Financial Services
What our customers say about SMARTS
You are about to modernize or develop a new automatic decision-making application, based on data, knowledge, or a combination of both. You wonder whether our SMARTS platform meets your business needs and technical specifications. Nothing is better proof of a product’s superiority than the testimonials of customers. So, we have selected some customers here so you can see why they chose SMARTS as their decision management solution.
Equifax, consumer credit reporting
Equifax is a global data, analytics, and technology company that serves financial institutions, corporations, government agencies, and individuals with enriched data and executable insights. These insights are typically derived from many data sources including financial, telecommunications and utility payments, employment, and income data.Equifax chose SMARTS as a core part of the Equifax InterConnect platform. Credit and risk analysts, both at Equifax and their customers, can seamlessly import data, capture decision logic, A/B test and analyze how the decisions apply to each transaction, and measure the collective impact of making changes to decisions. Business users and business analysts, both at Equifax and their customers, can autonomously make changes to policy rules.
Testimonial
“SMARTS is at the heart of our InterConnect platform. It has helped us quickly enter an era where the credit process must be smooth, automated and optimized for lenders and consumers. SMARTS’ all-in-one approach to authoring, testing and deploying business rules into a sophisticated yet simple product appealed to us from the start. Since then, we have been very satisfied with the use we make of it on a daily basis” — Deepesh Mohandas, Vice President, Global Product Management, Decisioning Platforms.
Learn more about SMARTS at Equifax.
NICE Actimize, suspicious financial activity monitoring
NICE Actimize is the largest and broadest provider of financial crime, risk, and compliance solutions for regional and global financial institutions, as well as government regulators. Consistently ranked as number one in the space, NICE Actimize experts apply innovative technology to protect institutions and safeguard consumers’ and investors’ assets by identifying financial crime, preventing fraud, and providing regulatory compliance.Integrated with NICE Actimize’s platform, SMARTS supports financial institution customers by making more rapid decisions on financial crime strategies and providing the ability to view the overall impact across all NICE Actimize analytics. This capability allows X-Sight customers to make more informed decisions while maximizing financial crime coverage and controlling costs.
Testimonial
“This technology partnership delivers our X-Sight customers more rapid development and deployment of financial crime management strategies across the broader NICE Actimize analytics ecosystem” — Craig Costigan, CEO.
Learn more about SMARTS at NICE Actimize.
Percayso Inform, insurance intelligence
Percayso Inform is an insurance intelligence provider whose services go beyond traditional data enrichment, providing unique, real-time solutions at all stages of the insurance lifecycle and delivers unrivaled insight into insurance customers, risk, and fraud.Powered by SMARTS, Percayso Inform Manager allows insurance providers of all shapes and sizes from start-ups to global insurance businesses across personal and commercial lines to build, adapt and optimize their own data enrichment, rating, and intelligence strategies dynamically and intuitively. It enables high volume data ingestion, rules configuration, operational and strategic decision management as well as a string of valuable features such as reporting, dashboards, champion challenger, data manipulation and more.
Testimonial
“Sparkling Logic stood out from the crowd. The functionality, configurability and ease of use of SMARTS ticked all our boxes but what really impressed us was the genuine desire by their team to create a true partnership and build a foundation from which we could both grow together” — Richard Tomlinson, Managing Director.
Learn more about SMARTS at Percayso Inform.
Enova Decisions, data analytics and decision management
Enova Decisions is an analytics and decision management technology company that was formed in 2016 to enable businesses to automate and optimize operational decisions through data, AI, and the cloud- in real-time and at scale.Testimonial
“Leveraging technologies like Sparkling Logic in our cloud service allows our clients to oversee the fine details of the decision algorithm without feeling overburdened by the complexity of what they’re designing” — Sean Naismith, Head of Analytics Services at the time.
Learn more about SMARTS at Enova Decisions.
LTCG, insurance business processing
LTCG is a leading provider of business process outsourcing for the insurance industry. The largest insurers rely on our unparalleled expertise to help manage their complex long-term care portfolios and maximize financial performance.LTCG evaluated tool and platform options and selected SMARTS as the decision engine for their claims adjudication system. They developed and implemented the new system and have since extended their use of SMARTS to support additional business processes.
Testimonial
“Thanks to SMARTS, we were able to discover, test, and deploy automated claims decision logic in under six months” — Kyle Korzenowski, CIO at the time.
Learn more about SMARTS at LTCG.
ABT, power management
ABT Power Management, now part of Concentric, furnishes, engineers, installs, and services industrial batteries and charging systems and is a recognized industry leader in material handling power management.Testimonial
“Now, using SMARTS, we have achieved near real time analysis of this [sensor] data and are able to respond immediately to conditions that need attention. SMARTS’ interface is easy and intuitive enough to allow our engineering staff to create and maintain the rules themselves” — Mike Shemancik, CIO at the time.
Learn more about SMARTS at ABT.
First Rate, wealth management
First Rate is the UK’s largest supplier of foreign currency and a top 5 currency wholesaler globally. They are one of the foremost FX experts in the industry, with a multi-billion-pound wholesale business and over 10 years’ trusted experience providing tailor-made travel money solutions for companies in the finance, travel, and retail sectors.Testimonial
“We chose SMARTS for its comprehensive decision management environment with out-of-the-box integration of business rules, and predictive analytics, and focus on decision improvement” — Nick Collins, Head of Business Solutions at the time.
Learn more about SMARTS at First Rate.
Onlife Health, patient-centric care management
Onlife Health, a GuideWell company, brings simplicity to population health and wellness, connecting and integrating people, technology, and benefit design through a user-friendly engagement platform, guiding members on the “next right thing to do” in their healthcare journey.Testimonial
“We are able to easily change our decisions as business needs dictate and deploy these changes without going through the full software change process.” — David Jarmoluk, Vice President of Enterprise Solutions at the time.
Learn more about SMARTS at Onlife Health.
Do you want to learn more or test SMARTS yourself? Just contact us or request a free trial.
About
Sparkling 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.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.
SMARTS as regulatory compliance technology
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).
Regtech
Regulations, 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-up
The 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.
If you envision modernizing the implementation of a regulation, be it Basel, Sarbanes-Oxley, MiFID, GDPR, or any other regulation, SMARTS can help. Just contact us or request a free trial.
About
Sparkling 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.
Decision Management Systems: What Are They and When to Use Them?
The purpose of this blog post is to succinctly introduce decision management systems for those who are new to the field. First, I present what decision management is, then the technologies that make it up. I also present where they are used and what they bring to the companies that use them.
What is decision management?
There are two types of decision-making technologies. The first are descriptive in that they implement how people make choices among alternatives based on their beliefs and preferences. Think of a doctor deciding a treatment following a diagnosis or a trader buying an asset following a predictive model. The second are normative in that they implement regulations, policies, or strategies regardless of the beliefs or preferences of those who follow the decisions. Think of a loan officer deciding based on an applicant’s repayment history or an insurer calculating the premium an applicant should pay based on the applicant’s medical condition.There is no single definition that differentiates the two technologies. You have heard of knowledge-based systems, expert systems or reinforcement learning for the former technologies and decision tables, decision trees or business rules for the latter. But there is a consensus to name the second type of technologies decision management systems. So, when you hear or read someone referring to decision management, think of prescriptive methods, technologies, products, and systems implementing formal laws, industry regulations, company policies, and business strategies.
What are the underlying technologies?
Often decision management systems are confused with business rule engines, but they are more than that. Behind the terminology of decision management systems hide multiple technologies. The simplest are decision tables, trees, and graphs. The most sophisticated combine rules and predictive models. If we take the example of SMARTS, it integrates eight decision engines into the same platform. Depending on the problem at hand, one may choose one or the other, or even combine them in the same set-up.Also, the users of modern decision management systems are not IT people anymore but businesspeople instead. So, they come with features that allow non-specialists to use them without IT intervention. With modern decision management systems, IT only takes care of the first installations and configurations, the systems come with everything necessary to ensure the governance and security of the applications developed as well as the ease of integrating them into the corporate IT architecture. If we take the example of SMARTS again, it comes with an easy-to-use graphical authoring interface, pre-deployment rule testing, rule repository with version control and rollback, large-scale simulation, real-time decision performance monitoring, and much more.
Where decision management systems are most used?
Like any technology, decision management systems are not a one-size-fits-all solution for every decision problem. They are not suitable for long-term or midterm slow decisions that companies make once a year or a quarter. In these cases, optimization technologies are more used. They are not also suitable for cases with uncertainty and where probabilistic technologies such as probabilistic graphical models are more used.Decision management systems are best suited when there is a substantial number of decisions and calculations that are often nested, often invoked, and likely to change often. Therefore, one must consider decision management systems for the operational and day-to-day decisions that companies make in the thousands and sometimes millions in a single day. We find these cases in banking for credit risk assessment, in insurance for premium calculation and even in retail for product configuration.
Although not dedicated to finance, insurance and healthcare, customers of SMARTS have widely used it for loan origination, risk management, fraud detection and money laundering prevention. These are typically cases where organizations make decisions and calculations thousands and sometimes million times a day and may change based on the market dynamics or global economy, or updates to regulations or business strategy.
What are the key benefits of using decision management systems?
Decision management systems come with two critical benefits. As decisions are explicit, they make it possible to understand and explain the decisions implemented so that one can change them more easily when a new situation requires it. They also reduce the risk of errors and biases met in customer-facing applications, such as recommendation, credit, or insurance systems based solely on machine learning.Key takeaways
- There are two sets of decision-making technologies. The first is descriptive. The second is prescriptive. Decision management systems are descriptive in that they implement formal laws, industry regulations, company policies, or business strategies.
- Decision management systems are best suited when there are thousands or millions of decisions and calculations that are often nested, often invoked, and likely to change often.
- The power of modern decision management systems lies less in the engines but more in the features surrounding them to enable businesspeople take full control of the decisioning process without heavy IT intervention beyond first installation and default configuration.
- Decision management systems have two key benefits. They ease the integration of changes in regulations, policies, and strategies. They also reduce errors and biases in customer-facing applications.
Where to look for further information?
We write regularly about decision management systems and SMARTS. You can find quite a bit of information on our blog and webinars. You can also download our white paper, request a demo of SMARTS, or try it.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 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.
Hassan Lâasri is a data consultant and interim executive, now leading marketing for Sparkling Logic. You can reach him at hlaasri@sparklinglogic.com.
SMARTS for data marketplaces
In my earlier blog post, I explained how decision management and business rules were suitable for micro-calculations, the type of computations that businesses often codify into large spreadsheets and use to score, rate, or price items. In this post, I explain how they are also suitable to simplify data integration, aggregation, and enrichment when building and running data marketplaces.
Data marketplaces
Data marketplaces are a shift from data warehouses where the goal is not only to store large volumes of data, but to make that data be consumed as a service without resorting to IT or prior knowledge of a query language. Data marketplaces are often organized into three layers:- At the lowest layer, we find raw data stored in the form in which it was ingested from the data sources. Data sources can be global ERP and CRM systems, or even local MySQL databases and shared Excel spreadsheets.
- At the middle layer, we find integrated data from multiple sources that is reconciled to resolve disparities and inconsistencies found in the original data. Often, the source systems do not have the same format for dates, names, phone numbers, and addresses. Sometimes the same object can have different attributes in different data sources.
- At the topmost layer, we find aggregate data expressed in summarized forms, often to inform about groups rather than individuals. It is at this level that we also find data enriched by external data to make them directly consumable by the businesspeople.
To learn more about data marketplaces, I recommend the Eckerson Group white paper: The Rise of the Data Marketplace – Data as a Service by Dave Wells.
Easy to define, hard to construct
Defining a data marketplace as we have just done is simple, its construction is complicated for two reasons:1) Data heterogeneity. It is not enough to bring together all the company’s data in a data marketplace for the data to be transformed into knowledge, forecasts, and decisions. Indeed, all data does not have the same age, the same structure, the same format, the same quantity, the same quality, and above all the same utility. If an attribute is important for a business line, it is not automatically important for another business line, yet within the same company.
Each business line has its vision of the product, of the customer, and of any entity managed by the various actors of the company. In the luxury sector for example, a dress, a bag, or a piece of jewelry, although it is a unique object, is seen through different attributes according to the databases where this same object is stored. Looking to exploit all the data available in a company to extract predictions and then decisions not only require integration but also transformation, unification, harmonization, and enrichment.
2) Data reorganization. A data marketplace would work better if data, information, and needs were always stable. But in a dynamic and rapidly changing business world, groups are reorganizing, and companies are acquired, merged or separated. For example, to simplify finance reporting, a country can change the region it was in a year ago, and it’s a safe bet that it will change yet again if a new boss is appointed, or a region is split or added. To be successful, data marketplaces must be implemented as change-tolerant projects.
These two reasons are representative of situations where decision management technologies are used: piecing together things that move independently to each other. Under the name of decision management, we group all the technologies that help organizations to automate all those simple but plentiful granular decisions and calculations that businesses often codify into decision tables, decision trees, or business rules.
Decisioning technologies to the rescue
Decisioning technologies can be used here. But one can use a database programming language or a general-purpose scripting language to automate the loading of data from the data sources into the raw data layer if the original format is kept. There is no real value using a business rules engine to do this straightforward job. The value of using a decisioning technology starts at the integration data layer, where attributes of objects are grouped together to form an updated version of an existing attribute or a new attribute.Take the example of customer data from two different databases. Suppose that customers have their home address and business address in a first source database, and that they only have their home address in a second source database. Suppose also that the format of the addresses is not the same in the two databases. What should the integration data layer hold? An address? So, which one? And what format? Two addresses? So, what professional address to put for these customers who are only present in the second database with the home address? These questions can be easily answered through decision rules.
Now, suppose at the aggregate data layer, we want to add an average turnover with a client that buys from two business units. Here again, calculation rules can be easily used. One can use SMARTS’ look-model engine to automate such calculations.
SMARTS
SMARTS is our all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of micro-decisions and micro-calculations described in this article. SMARTS comes in the form of one product, four capabilities:- A decision management platform that spans the entire life cycle of decisioning from modeling to deployment.
- A low-code no-code environment in which users express decisions and calculations through point-and-clicks and web forms.
- An AI & ModelOps environment that covers the full spectrum of ModelOps from importing existing models, to defining new ones, to executing learning tasks.
- A real-time decision analytics environment to manage the quality of decision and calculation performance.
SMARTS has been extensively used for decisions and calculations in finance, insurance, healthcare, retail, IoT, and utility sectors. To learn more about the product or our references, just contact us or request a free trial.
Wrap-up
- Data marketplaces promise to change the way data is consumed by businesses. Contrary to data warehouses, they are more complex to build and therefore deliver on their promises.
- Decision management technologies simplify data integration, aggregation, and enrichment when building and running data marketplaces. They make decisions and calculations explicit and therefore easy to change whenever situations change.
- SMARTS supplies multiple graphical representations* and engines to make such transformations, integrations, and enrichment easy to design, implement, test, deploy, and change according to situation changes.
* The following table, tree, and graph show three different representations of the same decision logic so that developers can use one that they are familiar with or that best fits the task at hand.
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
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.
Noise reduction in digital decisioning with Sparkling Logic SMARTS
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 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 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 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.
Authoring Business Rules with Data, Standards, and Apps in SMARTS
Nowadays, business rules automate hundreds, thousands, and sometimes millions of operational decisions that some organizations make every day. The most representative examples of such organizations are financial, insurance, and healthcare sectors. All these organizations make automated decisions with several combinations of terms and conditions, legal constraints, eligibility criteria, risk levels, and price ranges. In this blog, I explain how business analysts and ‘citizen developers’ author decisions with rules, data, standards, and apps in Sparkling Logic SMARTSTM.
Business rules
Business rules are not new; but until recently they were encoded in the rule syntax as “IF THIS THEN DO THAT” statements. As such, they needed detailed specifications from business analysts and skilled developers to code these business rules. And once the business rules were coded, they were complicated for business analysts to understand or control.Authoring with data
Gone are the days when business rule creation started with lengthly interviews where IT professionals asked business experts how they made decisions in line with company policies, industry regulations, and market dynamics. Starting with data, transactions, and use cases is now the new way. Fully in line with this new approach, SMARTS provides RedPenTM, SparkL, and Pencil. These are three independent but complementary technologies that business analysts can use to import data, and start authoring rules.RedPen is Sparkling Logic’s patented technology for authoring decisions through point-and-clicks. Using RedPen, business analysts write business rules using a use case approach. The loaded sample data provides the context to create, test, and run rules without prior knowledge of a special rule language and syntax. RedPen mimics what business experts do on paper when they flag decisions with a red pen. When business analysts activate RedPen, they can pin an existing rule, a field of this rule, or a rule set and modify it as if they were using a pen on a paper. They can also create new rules with RedPen, SMARTS will automatically turn them into executable rules. For cases where advanced logical, mathematical, and symbolic manipulations are required, business analysts can use SparkL.
SparkL (pronounced “sparkle”) is Sparkling Logic’s language for writing rules in a natural language format. SparkL can be used by business analysts with no formal technical background in rules syntax while still benefiting from mathematical expressions, string manipulations, regular expressions, patterns, dates, logical manipulations, constraints, and much more. They can express any imaginable decision logic and symbolic computation, making it the choice for highly sophisticated decisioning applications where the conditions as well as the actions can take a great variety of forms.
Other cases where the decisioning projects necessitate formal requirements and decision modeling, the standards development organization (OMG) offers a standard called Decision Model and Notation (DMN). Sparking Logic has adopted this standard and developed Pencil to operationalize DMN.
Authoring in the context of DMN standard
Pencil is a tool for users to model business decisions by dragging and dropping graphical icons to form a decision process. Pencil models comply with the DMN standard. Using an intuitive graphical interface, business analysts can immediately start capturing data requirements, decision models, and business rules, while collaborating to achieve the best explicit description of the decisions required for systems and applications. Pencil’s glossary can be used across decisions to achieve consistent use of terminology related to decisions. Business analysts can create or import data and then execute, test and continue to refine and improve decisions. Once decision modeling is done, Pencil provides a direct path to an executable decision.With SMARTS, a user has not to adapt to the tool, but the reverse, it is the tool that adapts to the user. The business analysts select the appropriate way for the task at hand. In the same project, they may choose Pencil to model decisions, RedPen for the major part of the application, and SparkL for the rest of the application. At any time, they can choose to display the rule sets as a group of rules, a decision table, a decision tree, or a decision graph. Moreover, they can switch from one representation to another and vice versa.
Orchestrating business apps
As intuitive as a decision management tool can be, it may never meet the needs of a real business person. The bells and whistles that business analysts need can be overwhelming for the credit manager or insurance underwriter who needs access to decision logic. This person is certainly more inclined to exploit decision-making logic than interested in learning how to create it, and even less in training on a rules authoring tool.For untrained business users, SMARTS sets the bar higher towards more simplification, and still within the same interface. They have full control over the configuration, management, and assembly of the decision applications that business analysts have developed, and they can do it all through web forms and point-and-clicks. With this added level of abstraction, untrained business users, business experts, and ‘citizen developers’ can adapt to industry regulations, company policies, and market dynamics, without IT intervention beyond the first installation.
Takeaways
- Business rules have moved from coding rules in “IF THIS THEN THAT” statements to authoring them with data, standards, and apps
- SMARTS implements this new way via RedPen, SparkL, and Pencil, three independent but complementary authoring tools that business analysts can use to express their decision logic
- Business users need business applications, not authoring business rules or developing machine learning models
- SMARTS gives business owners full control of business apps through web forms and point-clicks
- Today change is the rule, with SMARTS, automated decisioning is flexible to accommodate ever-changing regulations, company policies, and market dynamics
If you envision modernizing or building a credit origination system, an insurance underwriting application, a rating engine, or a product configurator, 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. 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 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.
Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at hlaasri@sparklinglogic.com.
Tags: business rules • decision automation • decision management • decisioning • DMN • RPA • rule authoring • SMARTS
How to implement a Rating Engine
Rating engine, pricing engine, compensation calculation, fee calculation, claims calculation, and many more types of projects aim at calculating a fee or cost. They typically share some complexity on the fine prints.
Implementing a rating engine has relied on different technologies over the years. The debate continues: can business rules be used for a rating engine? My answer is yes and no. I strongly believe that we need the full power of a decision management system. Let me explain.
When the rating engine logic is pretty straight-forward, business rules will do a good job. In the pay-as-you-go demo, we could summarize the pricing strategy in a few business rules. In real-life projects, compensation calculations and benefit calculations have also been implemented in a modest number of rules. As a rule of thumb (no pun intended), I would say that if you can express your rating logic as a formula, with or without a few exceptions here and there, you are in a good shape using business rules only.
Business rule engine is not enough
The limitation, though, is when these rate tables grow significantly. Some of these spreadsheets include tens of thousands of rows! This combinatorial explosion makes sense when you think of having rates specific to:
- 50 states,
- maybe up to 42,000 zip codes,
- 2 or 3 genders (some states can’t legally discriminate over gender, so you may deal with a gender-neutral pricing in addition to male and female)
- several risk score bins,
- many, many, many age options…
Writing all of these rules by hand may be a significant task. Rule import can help, of course, but keep in mind that it is likely a one-time solution. Updating and maintaining these rates over time will become a painful task, less painful than coding of course, but still painful. When actuaries come up with an updated rate table, finding the right line items could be a tricky exercise. My experience tells me that rare business experts provide a color-coded spreadsheet, highlighting only the changes. You certainly do not want to be in the business of comparing line by line the old and new rates.
However, the great advantage of business rules is their extreme flexibility. Assuming that the rating tables per se are taken care of, which I will cover on my next point, overrides for specific states, or product options, remains trivial by complementing the rating engine with business rules. Often, I see projects in which these overrides happen while or after retrieving rates. There is literally no end to the flexibility you can implement as business rules once rates are retrieved.
My first takeaway: I do like using business rules for fine-tuning rates, dealing with exceptions.
Rating Engines are mostly lookups
For rates that are dependent on this (sometimes) enormous number of lines to pick from, I prefer using lookup models. Lookup models are spreadsheets that can be used to return the matching columns. Let’s say you import a spreadsheet with state, age, gender, and score range. The lookup model will retrieve the rate that matches all 4 columns. You are not limited to one column to return though. In addition to the rate, the spreadsheet could return volume discount or any additional information.
The main advantage of using lookup models, in my opinion, is that there is no manual translation from spreadsheet to executable lookup model. The implementation effort is in the interface. Do you pass the actual gender for example, or do you translate its value to ‘neutral’ for those states that prevent you from using gender? Do you return a single rate, or is it possible to return multiple rates?
While rate tables can be as large as your business experts might dream, lookup models offer a great advantage in performance. They are indexed, and can return rates very quickly, somewhat independently of size.
My second takeaway: I do like using lookup models for retrieving rates.
Addressing evolving rates
As I mentioned before, the majority of the pain is typically in the maintenance of the rate tables in the rating engine. Using lookup models, this pain disappears. Updating rates is as easy as uploading a spreadsheet to the system. Overall, the 2 key aspects that I love about these mechanics deal with time-travel, seen from 2 different perspectives.
(1) The convenience of version tracking
Although rates change over time, you may need to resurrect these past rates. Many reasons come to mind. You might need to justify what your rates were at that time. Or, in urgency, you might need to backtrack bad rates that made their way into production.
Thanks to the underlying versioning system, and release management, these past rates are just a click away. Time-travel to the January 2020 release of your rating engine to resurrect pre-pandemic rates, or just to run simulations. You can also use the version history to promote that specific version of the spreadsheet to become current again. You will never lose any iteration of that spreadsheet that went into production. Peace of mind!
(2) The flexibility of time-sensitive rates
The introduction of new rates into your rating table is likely to follow a specific schedule. It happens that rates are just adjusted directly. But, more likely, they will start on a specific date, like January 1st. Rather than scheduling a job that will update the rates at midnight, it makes more sense to upload these rates upfront, and specify their effective dates.
Like business rules, rate tables can follow effective dates that apply to the entire sheet. Using the full power of business rules, you can activate these 2021 rates on January 1st for a group of states, and at a later date for another group of states.
I particularly appreciate that you have full control over the clock. Do you switch on January 1st at midnight at your headquarters? Should you adjust for the local time zone in each state? Do you consider invocation time? Or do you apply the proper rates based on delivery date? Once again, the sky the limit. I have yet to see a scenario that could not be implemented using a decision management system.
My third takeaway: I do like using lookup models for managing rates over time.
In conclusion, I highly recommend you consider a decision management system when implementing a rating engine. Business rules may have been limited in the past. However, decision management systems can certainly take you there now!