So far, we’ve covered how to use SMARTS for decision management, micro-calculation, and data transformation. In this blog post, we show how you can use it to implement regulatory technology (regtech).
RegtechRegulations, from Basel rules on bank capital requirement to Sarbanes-Oxley Act on corporate financial statements, to MiFID on pre- and post-trade transparency requirements across EU financial markets, have forced regulated companies to develop processes to find, assess and mitigate risks. To comply, investment firms, retail bankers, and insurance companies have turned to regtech for help.
Regtech is an acronym for governance, risk, and compliance management technologies in companies, more particularly those working in highly regulated industries such as financial services, insurance, and healthcare. They allow the implementation of legal requirements in the nervous system of companies, in front-, middle- and/or back-office.
All regulations being prescriptive, it was natural that rule-based systems were among the first technologies used, with varying degrees of success which we will detail here, before showing how SMARTS overcame them.
SMARTS as regulatory compliance technology
Your data is uploaded and transformed in the same tool
Highly regulated companies must deal with continuous growth in transaction volumes and a massive accumulation of data that they must ingest or produce daily while constantly complying with ongoing regulations. They could do it, but they had to use other tools besides rules-based systems and they either had to connect them or transfer the data back and forth. With SMARTS, they don’t have to, since it allows data to be uploaded and transformed into rules or calculations in the same tool.
Your data is turned into insights and your insights into decisions
Before the widespread use of big data and analytics, investment bankers relied on complex analysis of information using statistical learning. Today, the entire financial services and insurance industry has integrated advanced data analytics into its artillery to detect market signals and predict market trends. The most advanced want not only to transform data into insights, but these insights into decisions. SMARTS was one of the only tools if not the only tool to offer an integrated solution to transform such companies into lifelong learning organizations where data helps find opportunities and risks, machine learning turns that data into knowledge and rules transforms this knowledge into decisions, thereby closing the virtuous circle that data promises.
You limit your risk for noise, errors, and biases
Since the advent of data, regulators have been closely monitoring the bias issues of automated decision-making systems, particularly those that rely solely on data and use machine learning to calculate scores and then decide instead of a human. In SMARTS, users implement decisions in the form of business rules, decision trees, decision tables, decision flows, and lookup models. All these intuitive representations make decisioning self-explainable so that they can test decisions individually as well as collectively. So, at any time, they can check potential noise and errors before they translate into biases.
Your data and transactions are tracked in real-time
A key need of the financial services industry is real-time responsiveness to suspicious events such as unusual transactions that may show fraud, money laundering, insider trading, or may not be unusual in themselves but nevertheless exceptional in relation to other transactions before or after. Based on their experience in the earlier generation of decision management systems, the founders of Sparkling Logic decided to integrate real-time decision analysis from the ideation of SMARTS. The product has always integrated a dashboard that tracks data and transactions so that the user can react by changing rules in real time.
You react very quickly before an error, an anomaly, or a fraud spreads
Companies must make thousands of complex risky decisions – monetary, reputational, and legal risks. For example, in every decision they make, there are tiered combinations of terms and conditions, legal constraints, eligibility criteria, and levels of risk involved. Rule-based systems allowed them to implement these decisions in the form of tables or decision trees, or rules, but at the expense of side effects on the business. With SMARTS, they graphically define KPIs and drag and drop them into a dashboard to visually check the impact of each decision or group of decisions on business performance. Users can also set thresholds and define patterns which if reached will trigger notifications and alerts. This way, the users will be able to react very quickly before an error, an anomaly, or a fraud spreads and results in enormous damage.
Your system is easy to maintain and upgrade
Prior to the emergence of regtech as a hot technology, highly regulated companies used rules engines to encode the directive logic of laws, regulations, and internal policies. Additionally, they could implement complicated decisions with tens of thousands or more if-then rules. All went well until they discovered that, like any hard-coded software, rule-based systems could be complex to maintain. With SMARTS, they don’t code and hope the code is correct. They create, test, deploy, run, monitor, and change graphically through web forms and point-and-click. Therefore, systems developed with SMARTS are easy to maintain and upgrade.
Wrap-upThe SMARTS is not strictly speaking regtech in the sense that it does not come with all the code of financial and insurance regulations, but it allows them to be implemented quickly and explicitly in the form of rules, trees or graphs. This makes the code easier to change if regulations change as they often do, such as Basel which is in its third version and MiFID in its second version.
SMARTS not only eases the implementation of governance, risk and compliance rules, but it also facilitates their monitoring in real-time. SMARTS not only eases the implementation of governance, risk and compliance rules, but it also facilitates their monitoring. KPIs, dashboards and metrics were fundamental from the start of the product and not an afterthought once the product was released.
AboutSparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.
Our motto is “your decisions, our business.” Using SMARTS, organizations have automated complex decisions in days, not weeks, or months. Our mission is to enable customers to implement the most demanding decisioning requirements and to easily maintain and improve them over time.
Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Unlike other tools that focus solely on the authoring and maintenance of business rules, SMARTS is decision-centric and focuses on measuring and improving business outcomes in the context in which our clients work, especially with complex regulations. Major enterprise customers like Equifax, First American, SwissRE, Centene, and NICE Actimize integrate SMARTS in their core systems.
As an enterprise IT solution, SMARTS has different customers in the same organization who directly use it or indirectly benefit from it. This blog post aims to succinctly describe who our customers are, what they want, and what value we bring to each of them that matches their unique needs.
In our terminology, these are the customers in the organizations who design, author, deploy, and update decisions according to the company’s policies and industry’s directives.
When they looked for a decision management solution, they looked for product simplicity and rich functionality. More importantly, they wanted autonomy once the solution was in place.
After a few weeks after training on SMARTS, our business analyst customers reported that they very much liked to have data, models, and business rules in the same tool. They enjoyed how we succeeded in managing SMARTS’ evolution to have both richness and easiness in the same product. They also enjoyed being able to quickly author, test, deploy, run, monitor, and change decisions. Their experience with SMARTS was a joy as they could focus on the decisioning process and its outcomes instead of the technology to implement it.
Business users are the people who run, monitor, and manage the performance of the business. In our case, they are the internal customers of business analysts. They are the ones who use the solution daily.
They wanted to know how easy it will be for them to monitor decisions built by business analysts and make the necessary changes when the actual performance may deviate from the expected performance.
After using SMARTS, business users reported the following benefits: Quick change-test-deploy-run cycles, being able to work without coding and with no prior knowledge of machine learning or business rules, just with their knowledge of the business and using web forms and point-and-click.
By IT, we designate IT the people who install and connect the solution to the rest of the organization’s IT system. They asked for integration, performance, security, and fit with the IT global architecture and governance.
They want to have business analysts and business users to be autonomous but at the same time being able to monitor the solution as the rest of the IT infrastructure.
IT people liked all the performance, security, integration, and scalability we promised. They also appreciated SMARTS adherence to the enterprise IT architecture and governance as expected. They liked how easily they could deploy SMARTS on premises or in the cloud. Finally, they also very much liked to have no additional development or changes in the current applications.
These are the people who develop and manage models using data science libraries through languages such as Python, R, SAS, and SPSS.
They are not direct users, but they were willing to see their models fully operational into the new solution while they continue their effort on enhancing existing models and experimenting with new ones.
Thanks to SMARTS, they were able to know the performance of their models in production with real data and transactions. SMARTS was an effective demonstrator of their models.
In our case, these are the people who head organizations or verticals where decisions are at the core of their operations, throughout all the organizations activity. Their attention is “more revenue, less cost, and why not both!”
They wanted to hear about similar successful implementations in their market, in particular the time it would take to recoup their investment in the new solution, and the strategic advantages it will provide them after one year or two in production.
To management people, we brought strategic benefits. They could operate the business under a decisioning process that implements the business strategy. Their organization could finally make informed, error-free, and unbiased decisions. And they were insured that the decisions taken were in full compliance to internal policies and industry regulations.
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 an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines business rules with predictive models to create intelligent decisioning systems.
If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, a product configurator, a condition-based maintenance application, or such applications, SMARTS can help. Just contact us or request a free trial.
In 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 itIn 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 implement the decisions can test them out, one at a time or in groups, and visualize their outcomes in dashboards. Designers must be able to analyze the consequences of decisions on the organization before putting them into production.
Noise reduction with explicit decision rules, dashboards, and analyticsOur 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.
AboutSparkling 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.
In this blog post we describe SMARTS’ key capabilities: Decision Management Platform, Low-Code / No-Code Apps, Machine Learning Ops, and Real-time Decision Analytics.
SMARTS is a decision management platform that spans the entire business decision lifecycle, from modeling to deployment. Decision Management Platform, as the core functionality of SMARTS, includes authoring, testing, simulating and deploying decisions as well as the management and governance of those decisions. Key authoring capabilities include RedPenTM, which allows business analysts to write rules via point and click without learning a special syntax, and Fluid Metaphors, which allows business analysts to choose the most appropriate decision representation based on the task at hand. The same decisions can be viewed and modified in the form of decision rules, tables, trees, or graphs.
SMARTS is a data-centric product in that business analysts can import data and immediately start writing and testing rules against that data, making the building of rating, pricing, and any calculation engine an easy task. SMARTS’ powerful processing of spreasheets allows business analysts to retrieve rates from a very set of options; fine-tune the rates to cope with exceptions and overrides; and manage these rates over different time horizons, per geography, any user-defined dimensions.
SMARTS lifecycle management capabilities include release management (read-only snapshots of projects) to track promotions and deployments. Business analysts can view the history of their decision logic and revert to earlier versions or releases ant anytime. Based on their permissions, they can publish their changes into a test environment or into production.
SMARTS decisions are typically deployed as decision services either on the cloud on on-premise. SMARTS decision services provide support for secure service invocations in an authenticated context, support for high-availability and scalability.
All of SMARTS other capabilities, Low-Code/No-Code, Model Ops, and Real-time Analytics are integrated into to the Decision Management Platform.
Business analysts can develop decisions quickly and intuitively with SMARTS’ Low-Code capability. SMARTS Low-Code features include graphical representations of decision logic such as decision flows, tables, trees and graphs. Rules are written using the SparkL (pronounced “sparkle”) user-friendly language or with the point-and-click Red Pen feature.
Thanks to this low-code capability, business analysts can develop Business Apps which provide a no-code environment for untrained business users and ‘citizen developers’ to modify, test and deploy changes.
Many decisions deal with uncertainty- such as credit card approvals and loan and mortgage approvals. These decision benefit from predictive models that help organizations make the best possible decision in the face of uncertainties. SMARTS operationalizes predictive models in three ways.
- Business analysts can import machine learning models developed by a data science team using PMML (the standard model interchange format). Once imported these models can be integrated into any decision and deployed. The models can be tested in the context of the decision to ensure they produce expected results and then can be deployed.
- SMARTS’ patented BluePen machine learning technology can be used by business analysts to generate rules from data. Models created by BluePenTM are explicit and easy to understand.
- In cases where models exist but are not available as PMML, but are available as spreadsheets, SMARTS can import them as a dashboard, regression, or tree model.
SMARTS Real-Time Decision Analytics lets business analysts define metrics that measure the outcome of SMARTS decisions to ensure they are supporting business strategy and objectives. The SMARTS dashboard displays graphical representations of the metrics allowing the business analysts to analyze the impact of changes to decision logic and run simulations against large volumes of historical data. SMARTS decisions can also be monitored post-deployment and alerts can be defined which notify stakeholders when thresholds are exceed.
Using SMARTS Real-Time Decision Analytics capabilities, Business analysts can measure the end-to-end performance of the decisions that invoke these models – not only the quality of the model but the quality of the whole process.
We hope describing SMARTS in-terms of its powerful capabilities has given you a better understanding of the platform. Insurance companies, banks, and healthcare companies rely on decisions powered by SMARTS every day for loan origination, claims processing, credit risk assessment, as well as fraud detection and response.
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 SMARTS 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.
Colleen McClintock, is VP Products at Sparkling Logic. You can reach her at firstname.lastname@example.org.
Today, predictive analytics is common in any data-driven business. Typically, data scientists create predictive models first, and IT staff deploy these models in a production environment. At Sparkling Logic, not only have we streamlined this process, but we’ve extended it with prescriptive decisions. Sparkling Logic SMARTS AI & ModelOps is the third built-in capability of SMARTS to cover the full spectrum of operational predictive models, from importing models and creating new ones to initiating learning tasks. But let’s start with a brief overview of the stages data has gone through.
Data: a resource, an asset, a business
Until recently, data was a resource to conduct business and as such, it was typically managed by the CIO’s organization. The organization’s mission was to build the overall data architecture, to choose a database vendor, and to design all the applications necessary to process the data from the databases to the business and functional people screens. These applications were mostly reporting, letting the business get a sense for how the business has been doing based on the collected data.
Then came the first transformation, where data went from an asset used to understand how the business has been doing to being an asset leveraged to predict how the business could potentially do in the future. Reporting was enhanced by predictive analytics. The scope of the analytics was not only what had happened, but also what was happening and what could happen. In general, these two past-focused and future-focused activities cover most of what data science is in business, with some really important use cases on marketing, sales, and customer relationship management.
However, a new transformation is underway, first in the banking, insurance, and health sectors, but will certainly penetrate other sectors. It consists of transforming analytics into automated decisions, translating predictions into prescriptions. The goal of this transformation is to create a virtuous cycle where not only data is analyzed, but this analysis is transformed into decisions and actions that generate new data, and so on. Reporting and predictive analytics are now completed by prescriptive analytics.
Anticipating this trend, the founders of Sparkling Logic designed the SMARTS decision management platform to implement this cycle of data, insights, and decisions. Sparkling Logic SMARTS comes with a built-in AI & ModelOps environment that covers the full spectrum of operationalizing predictive models, from importing models built by data scientists, to creating new ones without prior knowledge of machine learning, to launching and managing learning jobs.
Sparkling Logic SMARTS AI & ModelOps
Predictive model importBusiness analysts can import AI, machine learning, and deep learning models developed by data scientists, and leverage them in the decision logic. The models could be developed in Python, SPSS, SAS, or Project R among others. SMARTS integrates them as long as they are compliant to PMML, a standard for sharing and deploying predictive models, or are accessible as services.
SMARTS supports importing as PMML neural networks, multinomial, general, and linear/log regression, trees, support vector machines, naïve bayes, clustering, ruleset, scorecard, K-Nearest Neighbors (KNN), random forest, and other machine learning models.
In cases where models exist but are only available as specification, business analysts can easily import these models and seamlessly transform them into business rules for full transparency and easy inspection.
There may be situations where it is necessary to call an external service that is available elsewhere. This external service can be a predictive model or a data source. SMARTS provides support for remote functions, which makes it possible to invoke an external service through JSON-RPC or REST services.
BluePen predictive technologyWhen time is of the essence, when models are short-lived or when expertise needs to be confronted with knowledge captured in the data, business experts can use the BluePen learning technology to quickly create a model, potentially leveraging existing models.
BluePen lets business analysts and business experts explore and analyze data using domain knowledge and expertise to identify predictors, or, alternatively, selects the predictors for them. Then, using the selected predictors, BluePen helps them to generate a model in the form of readable decision rules, tables, or trees, and integrate them into their decision logic.
Using BluePen, users can build meaningful predictive models in hours or days, rather than the months it often takes. Users can also engineer or modify the models. As a result, without heavy investment in data analytics efforts, these models can be tested, leveraged in simulations, and quickly deployed in the context of an operational business decision.
Regardless of the business analyst’s choice, he or she can operationalize a wide range of models within SMARTS. Being able to integrate models into decision logic is a central ability to test and measure the performance of the end-to-end decisioning.
Moreover, SMARTS allows the analyst to translate the insights from many different models into a decision. Typically, data-centric organizations will have many different models which each can contribute insights into what the decision should be. The orchestration of how these insights are combined is expressed in decision logic, turning multiple discrete predictions into actual prescriptive decisions.
The benefits of combining machine learning and automated decisioning as SMARTS does are nothing less than transforming businesses into always learning organizations where data helps identify opportunities, machine learning turns that data into insights and automated decisioning turns this information into action, closing the virtuous cycle that data promises.
- Data has moved from being a resource to assess how the business has been doing, to being an asset used to predict the future of the business, and finally to an asset used to improve automated decisions
- Sparkling Logic SMARTS comes with a built-in AI & ModelOps environment that covers the full spectrum of operationalizing predictive models, from importing models, to creating new ones, to launching and managing learning jobs
- With its AI & ModelOps capability, SMARTS helps in transforming businesses into learning organizations, closing the virtuous cycle that data promises. Data feeds analytics leading to improved decisions that generates additional data in addition to profits
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.
Carole-Ann is Co-Founder, Chief Product Officer at Sparkling Logic. You can reach her at email@example.com.
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 performanceH. 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 SMARTSSMARTS 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 casesInsurance 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.
ConclusionAs 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
AboutSparkling 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 firstname.lastname@example.org.
Low-code no-code is not a new concept to Sparkling Logic. From the beginning, the founders wanted to deliver a powerful yet simple product, so that a business analyst could start with data and build decision logic with built-in predictive data analytics and execution decision analytics.
Version after version, they have achieved this vision through SMARTS, an end-to-end decision management platform that features low-code no-code for business analysts and business users to develop and manage decision logic through point-and-click operations.
For business analysts, SMARTS offers a low-code development environment in which users can express decision logic through a point-and-click user interface to connect data, experiment with decisions, monitor execution without switching between different tools to get the job done. Depending on the nature of the decision logic at hand and user preferences, business analysts can choose on the fly the most appropriate representation to capture or update their decision logic. The resulting decision logic is seamlessly deployed as a decision service without IT intervention.
To push the simplification even further, Sparkling Logic founders turned to their customers for inspiration on their needs and developed three complementary technologies:
- RedPen, a patented point-and-click technology that accelerates rule authoring without a need to know rule syntax or involve IT to author the rules
- BluePen, another patented point-and-click technology to quickly create or leverage a data model and put it into production without involving data scientists or IT
- A dynamic questionnaire to produce intelligent front-ends that reduces the number of unnecessary or redundant questions
In addition to low-code development capability for business analysts, SMARTS also elevates the decision logic to a simple web form-based interface for untrained business users. They can configure their decision strategies, test the updated decision logic, and promote the vetted changes to the next staging environment — without learning rules syntax.
These business apps offer a business abstraction for most tasks available in SMARTS related to configuration, testing and simulation, invocation and deployment management, as well as administration.
For example, credit risk specialists can configure loans, credit cards, and other banking products, and pricing specialists can control pricing tables, through a custom business app specific to their industry. The no-code business app enables business users to cope with environment changes whether they are related to internal policies, competition pressure, or industry regulation.
Furthermore, SMARTS tasks can also be automated through orchestration scripts. Business users can trigger these scripts through the click of a button, or schedule them to be performed automatically and seamlessly.
Sparkling Logic is a decision management company founded in the Bay Area to accelerate how companies leverage internal and external data and models 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.