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.
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.
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.
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.
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.
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 email@example.com.
In this post, we present how to deal with the problem of noise, which is both a source of errors and biases in digital decision-making in organizations, through explicit decision rules, dashboards, and analytics. To illustrate our point, we use the example of the Sparkling Logic SMARTS decision management platform.
Noise in organizations’ decisioning and what to do about itIn an interview with McKinsey, Olivier Sibony, one of the renowned experts in decisioning, recommends algorithms, rules, or artificial intelligence to solve the problem of noise, a generator of errors and biases in decisioning in organizations. This recommendation resonates with our vision of automating decisioning — not all of the decisioning but the operational decisions that organizations make by thousands and sometimes millions per day. Think credit origination, claim processing, fraud detection, emergency routing, and so on.
In our vision, one of the best ways to reduce noise, and therefore errors and biases, is to make decisions explicit (like the rules of laws) so that those who 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 analyticsOur SMARTS decisioning platform helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation, reducing biases that could be a failure to comply with industry regulations, a deviation from organizational policies, or a source of an applicant disqualification. The consequences of biases could be high in terms of image or fees, and even tremendous for certain sensitive industries such as financial, insurance, and healthcare services.
In SMARTS, business users (credit analysts, underwriters, call center professionals, fraud specialists, product marketers, etc.) express decisions in the form of business rules, decision trees, decision tables, decision flows, lookup models, and other intuitive representations that make decisioning self-explainable so that they can test decisions individually as well as collectively. So, at any time, they can check potential noise, errors, and biases before they translate into harmful consequences for the organization.
In addition to making development of decisioning explicit, SMARTS also comes with built-in dashboards to assess alternative decision strategies and measure the quality of performance at all stages of the lifecycle of decisions. By design, SMARTS focuses the decision automation effort on tangible objectives, measured by Key Performance Indicators (KPIs). Users define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. Moreover, they can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.
During the testing phase, the users have access to SMARTS’ built-in map-reduce-based simulation capability to measure these metrics against large samples of data and transactions. Doing so, they can estimate the KPIs for impact analysis before the actual deployment. And all of this testing work does not require IT to code these metrics, because they are transparently translated by SMARTS.
And once the decisioning application is deployed, the users have access to SMARTS’ real-time decision analytics, a kind of cockpit for them to monitor the application, make the necessary changes, without stopping the decisioning application. SMARTS platform automatically displays KPI metrics over time or in a time window. The platform also generates notifications and alerts when some of the thresholds users have defined are crossed or certain patterns are detected. Notifications and alerts can be pushed by email, SMS, or generate a ticket in the organization’s incident management system.
Rather than being a blackbox, SMARTS makes decisioning explicit so that the users who developed it can easily explain it to those who will operate it. Moreover, the latter can adjust the decision making so that biases can be quickly detected and corrected, without putting the organization at risk for violating legal constraints, eligibility criteria, or consumer rights.
So, if you are planning to build a noise-free, error-free, and bias-free decisioning application, SMARTS can help. The Sparkling Logic team enjoys nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have them satisfied, but also proud of the system they build. We helped companies to build flaw-proof, data-tested, and scalable applications for loan origination, claims processing, credit risk assessment, or even fraud detection and response. So dare to give us a challenge, and we will solve it for you in days, not weeks, or months. Just email us or request a free trial.
AboutSparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful digital decisioning platform, accessible to business analysts and ‘citizen developers’. Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.
Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.
Tags: business rules • decision automation • decision management • decisioning • DMN • RPA • rule authoring • SMARTS
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!
ETCIO (an initiative of The Economic Times) has interviewed KM Nanaiah, country manager at Equifax. The article highlights the details of the tooling that is now available to financial institutions. They will see dramatic improvements in customer acquisition and loan decisioning.
Digital Disruption + Risk Management
Digital Disruption is at the top of every banking and insurance CEO’s agenda in 2017: how to become the disrupter and avoid getting disrupted. Across all credit-driven financial services firms, the pressure is intense with new market players emerging in all realms creating new expectations from customers.
Credit Risk Management and Decisioning are emerging as key scenarios that are ripe opportunities for digital disruption for two primary reasons.
First, the impact of credit risk decision management and compliance is significant to the bottom line and incremental improvements to processes are no longer enabling lenders and insurers to keep pace.
McKinsey reports that, “In 2012, the share of risk and compliance in total banking costs was about 10 percent; in the coming year the cost is expected to rise to around 15 percent… banks are finding it increasingly difficult to mitigate risk…To expand despite the new pressures, banks need to digitize their credit processes.” Top performing firms not only need to eliminate inconsistent approaches to credit analysis that expose them to unnecessary risk. To leap frog, they need to develop a systematic approach based on the integration of new data sources and credit-scoring approaches rather than relying solely on the historical performance indicators.
Second, risk management is, by its very nature, a data-driven discipline well positioned to take advantage of the massive advancements in analytics technologies at the new levels of scale enabled by cloud computing. This is dramatically lowering the cost of all solutions related to credit risk management for small to mid-sized financial services institutions, including FinTech startups that can enter the market quickly with limited barriers to entry.
What is the Opportunity in 2017?
Banks and Insurers can manage increasingly complex data under a higher volume of business rules. At the same time, they can apply an agile management framework of rules and data to take advantage of market opportunities in real-time. This is now possible at a fraction of the cost and time to implement compared to even five years ago. Our partnership with firms like Equifax is paving the way for the next wave of digital disruption in the financial services industry in scenarios like credit risk management and fraud detection.
The Equifax Story
Equifax has offered their leading, cloud-based decision management solution called InterConnect to their global customers for many years. The InterConnect solution “automates account opening, credit-risk decisioning, cross-selling and fraud mitigation during the account acquisition process.”
In 2016, Equifax was looking for ways to help their customers capture new opportunities in their credit risk management and decisioning process by strengthening one of the core components of their InterConnect platform: the Rules Editor.
Equifax’s customers were looking for enhanced support in defining, testing and optimizing business rules. Even more importantly, they needed to rapidly seize competitive advantage through the agile implementation of new business rules and automated optimization strategies based on real-time results, as well as the development of test data for repeated use to enable greater consistency and scale.
Equifax turned to Sparkling Logic as a key partner to fulfill these requirements for InterConnect. Sparkling Logic’s decision management engine powers the enhanced Rules Editor. One specific strategy that was not previously possible was the testing and implementation of Champion and Challenger credit decisioning strategies.
Before Sparkling Logic, customers struggled to compare two or more decisioning strategies at the same time. With Challenger and Champion strategies now enabled in the enhanced Rules Editor, new strategies (“Challengers”) can be developed, tested, and deployed simultaneously with existing strategies (“Champions”). Winning strategies are immediately applied to new decisions after the initial test period. Additional revenue is now captured that would have been lost while you waited for one test after another to play out.
What’s Next? How do you replicate this model to leap frog your digital disruption strategy?
While your competitors are busy applying incremental improvements to their portfolio management strategies and using historical performance data to drive crediting decisions, you have the opportunity to leap frog. This is possible when you immediately capture available revenue opportunity by applying an automated decision management engine to your credit decisioning processes.
Largest P2P Lending Market in the World
Fintech is a hot topic around the globe and China is no exception. The Chinese peer-to-peer lending market is the largest in the world exceeding $150 Billion in 2015. The 2,595 Chinese P2P lending platforms, counted at the end of 2015, have cumulatively brokered 1.37 trillion yuan according to a report in China News. These numbers are particularly significant since they came from true peers, small investors with little institutional money powering the sector.
Challenges of Skyrocketing Growth
Although P2P lending in the US is heavily regulated, Chinese platforms operated without regulatory safeguards until 2016. This unregulated environment fueled growth but also resulted in a significant number of failed platforms (896 in 2015) and in some less than credible platforms defrauding unwary investors.
Another issue facing the industry is the lack of credit reporting agencies and FICO scores that exist in developed markets like the US. According to PIIE (Petereson Institute for International Economics) Chinese lending platforms use alternative approaches such as reviewing bank statements to identify sources of borrowing that don’t turn up in credit records, verifying whether or not a borrower pays his or her phone bill, and in some cases, platforms even send employees to check on physical assets in person.
Decision Management Addresses Challenges of P2P Platforms
Recently Jin Xu, from Sparkling Logic’s Chinese partner, Xinshu Credit, presented at the Global Internet Finance Summit 2016 in Shanghai. Jin Xu discussed some of the challenges faced by P2P lending companies and how Sparkling Logic helps companies, such as Weshare Finance, address these challenges:
- Labor costs, especially for IT engineers, are rising in China. Decision management platforms, like SMARTS Decision Manager, reduce development time and time to market when compared to traditional systems developed using code.
- SMARTS enables business and risk analysts to manage lending decisions with minimal IT support, resulting in a less costly, more agile solution.
- As new fraud schemes continuously arise, SMARTS allows companies to rapidly respond in implementing fraud prevention measures.
- Most P2P lenders require external data to evaluate risk. SMARTS enables the implementation of pre-screening rules to avoid requesting unnecessary and costly external data for ineligible borrowers.
- As data accumulates, SMARTS predictive analytics capability allows companies to extract knowledge from historical data to improve lending decisions.
Weshare Finance, a leading Chinese FinTech company, recently selected SMARTS to revamp its loan processing system. WeShare Finance was founded in March 2014 and is a standing council member of the Association of Internet Finance China. Weshare focuses on providing and cash and installment services to individuals with the motto “mobile inclusive makes life better”. Within 60 seconds Weshare Finance can make a remiitance into a user’s bank account, and is called the handheld ATM of young people.
A little over year ago, Sparkling Logic joined the Fintech accelerator at Plug And Play. We were honored to be selected as one of 29 technology startups (from among 850 applicants) to join the inaugural launch of Plug and Play’s Fintech accelerator.
Plug and Play is the world’s largest global technology accelerator and venture fund with over 350 startups and 300 corporate partners. Plug and Play connects startups to corporations through vertical-specific accelerator programs. The goal of these accelerators is to help startups with funding, acquiring more customers, and fine tuning their business to better match customer demands. In our case, we joined the Fintech accelerator which was focused on financial technology and security. This accelerator was a good fit for us since many of our customers are in the Financial Services industry and are using our SMARTS Decision Manager product for applications such as fraud detection and risk management.
During our time at Plug and Play we have gained new customers and partners. We have also benefited from lots of exposure by presenting to over 80 companies. In meetings and discussions with these companies, we learned more about their needs and where our SMARTS decision management technology can help. This enabled us to refine our messaging and inspired the launch of a new pilot program to help customers develop a meaningful proof-of-value project using SMARTS.
Recently Plug and Play published a blog post and created a video featuring our CEO, Davorin Kuchan. In the blog post and video, Davorin shares more about our experiences during our year at Plug and Play. It will give you a sense of what life is like as a technology startup in Silicon Valley!