Optimization is a mathematical process for finding the best decision for a given business problem – usually highest profit, lowest cost given a set of constraints. Involve applying an algorithm to data, decision variables, constraints and an objective function. In financial services and insurance optimization is still fairly new (unlike, say, supply chain) but the complex regulatory environment and tradeoffs between risk and reward are ideally suited to it. It also helps with champion/challenger and experimental design. Combining optimization with business rules, and with predictive analytics, is a growth area and I got an update from FICO on how they see these technologies working together.
Business rules and optimization allow you to use rules for flexibility and agility and optimization to get to “best” faster. FICO provides FICO Decision Optimizer as a packaged solution that solves specific banking optimization problems, and FICO Xpress-Mosel as an optimization modeling tool for solving a wide range of industry problems. Both of these products can be combined with FICO Blaze Advisor to leverage business rules management. With these tools, FICO can offer a number of ways to integrate rules and optimization:
- Use Blaze Advisor to deploy an optimized strategy tree created using Decision Optimizer or Xpress optimization results
- Invoke a configured Xpress-Mosel optimization model from a Blaze Advisor decision service (Mosel is the Xpress modeling and programming language)
- Use rules to configure the parameters of the Xpress-Mosel model and then execute it
- Blaze Advisor could provide the input data to Xpress-Mosel Models
- A core model in Xpress-Mosel with Blaze Advisor providing pieces using rules execution t assemble the pieces
- A skeleton model in Xpress-Mosel with Blaze Advisor providing the Xpress-Mosel Code and data using rules to assemble it
For example in debt consolidation use inputs and preferences to find the best payoff loan. The customer has several exiting debts, at different rates of interest, and wants to optimize for payment or pay off period etc. The optimization model does the tradeoff while the rules manage the eligibility of the customer for specific products that might be available for the pay off choice. The optimization engine gets to pick only from the eligible products (the rules for this are already being used elsewhere in most companies so this allows the rules to be reused not repeated for the optimization problem). This can also use predictive models e.g. to predictive price sensitivity (the model is used to calculate a value that is input to the model).
Business rules, optimization and predictive analytics are also being used in FICO Retail Action Manager to optimize marketing spend. Uses the business rules management system to ensure consistent and targeted messages across channels, predictive models to predict who will buy what and an optimization model to pick the optimal offer and channel given the constraints you have.
Retail space planning was another solution that included rules and optimization. Retailer was trying to maximize profitability in the “planograms” or shelf layouts that were being developed. This used predictive analytics to predict how likely customers might be to pick more expensive products if they are positioned correctly, rules for defining the constraints like competing products or store layout consistency as well as contractual requirements from suppliers. Optimization handled the tradeoffs.
I see more and more use cases for business rules, optimization and predictive analytics in combination. The move to considering these complementary technologies as a platform for decisioning is welcome.