Business Rules Forum: Kevin Chase of ING

I’m squeezing in one last session before flying out: Kevin Chase, SVP of Implementation Services at ING, discussing how to use rules in a multi-client environment, specifically on the issues of reuse and reliability. I’ve done quite a bit of work implementing processes in multi-client environments — such as a mutual funds back-office outsourcing firm — and the different rules for each client can make for some challenges. in most cases, these companies are all governed by the same regulations, but have their own way that they want things done, even if they’re not the ones doing it.

In ING’s case, they’re doing benefits plan administration, such as retirement (401k) plans, for large clients, and have been using rules for about six years. They originally did a pilot project with one client, then rolled it out to all their clients, but didn’t see the benefits that they expected; that caused them to create a center of excellence, and now they’re refining their processes and expanding the use of rules to other areas.

They’re using rules for some complex pension calculations, replacing a previous proprietary system that offered no reuse for adding new clients, and didn’t have the scalability, flexibility and performance that they required to stay competitive. The pension calculator is a key component of pension administration, and calculating pensions (not processing transactions) represented a big part of their costs, which makes it a competitive differentiator. With limited budget and resources, they selected ILOG rules technology to replace their pension calculator, creating a fairly standalone calculator with specific interfaces to other systems. This limited-integrated approach worked well for them, and he recommended that if you have a complex calculator as part of your main business (think underwriting as another example), consider implementing rules to create a standalone or lightly-integrated calculator.

In their first implementation phase, they rewrote 50+ functions from their old calculator in Java, then used the rules engine to call the right function at the right time to create the first version of the new calculator. The calculations matched their old system (phwew!) and they improved their performance and maintainability. They also improved the transparency of the calculations: it was now possible to see how a particular result was reached. The rules were written directly by their business users, although those users are actuaries with heavy math backgrounds, so likely don’t represent the skill level of a typical business user in other industries. They focused on keeping it simple and not overbuilding, and used the IT staff to build tools, not create custom applications. This is a nice echo of Kathy Long’s presentation earlier today, which said to create the rules and let the business users create their own business processes. In fact, ING uses their own people for writing rules, and uses ILOG’s professional services only for strategic advice, but never for writing code.

After the initial implementation, they rolled it out to the remainder of their client base (six more organizations), representing more than 200,000 plan participants. Since they weren’t achieving the benefits that they expected, they went back to analyze where they could improve it:

  • Each new client was still being implemented by separate teams, so there was little standardization and reuse, and some significant maintenance and quality problems. It took them a while to convince management that the problem was the process of creating and maintaining rules, not the rules technology itself; eventually they created a center of excellence that isn’t just a mentoring/training group, but a group of rules experts who actually write and maintain all rules. This allows them to enforce standards, and the use of peer reviews within the CoE improves quality. They grow and shrink this team (around 12-15 people) as the workload requires, and this centralized team handles all clients to provide greater reuse and knowledge transfer.
  • They weren’t keeping up with ILOG product upgrades, mostly because it just wasn’t a priority to them, and were missing out on several major improvements as well as owning a product that was about to go out of maintenance. Since then, they’ve done some upgrades and although they’re not at the current release, they’re getting closer and have eliminated a lot of their custom code since those features are now included in the base product. The newer version also gives them better performance. I see this problem a lot with BPMS implementations as well, especially if a lot of custom code has been written that is specific to a current product version.
  • They had high infrastructure costs since each new client resulted in additional hardware and the associated CPU licensing. They’ve moved to a Linux platform (from SUN Solaris), moved from WebLogic to JBOSS, and created a farm of shared rules servers.
  • Since they reduced the time and expense of building the calculator, they’ve now exposed other areas of pension administration (such as correspondence) that are taking much longer to implement: the pension calculator used to be the bottleneck in rolling out new products, but now other areas were on the critical path. That’s a nice thing for the calculator group, but had them start to recognize the problems in other areas and systems, pushing them to expand their rules capability into areas such as regulatory updates that span clients.

This last point has led to their current state, which is one of expansion and maturity. One major challenge is the cleanliness and integrity of data: data errors can lead to the inability to make calculations (e.g., missing birthdate) or incorrect calculation of benefits. They’re now using rules to check data and identify issues prior to executing the calculation rules, checking the input data for 30+ inconsistencies that could cause a failure in the calculator, and alerting operations staff if there needs to be some sort of manual correction or followup with the client. After the calculations are done, more data cleansing rules check for another 20+ inconsistencies, and might result in holding up final outbound correspondence to the participant until the problem is resolved.

He wrapped up with their key lessons learned:

  • A strong champion at the senior executive level is required, since this is a departure from the usual way of doing things.
  • A center of excellence yields great benefits in terms of quality and reuse.
  • Leverage the vendors’ expertise strategically, not to do the bulk of your implementation; use your own staff or consultants who understand your business to do the tactical work.
  • Use an iterative and phased approach for implementation.
  • Do regular assessments of where you are, and don’t be afraid to admit that mistakes were made and improvements can be made.
  • Keep up with the technology, especially in fast-moving technologies like rules, although it’s not necessary to be right on the leading edge.

Great presentation with lots of practical tips, even if you’re not in the pension administration business.

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