If you were not at FinovateSpring 2019, here is your chance to catch up with our favorite demo. Enova demonstrated how its Enova Decisions Cloud solution allows mid-market business analysts to integrate data-sources and deploy predictive models without going back to IT. EDC is powered by Sparkling Logic SMARTS!
Make sure you watch the video till the end for the “blow your mind” reference 🙂
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.
The Willis Towers Watson Story
Willis Towers Watson (NASDAQ: WLTW) is a leading global, advisory, broking, and solutions company that helps their clients turn risk into a path for growth. To stay competitive and to continue to meet the needs of their financial services customers, Willis Towers Watson embarked on an initiative to transform how they made decisions. Placing a priority on making more informed, data-driven decisions brought together their community of broking experts to ensure they could drive the best result for their customers.
As Willis Towers Watson looked to replace silos of data, manual processes, and custom applications that were difficult to maintain and test, they turned to Sparkling Logic SMARTS. SMARTS is a modern, agile, and easy to implement cloud-based rules engine and decision management platform that Willis Towers Watson is harnessing to drive their competitive edge.
When they started evaluating rules engine and decision management solutions, like most organizations, they wrote an RFP with very specific requirements. They were launching “Connected Broking” – a global placements platform and needed the rules engine to determine panel eligibility for insurance panels and they needed to automate decisions to channel the right risk to the right marketplace to better serve their customers.
However, once Willis Towers Watson selected SMARTS, they began to identify new use cases where the software could be applied to solve other business problems that were never even considered during the RFP phase. In some cases, the program management team saw uses for SMARTS that even their rules authoring lead and business analysts didn’t believe were a good fit for a rules engine.
The two new, not-so-obvious use cases that Willis Towers Watson considered were:
- Dynamic Data Capture
- Task Management
Let’s dive in a bit further:
Dynamic Data Capture
Willis Towers Watson identified an opportunity to apply business logic and rules to dynamically determine what types of data (and the sequence of that data) would need to be captured from an end customer so that the insurance carrier could make appropriate credit decisions and offer the best products and services. The team integrated SMARTS with a webform they developed to drive the data type and data sequence presented based on the application of business rules. Then, the webform was built to call back to SMARTS to validate the data that was entered. This solution is in production today.
Willis Towers Watson currently has plans in place to use the decision engine in an orchestration capacity. Specifically, they will use SMARTS in combination with a lightweight task management application that can trigger allocated tasks based on business events – getting the right task to the right person in the right application.
With each new use case discovered, Willis Towers Watson is looking to continue to harness and extend the value of SMARTS. It has been possible for the company to explore new use cases quickly due to the ease of implementing and managing SMARTS including the limited amount of developer and analyst resources required to author rules. As a result, the team has been able to devote time and resource to building the framework and integrations for both dynamic data capture and task management routing.
To learn more about how Willis Towers Watson achieved these results, read their case study.
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 recent blog post, by one of our partners, Mariner, describes the high-level architecture they use to implement Internet of Things (IoT) solutions for their manufacturing and distribution customers. Data from devices and sensors is collected, aggregated, and enhanced using the Microsoft Azure suite of technologies and passed to a SMARTS decision service.
In this post, we’ll explore how Mariner uses the SMARTS decision management platform, to make real-time decisions based on sensor data.
Let’s look at an actual customer story. ABT Power Management is an innovative power management company based in North Carolina. ABT has a joint product/service offering called GuaranteedPOWER
® where they use an IoT solution (developed by Mariner) to proactively manage and maintain the batteries that power lift trucks and other material handling vehicles. It turns out that if the batteries aren’t properly maintained, over time they can actually cost more then the trucks they power. (See the ABT Power Management Customer Story.)
As the high-level architecture diagram illustrates, sensor data from batteries and chargers is collected and enhanced prior to being passed to a SMARTS decision service. The SMARTS decision service alerts ABT to dispatch field engineers to the site when equipment maintenance or repair is needed.
The knowledge on how to properly maintain the batteries and chargers is based on the manufacturers’ recommendations and the expertise of ABT’s field engineers. The engineers have a deep understanding of the technology and years of real-world experience. Mariner worked with the engineers to translate their knowledge into concrete SMARTS decisions that define how to proactively manage and service the batteries and chargers. These decisions are made up of rules that define when to perform specific maintenance services such as charging, watering, and rotating the batteries at a customer’s site.
Capturing the Knowledge
Let’s look at a simple example. One condition the sensors monitor is the water level in the batteries. When the water level is low, the battery needs water. You could write a simple rule to capture this:
IF battery water level is low
THEN create an alert to water the battery
But in the real world, this rule is not quite as simple as it seems at first glance! Battery sensors are very sensitive instruments and when a battery is being physically moved it could register a low water condition. So the rules need to take this knowledge into account and perhaps detect if the battery water level registers low for two or more consecutive days. Also the rules need to consider additional factors in deciding whether or not to water the battery. For example, how many other maintenance actions are required at a site? How much time does it take and how much does it cost to dispatch a field engineer to the site? And, can the maintenance visit be scheduled so that it coincides with maintenance and repair required by other customers who are in the same geographical area?
All of this knowledge is captured in the decision making rules that analyze the sensor data. In addition, predictive analytics is used to detect patterns in the data that could lead to future equipment failures. Preventative maintenance is scheduled to prevent these failures.
Mariner’s business analysts captured and tested these rules and decisions in the SMARTS Analyst Workbench (watch this video to see the rules in SMARTS).
Mariner also used SMARTS to run simulations using historical data in order to ensure that the decisions (and resulting alerts) were consistent with the recommended actions specified by ABT’s field engineers.
Deploying and Continuously Improving
Once tested and validated, the rules were deployed to the SMARTS decision service, where they make proactive maintenance and servicing decisions in real time. Over time, new rules have been added and existing rules have been improved and refined so that more conditions can be automatically detected and acted upon.
In summary, SMARTS Decision Manager provides an ideal platform for automating and deploying IoT real-time decisions. SMARTS helps organizations make sense of vast amounts of sensor data and translate it into concrete actions.