W6 Case Study question from Pages 504-505 and 537-539 (Assignment File attached)

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End-of-Chapter Application Case

Tax Collections Optimization for New York State

Introduction

Tax collection in the State of New York is under the mandate of the New York State Department of Taxation and Finance’s Collections and Civil Enforcement Division (CCED). Between 1995 and 2005, CCED changed and improved on its operations in order to make tax collection more efficient. Even though the division’s staff strength decreased from over 1,000 employees in 1995 to about 700 employees, its tax collection revenue increased from $500 million to over $1 billion within the same period as a result of the improved systems and procedures they used.

Presentation of Problem

The State of New York found it a challenge to reverse its growing budget deficit, partly due to the unfavorable economic conditions prior to 2009. A key part of the state’s budget is revenue from tax collection, which forms about 40 percent of their yearly revenue. Tax collection mechanism was therefore seen as one key area that would help decrease the state’s budget deficit if improved. The goal was to optimize tax collection in a very efficient way. The existing rigid and manual rules took too long to implement and also required too many personnel and resources to be used. This was not going to be feasible any longer because the resources allocated to the CCED for tax collection were in line to be reduced. This meant the tax collection division had to find ways of doing more with fewer resources.

Methodology/Solution

Out of all the improvements CCED made to their work process between 1995 and 2005, one area that remained unchanged was the process of collection of delinquent taxes. The existing method for tax collection employed a linear approach to identify, initiate, and collect delinquent taxes. This approach emphasized what should be done, rather than what could be done, by tax collection officers. A “one-size-fits-all” procedure for data collection was used within the constraints of allowable laws. However, the challenge of a complex legal tax system, and the less than optimal results produced by their existing scoring system, made the approach deficient. When 70 percent of delinquent cases relate to individuals and 30 percent relate to business, it is difficult to operate at an optimal level by taking on delinquent cases based on whether it is allowable or not. Better processes that would allow smarter decisions about which delinquent cases to pursue had to be developed within a constrained Markov Decision Process (MDP) framework.

Analytics and optimization processes were coupled with a Constrained Reinforcement Learning (C-RL) method. This method helped develop rules for tax collection based on taxpayer characteristics. That is, they determined that the past behavior of a taxpayer was a major predictor of a taxpayer’s future behavior, and this discovery was leveraged by the method used. Basically, data analytics and optimization process were performed based on the following inputs: a list of business rules for collecting taxes, the state of the tax collection process, and resources available. These inputs produced rules for allocating actions to be taken in each tax delinquency situation.

Results/Benefits

The new system, implemented in 2009, enabled the tax agency to only collect delinquent tax when needed as opposed to when allowed within the constraints of the law. The year-to-year increase in revenue between 2007 and 2010 was 8.22 percent ($83 million). As a result of more efficient tax collection rules, fewer personnel were needed both at their contact center and on the field. The average age of cases, even with fewer employees, dropped by 9.3 percent; however, the amount of dollars collected per field agent increased by about 15 percent. Overall, there was a 7 percent increase in revenue from 2009 to 2010. As a result, more revenue was generated to support state programs.

Questions for the End-of-Chapter Application Case

1. What is the key difference between the former tax collection system and the new system?

2. List at least three benefits that were derived from implementing the new system.

3. In what ways do analytics and optimization support the generation of an efficient tax collection system?

4. Why was tax collection a target for decreasing the budget deficit in the State of New York?

End-of-Chapter Application Case

Solving Crimes by Sharing Digital Forensic Knowledge

Digital forensics has become an indispensable tool for law enforcement. This science is not only applied to cases of crime committed with or against digital assets, but is used in many physical crimes to gather evidence of intent or proof of prior relationships. The volume of digital devices that might be explored by a forensic analysis, however, is staggering, including anything from a home computer to a videogame console, to an engine module from a getaway vehicle. New hardware, software, and applications are being released into public use daily, and analysts must create new methods to deal with each of them.

Many law enforcement agencies have widely varying capabilities to do forensics, sometimes enlisting the aid of other agencies or outside consultants to perform analyses. As new techniques are developed, internally tested, and ultimately scrutinized by the legal system, new forensic hypotheses are born and proven. When the same techniques are applied to other cases, the new proceeding is strengthened by the precedent of a prior case. Acceptance of a methodology in multiple proceedings makes it more acceptable for future cases.

Unfortunately, new forensic discoveries are rarely formally shared—sometimes even among analysts within the same agency. Briefings may be given to other analysts within the same agency, although caseloads often dictate immediately moving on to the next case. Even less is shared between different agencies, or even between different offices of some federal law enforcement communities. The result of this lack of sharing is duplication of significant effort to re-discover the same or similar approaches to prior cases and a failure to take consistent advantage of precedent rulings that may strengthen the admission of a certain process.

The Center for Telecommunications and Network Security (CTANS), a center of excellence that includes faculty from Oklahoma State University’s Management Science and Information Systems Department, has developed, hosted, and is continuously evolving Web-based software to support law enforcement digital forensics investigators (LEDFI) via access to forensics resources and communication channels for the past 6 years. The cornerstone of this initiative has been the National Repository of Digital Forensics Information (NRDFI), a collaborative effort with the Defense Cyber Crime Center (DC3), which has evolved into the Digital Forensics Investigator Link (DFILink) over the past 2 years.

Solution

The development of the NRDFI was guided by the theory of the egocentric group and how these groups share knowledge and resources among one another in a community of practice (Jarvenpaa & Majchrzak, 2005). Within an egocentric community of practice, experts are identified through interaction, knowledge remains primarily tacit, and informal communication mechanisms are used to transfer this knowledge from one participant to the other. The informality of knowledge transfer in this context can lead to local pockets of expertise as well as redundancy of effort across the broader community as a whole. For example, a digital forensics (DF) investigator in Washington, DC, may spend 6 hours to develop a process to extract data hidden in slack space in the sectors of a hard drive. The process may be shared among his local colleagues, but other DF professionals in other cities and regions will have to develop the process on their own.