensitive industries such as financial, insurance, and healthcare services.

In SMARTS, business users (credit analysts, underwriters, call center professionals, fraud specialists, product marketers, etc.) express decisions in the form of business rules, decision trees, decision tables, decision flows, lookup models, and other intuitive representations that make decisioning self-explainable so that they can test decisions individually as well as collectively. So, at any time, they can check potential noise, errors, and biases before they translate into harmful consequences for the organization.

In addition to making development of decisioning explicit, SMARTS also comes with built-in dashboards to assess alternative decision strategies and measure the quality of performance at all stages of the lifecycle of decisions. By design, SMARTS focuses the decision automation effort on tangible objectives, measured by Key Performance Indicators (KPIs). Users define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. Moreover, they can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.

During the testing phase, the users have access to SMARTS’ built-in map-reduce-based simulation capability to measure these metrics against large samples of data and transactions. 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.

And once the decisioning application is deployed, the users have access to SMARTS’ real-time decision analytics, a kind of cockpit for them to monitor the application, make the necessary changes, without stopping the decisioning application. SMARTS platform automatically displays KPI metrics over time or in a time window. The platform also generates notifications and alerts when some of the thresholds users have defined are crossed or certain patterns are detected. Notifications and alerts can be pushed by email, SMS, or generate a ticket in the organization’s incident management system.

Rather than being a blackbox, SMARTS makes decisioning explicit so that the users who developed it can easily explain it to those who will operate it. Moreover, the latter can adjust the decision making so that biases can be quickly detected and corrected, without putting the organization at risk for violating legal constraints, eligibility criteria, or consumer rights.
So, if you are planning to build a noise-free, error-free, and bias-free decisioning application, SMARTS can help. The Sparkling Logic team enjoys nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have them satisfied, but also proud of the system they build. We helped companies to build flaw-proof, data-tested, and scalable applications for loan origination, claims processing, credit risk assessment, or even fraud detection and response. So dare to give us a challenge, and we will solve it for you in days, not weeks, or months. Just email us or request a free trial.

About

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

Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that 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 hlaasri@sparklinglogic.com.


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