Legacy Modernization
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.
Our customers — who they are, what they want, and what we bring them
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.
Business analysts
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
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.
IT
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.
Data scientists
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.
Management
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.
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.
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.
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.
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
Transform your Legacy Code into an Agile Decision Service
Modernization of Application Business Logic
Today, legacy modernization initiatives are everywhere. The need to modernize systems in order to transform businesses is often a requirement, and the stakes can be high. An enterprise may feel extreme business pressure to operate like its smaller more agile competitors. Getting to that agile “state” can be a huge endeavor when a 7-10+ year old legacy system has to be modernized as part of the process. There are a number of companies offering products, services and methodologies related to legacy modernization. I’ve added some links to resources at the end of this post.
You can think of “core business logic” as the decisions that a system makes as it processes transactions. For example, the core business logic of a legacy insurance underwriting application, might include decisions such as whether or not the applicant is eligible for coverage, what is the level of risk, whether to approve, deny or refer, and what to charge for premiums. Modernization of those decisions would involve a recreation of the business logic that’s in the legacy system. But on a modern, agile platform that can meet today’s business needs.
According to James Taylor, from Decision Management Solutions, “ By focusing on the decisions represented in the core business logic you are likely addressing the most costly to maintain aspect of a legacy system. Decision-making is often high-change with new regulations, new policies, competitive response and evolving consumer/market conditions driving a need to make changes. For example, the way a fee is calculated, the way an application is validated, the way a claim is checked for eligibility – these kinds of decisions, hidden in the core business logic of legacy systems, result in big maintenance bills and long periods where the system works incorrectly or inconsistently with the business need.”
With a focus on the core business logic, how do you approach modernization?
Code Conversion Legacy Modernization Approach
Often, for modernizing business logic functionality, organizations, and their consulting partners, will take a “code-focused” approach. Using various tools, the migration is accomplished by assessing the legacy software code, analyzing and uncovering core business logic, applying conversion tools to create modernized code, and finally, testing for functionality and errors.
Code Conversion – Limitations
Although this is a viable way to approach the modernization of business logic, it has two significant limitations:
Today’s platforms are different
Older system architectures are less functionally modular than today’s microservice architectures. Most legacy systems were built as monolithic applications or client-server systems. The business logic in these systems was likely based upon procedural or object-oriented design and scattered throughout functions or methods in COBOL, C, or C++ code. (Yes, today some systems developed in “modern languages” like Java and C++ are now considered legacy!) The concept of organizing business logic around decisions and services simply did not exist when these applications were developed.
Today’s preferred approach to core business logic is to define decisions in a decision management platform, where the decision rules will be organized according to how the enterprise thinks of the business problem. Decisions are then deployed to a decision service and integrated into modern architectures through a REST API.
Legacy systems weren’t built for change
The legacy system has undoubtedly been enhanced a number of times over an extended period – incrementally. By incrementally, I mean that changes made to the legacy system were based on the delta between the system in its then current state, and some new functionality that more accurately reflected what the business needs were at that time.
If a legacy system had been in use for, say, a 7-year period, with 2 incremental updates per year, those 14 incremental changes in functionality were always constrained by the systems previous state. Legacy systems simply weren’t built to support change and provide the ability to change in unexpected ways.
The result is that the code inside these legacy applications is complex – more complex than if it was built from scratch to have the same functionality it has today. It’s not just spaghetti code- it’s spaghetti on top of spaghetti! Converting that functionality to a modern platform using code-conversion tools alone can bring all the extra, unneeded complexity into the new platform.
Decision Management – Data-Driven Legacy Modernization Approach
Today’s modern decision management platforms use data and analytics to enable the process of improving operational decisions. Looking at historical results from the legacy system, adjusting rules inside a decision, and then running tests comparing the results, is an effective way to transform legacy business logic into modern decision services.
Using insurance underwriting as an example, you could look at the applicants that were approved by the legacy system (i.e. legacy system historical results) and compare them to the applicants that would be approved using a baseline set of business rules in a decision management system. Analyzing mismatches between the two sets of results could drive the discovery of which rules are missing or need to be adjusted to produce matching results.
For example, you might discover that 25% of the differences in approval status are due to differences in risk level. This insight leads you to focus on adding and/or modifying your risk related rules. Once the legacy code and the rules are assigning the same risk level, the overall mismatches have been reduced by 25%. Repeating this analyze-improve step will reduce your mismatches until the results from the modernized business logic exactly match those from the legacy system.
Decision Management – Benefits
The modernized business logic, expressed as business rules in a decision management platform, doesn’t follow the complex path of how the legacy system was created or updated over years; but it does get the same results. It’s a simpler, and easier to maintain and extend, representation of the business logic.
Also, using a decision management platform to hold and maintain the modernized business logic brings with it the standard agility benefits of decision management:
- Easy to change
- Understood and often managed by Business Analysts
- Change management control through versions and releases
- Highly scalable deployment
Summary and Resources
Using this “data-driven” approach to recreate the core business logic of a legacy system on a modernized platform can complement, and in many cases, replace a code conversion approach to modernization. At a minimum, in the large and complex world of legacy modernization, it can be an important part of the toolkit and methodology to achieve success.
Below, I’ve listed some resources for legacy modernization, and some for Decision Management. Please let me know about others we should include.
Legacy Modernization Resources:
Decision Management Resources: