— Posted by Carole-Ann Thank you for attending the Evolution of BRMS session at Business Rules Forum. It was great meeting some of you there in person. For those of you that could not make it, I wanted to give…
— Posted by Carole-Ann
Thank you for attending the Evolution of BRMS session at Business Rules Forum. It was great meeting some of you there in person.
For those of you that could not make it, I wanted to give you the
gist of what I presented. A previous presentation covered the evolution of
the business rules technology focusing first on the drivers that forced
the market to shift its focus from Business Rules Engines (BRE) to
Business Rules Management Systems (BRMS). In this second presentation, I explore the evolution that
is taking place as we speak, going from BRMS to Decision Management
In a nutshell, the main
ideas are summarized below.
Increase Confidence in Strategy Performance
- Once business rules are verified and validated, they are typically promoted to Production at the right time — How do you check today that those business rules will allow you to achieve your business goals? How long does it take to realize whether or not the rate of automatic decisions is acceptable?
- The next step in decision improvement is to better compare champion / challenger strategies — How do you accurately predict the relative business value of each strategy? How would external elements such as interest rate change or cost of resources influence the ability to repay or likelihood to accept an offer?
- The last step (in this scenario; another methodology is to start actually here) would be to generate an optimized strategy out of this decision model and historical data — How does your current strategy map compared to an optimal assignment? Can you infer an optimal strategy that can be applied consistently to your incoming transactions?
Address more Sophisticated Decisions
- Business Rules come typically from experts, regulations and/or legacy code — Are they as precise / efficient as those of your competitors? Would statistics on your historical data help you better identify the good versus bad risk applicants? What if you could predict which customers are likely to accept which offers? How competitive would you be if you could accurately price each transaction according to your estimated related expenses?
- Some decisions may appear sub-optimal and could be improved — Do you offer your customers the best possible deal given your product constraints and your business objectives? How efficient is your usage of resources given your delivery schedules?
- Decisions made in silos may lead to contradictions, overlaps, inefficiencies — Do you proactively market to customers that have a history of Fraud or Delinquency? How valuable are the customers you try to retain?