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Live from Decision CAMP 2014 – Tobias Vigmostad

Digitalizing Business and Legislative Rules in the Norwegian Immigration Administration

Decision CAMP 2014

TobiasTobias visited us from Norway to share his experience with the immigration administration.

The objective was to allow business and legal to manage their own rules without any technical assistance.  Automation of immigration rules in a prudent way also means that decisions needs to stay in their jurisdiction, rather than being outsourced to external IT services.  Control had to remain in house.

Ease of automation depends on rules complexity, but they also considered how complex facts were.  Banking decisions are simple in comparison to criminal or immigration cases.  This is an interesting aspect that I have not seen being described with such clarity before.  Taxes or pension rules can be fairly complicated, but the facts are usually black and white.  You are eligible or you are not.  Calculations might be complicated but they can systematically determined based on each application.  In the case of immigration or building code, there is a lot of grey area.  This talk is getting my attention because of all the work we are currently doing with permit applications.

Although decisions in the category of “complicated facts” should be handled differently, there might be some cases that could be more easily automated, to start with.

We all had a chuckle in the room when Tobias translated for us how the press reacted to the initiative.  This is a reaction, a fear we have seen at times about automating decisions, replacing human worker.  We all know that the benefits are great for all constituents.  Though reducing the human workload could be seen as a threat, it is also an opportunity to spend their attention to more interesting work.  Oh my, I could go on and on…  Accelerating immigration decision, as well as permits or criminal investigation seems like a good thing to me.

I am digressing though…  UDI’s approach started with decision support, continued with standardizing, to achieve automation.

As I have seen in many projects, business rules elicitation combined with true collaboration within a team allows to improve the quality of the business rules.  The ideation to implementation to testing to testing cycle is enabled by business rules.  It used to take 6 months to get these ideas tested, now it takes barely a week.

As for the Tax Administration project in New Zealand, business people were thrilled with the system, and asked for more.  The system now processes 5-10k cases per month.

Live from Decision CAMP 2014 – Marcia Gottgtroy

An Intelligence Led Approach to Decision Management in Tax Administration

Decision CAMP 2014MarciaWhat is intelligence led?  You need to start by thinking about your objectives, but you don’t always have to get top down.  there is a little of iteration between top-down and bottom-up.

The Inland Tax Revenue administration aimed to achieve straight through processing, including risk management, with a 360-degree view of customers with real-time data.

In order to do that, it was critical to collect as much data as possible.  The platform they put in place allows for the collection, analysis and

  • data layer
  • integration layer – analytical store
  • decision layer – inefrence engine
  • services layer – business intelligence
  • intelligence team

They started with GST (Goods and Services Tax) because of its large volume of daily transactions.  It was a big issue for the business.  New Zealand is great lab for technology because big is not huge.  Usually they process 4,000 GST refunds for small and medium businesses and another 4,000 for larger companies.  They used to review all the refunds but manual process was becoming too much to handle.  One important detail is that management was fully supporting the initiative.

They applied the CRISP methodology.  Core principles:

  • Collaboration
  • Education
  • Continuous Improvement

The pilot group was composed of 3 wise men, who make the final decision, and 6 champions.  Wise men started capturing the first rules.  The champions then developed on the initial work.

The knowledge was captured in declarative form.  The expert-based risk assessment model took 4 months.  It was captured in rules, including rationale and clarifications.

The pilot was a success.  95% of GST refunds were automatically released.  They reduced by more than 2 days the GST processing time.  Customers got GST refunds faster, over the nightly processing and release of funds.

They reduced the costs for Inland Revenue and for customers.

The feedback after running the pilot for 3 years.  The system is now vital.  They do not want to go back to the way they used to do it.

One fraud case they investigated and found within a month.  The scam had been going for months before.  By printing the whole network, they could visualize the relationships and solve the case.  Check the picture in the slides!

After the first results, they started receiving tons of requests to add to the scope.  Victims of their own success!  They do have to make sure that they control the expansion of the system, and stay focused.  The team has grown a bit to 15 people, but it is stable now.

Tax Day Dream… What if…

Tax Day

Yesterday was Tax Day here in the US…  I hope that this painful exercise is now behind all of us.  If not, well, I hope you filed at least an extension!

Going through the numbers, it made me think about the number of decisions involved in this annual practice.  Obviously there are lots of business rules needed to make the calculations that may or may not lead to a well-appreciated refund.  Many tax services in the world, like IRS in the US, already use Business Rules Management Systems (BRMS) to validate returns, flag potential errors or detect fraud.  Tax systems must be super flexible to accommodate the legislations that keep changing year over year: income may be taxed differently, eligibility criteria for deductions may be more or less lenient, etc.  validation rules on top could work in conjunction with predictive scores to red-flag the fraudulent returns.

My mind kept wandering further…

Simulation tools come with Tax software.  You can predict the impact of adding a dependent, buying a house or taking a leave of absence.  This feels right but not nearly enough…  What if we could run much more sophisticated what-if scenarios?  Understanding the tax formula is typically beyond people’s abilities here (which explains the success of companies like Intuit).  As a result, many people often make sub-optimal investment decisions as they can’t always call their CPA for advice.  Simulation tools in a way that is understandable and usable by common people would be pretty cool.

But if I was in the IRS shoes…

How sophisticated are their simulation models?  Granted the Administration could estimate how many American taxpayers fall into this or that bucket regarding a given criteria.  Based on that you could easily figure out how many $$ would come in the Government’s pocket with a policy change and the $$ difference on the taxpayer’s budget.  If they are sophisticated they could propagate the calculations further to estimate the impact on other potential eligible deductions.  This would allow the Administration to better anticipate the expected revenue.

Tax calculations contribute to the bottom-line number of course but this is not the only criteria that dictates it.  Other factors would definitely influence it.  Take for example unemployment.  Knowing the unemployment rate is soaring, you could apply it uniformly to the various brackets or apply predictions for each one to increase the accuracy of your model.  Some industries, States or demographics may be more severely impacted than others.  Other factors like high rate of foreclosures, delayed retirement dates or increased charity donations due to the many earthquakes that affected the world this year could be modeled into the simulation to predict the expected overall income tax revenue.  By adding to the list of external factors that could influence the bottom-line result, the model precision could be improved significantly.

Granted you do need the insight of experts, in that case a gold-star team of economists but the end goal is worth it.  Can you envision the power of a prediction tool that could estimate the effect of direct or indirect legislations on the income-tax-based revenue for the Government and on the taxpayer’s side too?  This year, as a result of the recession, the Government’s revenue from income taxes decreased from over 20% down to 15%.  Debates over whether we need to increase or decrease tax rates would become less philosophical and more objective with tangible datapoints such as this.

I am not claiming that the economists that assisted the Government over the year did not use decision modeling techniques such as these of course.  My point was mostly to illustrate the practical steps that any business could make to gain objective insight.  Decision Modeling is not that complicated and the return can be enormous.  It is critical though that you involve the right experts that can gauge the existence and influence of each one of those factors.  Your models can be refined over time.

Simulation in itself is better than nothing, but the true value is in your understanding of the components of your decisions.  Do not second guess the indirect forces that impact your business.  Stop shooting darts in the dark.  Take charge.  Be proactive, be informed.

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