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SMARTS in credit and risk management


SMARTS in credit and risk management

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

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

Selected use cases

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

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

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

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

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

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

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

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

Benefits

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

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

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

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

Further reading

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

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

About

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

Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, decision technology platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.

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

Noise reduction in digital decisioning with Sparkling Logic SMARTS


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

Noise in organizations’ decisioning and what to do about it

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

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

Noise reduction with explicit decision rules, dashboards, and analytics

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

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

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

During the testing phase, the users have access to SMARTS’ built-in map-reduce-based simulation capability to measure these metrics against large samples of data and transactions. Doing so, they can estimate the KPIs for impact analysis before the actual deployment. And all of this testing work does not require IT to code these metrics, because they are transparently translated by SMARTS.

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

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

About

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

Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.

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

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.

LTCG Balances Technology and Human Touch in Claims Processing


LTCG

Long Term Care Group (LTCG) is a leading provider of business process outsourcing services for the insurance industry. They are the largest third party long term care insurance provider offering underwriting, policy administration, clinical services, as well as claims processing and care management for America’s largest insurance companies. Insurers rely on LTCG for these services due to LTCG’s deep expertise in long term care portfolios, which require specialized knowledge and processes. LTCG continually invests in the people, processes, and technology to maintain their leadership position in the industry.

Several years ago LTCG developed and implemented an automated claims adjudication process using Sparkling Logic SMARTS as the decision engine. Prior to this initiative more than 90,000 claims per month were processed manually by LTCG’s team of claims examiners. LTCG wanted to reduce the time their claims examiners needed to spend researching and making a claims decision in order to maintain the highest levels of customer satisfaction.

Long term care insurance is unique in that benefits are coordinated by care coordinators who create a plan of care to help policyholders leverage the benefits covered by their policy based on clinical guidelines that direct care needs over time. Due to the unique nature of long-term care needs, LTCG wanted to balance the use of technology with their emphasis on human touch to ensure the best possible care and coverage for policyholders.

The first automated claims adjudication system was developed in 6 months using an agile methodology and Sparkling Logic SMARTS. The Scrum team was able to iterate on the business rules and logic quickly thanks to the simplicity and power of the SMARTS user interface and software architecture.

Download the LTCG Case Study to learn more.

Is Insurance ready for the disruption swarm?


After years of disruption in the banking and payments industry, tech rebels are setting their sites on an adjacent financial target – insurance. According to SVIA, this is a $5T prize, where several new technology trends are converging to change the face of insurance. Like a swarm of locusts, new business models, shared economy, Internet of Things and core technology advances have all made leaps in the last few years, promising great change on the face of the established insurance institutions.

digital insurance applications
Applications of Digital Technology in Insurance, source: Deloitte
In a recent LinkedIn blog post, I wrote about recent developments in the Silicon Valley, where numerous startups work daily to challenge status quo, long overdue for an overhaul. To compete in the new insurance economy, insurance IT, business analysts and data scientists will ALL need to rethink core tools they use to discover, test AND rapidly deploy their business models. It will be increasingly important not only WHAT decisions are made, but also how QUICKLY. Fast discovery, testing and nimble deployment are essential to compete in the battle that is about to engulf the insurance industry.

Modern SaaS, analytics and decision rules engines like Sparkling Logic allow insurance players to quickly discovery and modify business and risk logic. 20-year-old tools simply will not do. Without latest tools platforms, insurance carriers will find it difficult to defend themselves against modern fraud, advanced risk and emerging insurance business models.

Social Media New Order: Be Nice, Be True


Social & Friendly

Social Media has been soaring in the workplace during the tough years of the recession and keeps getting momentum.  With reduced budget for travel and less personnel, providers and end-users have been creative in finding ways to leverage the social media platforms.  Many ideas have popped up here and there to leverage Twitter or Facebook, some better than others of course.

I found pretty interesting this enthusiasm for true relationship, even if partially digital.

During one of my discussions with Betsy Burton from Gartner, we talked about what may have caused it.

The Chicken and The Egg (again)

I know… I do like this analogy…  For a biologist by trade, it is a fundamental question of course.  But let’s not digress…  Back to Social Media and Relationships…

Gartner explained in an early Pattern-Based Strategy presentation that people turned slowly into numbers.  Think about it: we are our social security number, we are our credit card number…  If this was not the case, identity theft would not thrive as much as it does.  People are getting more aware of that unfortunate reality and as a result value “human relationship” more than they have in the past: it differentiates them from their digital existence.

Social Media was there at the right time.  Extensively used by teenagers, suddenly it became exciting to the workforce as well.  Services like Twitter allowed professionals to interact with thousands or millions of tweeps around the world.  Talking about your everyday life on Twitter may have sounded unproductive initially but it fulfilled the human aspect: being personal, sometimes intimate…  People had finally an opportunity to be something else than a risk score.  Of course over time more and more companies are finding ways to make use of that service, 140 characters at the time.

With a wider array of social details available and the development of Socialitics (analytics applied to social networks data), a fantastic source of information is becoming readily available.  When you explore professional connections within LinkedIn, you can infer “something” about any professional.  Digging deeper into the larger mesh of social networks, you can discover more about what they read, what they watch, who they spend time with, etc.  Despite the threat of identity theft, people are sharing more details than ever because they find value in it.  They can reach out to people that share the same interests or the same goals, professionally or personally, and develop a relationship that fulfills a real need.

I questioned Betsy as to whether Transparency, as one of the 4 pillars of Pattern-Based Strategy, was suddenly valued and appreciated as a reaction to our “digitalization” alone or whether it was also due to the fact that Social Media suddenly made it available?  Transparency may have been a value all along but only truly exposed with the explosion of social media.

Some pretty dark practices happen all the time in business unfortunately.  People abuse companies, companies abuse people, people abuse people or companies abuse companies.  Whether they use power or deception or anything else, the result is the same.  Over the past couple of decades, more people and companies have been increasing more conscious.  As a result some initiatives, going against pure greed, such as Sustainability have been nevertheless successful.  It is clear that one person alone cannot do it all.  The new power comes from collaboration across boundaries — companies, countries, etc.

Transparency is also like fuel for Social Media.  You get the full benefit of your participation when you actually participate.  If you are true to the network, if you are transparent, then you will be able to connect with people of the same vein and reach a higher level of exchange, more practical advice, more comfort or greater motivation.  Granted Deception could make its way into the system.  It reminds me of the Population Dynamics theories I learned in school…  The fact is that Social Media seems to be pretty effective at policing itself.

  • First, there is a fairly significant investment for a true participation.  “Faking it” seems like an unreasonable investment for pitiful results, not worth it.
  • Second, it is hard work to maintain a fake identity.  The risk of being uncovered is not small, so eventually those individual disappear.  Social Networks also actively develop mechanism to eject those people — Twitter launched verified accounts for celebrities mostly…
  • Lastly, social networks are dynamically shaping themselves: poor content and/or pure sales pitches without a little human value-added get quickly rated as such and attendance quickly drops

Transparency, wits, content have naturally become prime currencies on those Social Networks.

So…  Is the need for Transparency a consequence of Social Media dynamics?  Or is Social Media’s success a consequence of the need for Transparency?

1 + 1 = N

Being a Product Manager in my heart, I am always wondering what people truly want…  Some companies have been successful at deploying a community of users.  I actively participated in designing one for a previous employer but found true limitation in the technology or configuration that was chosen.  What I was quite happy to see though is that some users got involved.  A handful of passionate users took ownership of some technical questions.  I believe there is a huge potential for even more sparkling exchanges.  I am playing with this idea so stay tuned!

I have been impressed with the Transparency of companies that were open to share a lot of design details even to their competitors as part of industry user groups.  As I was told in “Ecole Preparatoire” (an elitist process to select students in France), the likelihood of a fellow student to take your spot are tiny, keep in mind that you will gain much more by supporting each other.  That quote from my teacher stuck with me (while students in other schools were busy stealing each other’s notebooks!!!).  I love to see that companies are adopting the same philosophy.  I love to see that Insurance companies that are direct competitors are willing to teach each other how to use Decision Management technologies because they will not compete on whether they design their Business Rules services this way or that way, they will compete on the strategies that the business rules execute.  Helping each other will increase each participating company’s skills at effectively leveraging the technology so that they can focus on content.  It is beautiful to see it happening in the real world.

This is not completely new as organizations like OMG have promoted competitors’ collaboration in designing standards for many years.  Social Media act as an amazing catalyst for more of that.  There is so much more we can accomplish when we get together…


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