There are many compelling use cases for decision management systems:
- Fraud detection
- Underwriting and origination
- Marketing
- Personalization
- Collections
- Government operations
- Supply chain management
- Asset management
- Manufacturing
- GRC
- Healthcare
- Process efficiency
This list is by no means comprehensive. Any time an organization must make a decision over and over again – and where the accuracy or consistency of that decision, its compliance with regulation or its timeliness are important -- decision management systems can play an important role.
While decision management systems are proven in many industries and situations, not everyone is familiar with them so it is useful to categorize suitable decisions to clarify the use cases for this approach. Use cases generally fall into one of these categories:
Eligibility or Approval — Is this customer/prospect/citizen eligible for this product/service?
These operational decisions should be made consistently every time. The use of a business rules-based system to determine eligibility or to ensure that a transaction is being handled in a compliant way is increasingly common.
Validation – Is this claim on invoice valid for processing?
Validation decisions are rules-based, and the rules are generally fixed and repeatable. Validation is often associated with forms, and online versions of these forms are of little use without validation. The move to mobile apps makes validation even more important.
Calculation – What is the correct price/rate for this product/service?
Calculations are usually operational and rules-based. The rules are generally fixed and repeatable, but making them visible and manageable using business rules pays off when changes are required or when explanations must be given. Today too often calculations are embedded in code.
Risk – How risky is this supplier’s promised delivery date and what discount should we insist on?
Making a decision that involves a risk assessment - whether delivery risk, credit risk or some other kind of risk - requires balancing policies, regulation and formal risk analysis. The use of predictive analytics to make risk assessments has largely replaced “gut checks” and allows risk assessments to be embedded in systems.
Fraud – How likely is this claim to be fraudulent and how should we process it?
Fraud detection generally involves a running battle with fraudsters, putting a premium on rapid response and the ability to keep up with new kinds of frauds. Managing the expertise and best practices required to detect fraud using business rules gives this agility, while predictive analytics can help with the kind of outlier detection and pattern matching that increases the effectiveness of these systems.
Opportunity – What represents the best opportunity to maximize revenue?
Especially when dealing with customers, organizations want to make sure they are making the most of every interaction. To do so, they must make a whole series of opportunity decisions, such as what to cross-sell or when to upsell. These decisions involve identifying the best opportunity, the one with the greatest propensity to be accepted, as well as when to promote it and where. A combination of expertise, best practices and propensity analysis using business rules and predictive analytics is required.
Maximizing – How can I use these resources for maximum impact?
Many business decisions are made with a view to maximizing the value of constrained resources. Whether it is deciding how best to allocate credit to a card portfolio or how best to use a set of machines in a production line, deciding how to maximize the value of resources involves constraints, business rules and optimization.
Assignment – Who should see this transaction next?
Lots of business processes involve routing or assignment. In addition, when a complex decision is automated, it is common for some percentage to be left for manual review or audit. The rules that determine who best to route these transactions to and how to handle delays or queuing problems can be numerous and complex, ideal for managing in a decision management system.
Targeting – What exactly should we say to this person?
In many situations there is an opportunity to personalize or target someone very specifically using predictive analytics. Combining the wide range of big data sources and using business rules to constrain this to be compliant with privacy and other regulations creates a personalized interaction that maximizes long term customer value.
The power of decision management systems cuts across industries and across business processes, offering compelling use cases in many areas. The recently updated Decision Management Platform Technologies Report (available here) has lots of additional information on use cases, all based on real customer experience.
James Taylor is the CEO of Decision Management Solutions and is the leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. Taylor is a faculty member of the International Institute for Analytics and is the author of “Decision Management Systems: A practical guide to using business rules and predictive analytics” (IBM Press, 2011). He previously wrote Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions (Prentice Hall) with Neil Raden, and has contributed chapters on Decision Management to multiple books. He is a frequent contributor to Information Management and writes a regular blog at JT on EDM. You can follow him at @jamet123
Be the first to comment on this post using the section below.