Digital business provides new revenue and value-creating opportunities, helping organizations to:
- Improve customer engagement and retention with next best action marketing.
- Process more claims faster and more accurately.
- Reduce the cost and difficulty of regulatory compliance.
- and more.
Next Best Action
Digitizing, personalizing and improving customer interactions is critical to business growth, customer retention and engagement. Executives want to ensure that every interaction with each customer adds value. They want to deliver the next best action for each customer, across a portfolio of existing systems and evolving channels, at a reasonable cost.
Different from traditional upsell or cross sell campaigns based on segmentation or one-size-fits-all campaigns, next best action programs leverage decision modeling, predictive analytics and business rules technology to create a dynamic view of the customer that automatically customizes the next best action for each individual customer interaction.
In a digital business, decision modeling defines the next best action, a Business Rules Management System (BRMS) makes it easy for the marketing team to update their program without involving IT, and predictive analytics on customer data ensures the right message or offer is conveyed.
Today’s insurance claims systems increase efficiency through process automation and workflow. However, manual decision points create bottlenecks and processing delays, underutilize business expertise and analytical insights, and lack agility and transparency.
Digital claims processes powered by decision modeling, business rules and predictive analytics reduce manual reviews, detect fraud before claims are paid, and enable business users to manage and change rules directly.
For example, it is often clear whether the claim should be approved or not, yet in many claims systems the decision point is handled by workflow with the validated claim being routed to someone for adjudication. This consumes time, money and resources. Most policies and regulations are written up as requirements and then hard-coded after waiting in the IT queue, making changes slow and costly. In a digital business, decisions are explicit and modeled accordingly, rules are written to be understandable by the business, not buried in software code, and the business owns the ongoing maintenance of the rules.
In another example, the use of predictive analytics to find fraudulent claims is growing. Fraud analysts may mine data to find the key risk factors for fraudulent claims. But today this analysis is often done after the fact, resulting in a “pay and chase” approach. Digital claims processes can apply this fraud insight so the claims that need review are flagged before they are paid, reducing costs and improving the odds of catching fraudsters.
Key compliance challenges facing organizations include:
- System changes, even simple ones, take too long.
- Business requirements are translated into program code, making them difficult for SMEs to comprehend or validate.
- Measuring the correctness and effectiveness of a system’s outcomes is difficult.
- Demonstrating compliance is challenging in a complicated web of systems.
Digital compliance processes powered by decision modeling, business rules and predictive analytics enable rapid, transparent and safe changes to business rules by business users. Decision modeling’s standard way of representing precise business meaning makes it easy to understand, without using code. Once defined, decisions can be specified in detail using rules and predictive analytics where applicable.
Decision logic captured in a BRMS has many advantages over traditional computer code. In particular, business rules are easier to read for non-programmers and easier to change than hard coded logic. A BRMS also offers interactive tools for testing, documentation, impact analysis and more. And a BRMS is an effective approach to the upgrade of the regulatory infrastructure.
How Decision Modeling, Business Rules and Predictive Analytics Work Together to Deliver Digital Business
Most organizations today think of business rules and predictive analytics completely separately and miss the opportunity to drive better decision making by applying them as a set. They are getting value from point solutions but are struggling to deliver on the full promise of a digital business.
A graphical decision model based on the industry DMN standard is easily and widely understood across business, IT and analytics teams. Decision modeling streamlines business rule analysis, makes clear where predictive analytic models can be applied to improve decisions, and links both business rules and analytics to business objects. Decision modeling ties these powerful capabilities together as a coherent set.
A modern BRMS manages the executable decision logic – the business rules – for the decision-making being automated. Business users can change the rules and see the impact of a change before they deploy it. Business rules based on a decision model are simpler and easier to manage, too. And a BRMS makes a great platform for operationalizing analytics.
Predictive analytics improve the accuracy of decision-making and ensure continuous improvement and decision models show exactly where an analytic will make a difference. Business partners have a much clearer vision of what the predictive model could do, and how it can be improved in an iterative process as more information is made available. A decision model also clarifies for the data science team what model would best serve the business problem, and it is often different than what was initially considered.
Delivering Digital Business
Digital business is powered by improving and automating decisions. Being explicit about decision making, and improving decisions with rules and analytics, is the foundation for transformational improvements in business performance.