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4.1 Subject Matter Category 3 Tax Collection, Fraud Prevention
and Detection
RFP Section 5, page 9
All contractors submitting a proposal MUST identify one or more subject areas from Appendix A for which they will contractually commit to provide data analytics tools and services.
For each Subject Matter Category for which the responder seeks to be considered, provide
the following:
A. A description of relevant experience specific to this subject matter category;
RSI Response:
4.1.A Relevant
Experience
In 1996, Revenue Solutions, Inc. (RSI) was founded as a consulting and products company with a sole focus to work with tax administration agencies. In late 1998, RSI recognized the demand for a transferable, easy to use product that could attack the “tax gap.” Our approach was based on similar concepts used by private sector marketing databases – “understand your customer and target your products.” RSI also realized that tax agencies were faced with reductions in personnel and many traditional “external tape matching” ideas were simply too time consuming and inefficient to be effective. With
the advent of low cost, high speed servers and sophisticated database technology, coupled with the experience of RSI and its philosophy to build a configurable solution where internal and external data sources could be easily aggregated into “standardized, centralized taxpayer portfolios” for subsequent analysis, including advanced analytics, by revenue agency end users, product design and development began. Today, RSI continues this
multi-year, multi-million dollar investment in the evolution of the Revenue Premier product at our Solution Center in Roseville, California.
RSI has been, and continues to be, a trusted partner and recognized leader in the government information systems industry offering modernized Compliance Management (i.e., Collections, Fraud, Discovery, Audit, and Business Intelligence) and Revenue Administration Management solutions (i.e., Integrated Tax Systems and Unemployment Insurance Tax Systems) and implementation services.
Figure 4-1 below identifies the states which RSI has partnered with since inception.
RSI’s team has successfully implemented similar collection improvement and tax fraud programs in other state revenue agencies, resulting in tens of millions of dollars in collected and saved taxes.
Figure 4-1: RSI Engagement Map
Within our Consulting Division, RSI is organized to match the two main functions of a tax agency – compliance and tax processing. Within each major discipline, RSI has concentrated practice lines. Each practice has dedicated consultants who are experts in this field. Each practice line also offers software solutions specific to that practice line. The solution areas of our Compliance Management practice are described below.
Compliance Management
Compliance Management provides a wide range of support for tax enforcement from non-registration discovery and non-filing to fraud, audit, collections and taxpayer education. At RSI, Compliance Management brings the next generation enterprise approach to tax compliance. Based on an integrated technical foundation, Compliance Management leverages extensive data as well as sophisticated analytics and predictive models to determine the taxpayer interactions that will best enhance customer service and increase compliance. Our services and solutions improve efficiency, promote cost-effectiveness, and maximize compliance in many areas of tax agency operations while raising levels of customer service. The goal of Compliance Management is to help our tax agency clients apply the right compliance action to the right taxpayer at the right time, through the intelligent use of information. The Compliance Management area comprises four solution areas as described below.
Data Warehousing and Business Intelligence – this area is focused on projects where
RSI is implementing our Revenue Premier product, to support the pursuit and identification of non-filer, non-registrant, fraudulent, and under-reporting individuals and businesses to attack the tax gap – through the use of data. Furthermore, the underlying compliance warehouse component provides a foundation for various tools and views to support fraud detection, audit selection, and collections (e.g., scoring, skip tracking, bank location, asset identification etc.). This area also supports the extension of the portfolio-based warehouse
for use in other state agencies, including Unemployment Insurance and Medicaid. It also includes development of advanced database reporting (business intelligence) for executive agency management, tax policy, and tax research.
Audit Management – this area is focused on projects where RSI is implementing our
software products that support the desk audit, fraud and field audit groups in managing and conducting audits, including auditor productivity software, audit workpaper packages, and data sampling software. Services include business process re-engineering and software implementation services based on RSI’s deep knowledge in desk and field audit.
Decision Analytics – this area is focused on the application of statistical techniques
against taxpayer-based data which, in turn, derive probability models and allow agencies to make more educated decisions for fraud detection, audit selection, collection approaches, and taxpayer service. It includes use and deployment of statistical data mining packages, and implementation of RSI’s TaxAnalytics Framework, designed for embedding advanced analytics into an agency’s case creation and resource allocation decisions.
Accounts Receivable Management – this area is focused on projects where RSI is
implementing our software products for the management and more effective recovery of tax receivables. It provides consulting services to implement business process re-engineering solutions and tools, such as risk-based strategies, which apply RSI’s extensive collection expertise to score, treat and manage tax debt collection, including agency and external (i.e., outsourced) collection agents.
4.1.A.1.
Unique Qualifications of Revenue Solutions Inc
.
RSI is one of few consulting firms dedicated solely to the tax and revenue industry. Tax is our core business. RSI has completed over 250 Tax and Revenue projects since its founding in 1996. Pertinent to the evaluation of this proposal is that RSI has implemented more data -driven compliance solutions for state tax agencies than any other vendor. Today, RSI has eleven (11) licensed Revenue Premier customers. The Minnesota project for Tax Collection, Fraud Prevention, and Detection is an exact fit for RSI’s Revenue Premier product. RSI has worked with several clients on these same compliance goals. Described below are the primary reasons why RSI is uniquely suited to provide services in this subject matter category.
1. RSI’s extensive prior experience and expertise with tax agencies, compliance operations, data warehouse technology, and the application of advanced analytics. RSI’s team has successfully implemented similar collection improvement and tax fraud programs in several other state revenue agencies, resulting in tens of millions of dollars in revenue improvements. There is low risk to the RSI approach and no time wasted on programs that offer low return.
2. RSI understands tax data, IRS data, and the variety of external data sources that are or can be made available to the State of Minnesota. RSI understands how to apply that data for the purposes of leveraging analytics to improve compliance management.
3. RSI’s commitment to have senior level staff involved for all phases of a project – staff that have the full backing of our corporate resources to make this partnership with Minnesota successful.
4. Quality Assurance and product oversight led by RSI’s Consulting Division and Compliance Practice Line to ensure the product and our best practices are being implemented to their fullest potential.
5. Our approach to customer partnering, training and knowledge transfer, which will make Minnesota capable of quickly assuming responsibility of system operations and thereby reduce the cost of ownership beyond the contract period.
6. Our company’s commitment and employees' passion for the business of taxation and the application of advanced analytics – “this is what we do and we do it well.” RSI is a frequent speaker at industry conferences and a leader in innovation to improve tax administration and compliance management.
7. Our “best value” proposition that maximizes product and services features with an achievable return on investment.
8. RSI has implemented various types of data analytics solutions in many states. Some of this work is presented in this section as well as in our response to Section 5, Tab 4, item E. In addition, our staff has worked in this field for up to 15 years and bring that experience to this proposal and to Minnesota. This level of experience means that RSI understands how to make analytics work for departments of revenue. It’s not just about complex math in optimization algorithms. It’s also about understanding the underlying business problems and improving business processes, integrating new systems and processes with the existing systems infrastructure, persuading and supporting staff to adopt the new tools, and being able to provide measureable results. RSI has demonstrated these skills at several clients.
In preparing our proposal, RSI has taken advantage of our collective corporate experience and the expertise gained on very similar tax agency collection enhancement and fraud identification and discovery projects. Table 4-1 below highlights RSI’s similar experience across several other states.
Table 4-1: RSI Data Analytics Experience
*Under evaluation or in planning stages **Tax Preparer fraud solution
Note that Nevada does not have a Personal Income Tax
RSI has implemented more compliance solutions for state tax agencies than any other vendor. The Tax Collection, Fraud Prevention and Detection subject matter area is an exact fit for RSI’s products and services, we understand the objectives, scope, duration and complexities of such projects.
All aspects of compliance management work that requires an agency to identify, prioritize and work through cases can benefit from the application of analytics. Not every potential case is the same. Some cases resolve easily, others require substantial time and effort to resolve. Some should not have been selected and result in “false positives” and an inconvenience to compliant taxpayers. These facts beg for differentiated treatments, if only it were possible to tell the difference between prospective cases. That is the role of advanced analytics and predictive models. Predictive models can be developed for refund fraud, collection, audit selection, non-filer false positive identification, abatements, criminal investigations, and taxpayer education purposes, amongst others. The scores derived from those models can be used to select cases, assign cases to differentiated treatments based on probabilities, and to prioritize cases within those treatments. Many of those treatments will be automated, thus
relieving the burden on staff members from working cases that do not need to be reviewed manually. This will free up that time to be applied to other cases more demanding of direct attention, within differentiated treatments.
RSI has developed a refined approach to advanced analytics, predictive modeling of taxpayer behavior, and model deployment over several projects and the past decade of ongoing innovation and solution investment. RSI has subsequently abstracted that methodology into an analytical workbench, called TaxAnalytics Framework, that automates many of the analytical processes to improve quality, lower delivery risk, and to make these capabilities more directly and efficiently accessible to our clients. This automation includes processes for organizing analytical data, maintaining updated performance data, developing and deploying predictive models, integrating with existing systems, and reporting on case performance and decision effectiveness. What requires months or years of custom development and complex implementation services by competitors, RSI has reduced considerably. This offering not only enables the creation of models, it also enables those models to be re-developed (not just ‘tweaked’) over time on an as-required basis. This ensures that all deployed models remain current. It also ensures that new case performance is encapsulated in those new models as it becomes available, making the analytics a more dynamic and adaptive component of the business solution. So, for example, if new fraud schemes evolve over time, the characteristics of taxpayers participating in such schemes will be incorporated into the models.
The following sections describe some of RSI’s experience implementing analytics-based solutions in different areas of the tax compliance process.
4.1.A.2.
Tax Refund Fraud Detection Experience
4.1.A.2.a Refund Fraud Modeling Example
Refund fraud prevention requires technology, data, compliance knowledge and integration services to timely and effectively distinguish between valid and fraudulent refunds within the returns processing flow. With assistance from RSI, the Massachusetts Department of Revenue uses Revenue Premier to create an effective solution to this complex problem. By instituting a set of “pre-validation” rules, whereby
confirmed taxpayer identities are assembled in advance and checked during returns processing, valid refund requests from legitimate taxpayers are able to move quickly through the system. Refunds that are not pre-validated are then subject to a more thorough risk assessment and review including possible routing for manual actions.
The scope of this work includes aggregation of data sources (e.g., W-2, DMV, employer withholding account data, Federal data, etc.) to create a pre-validated taxpayer identity list. Verification of
SSN/Name (i.e., identity), Employer, Employment, Withholding credit, and Employer Withholding registration and payments is streamlined and automated, allowing MA DOR resources to focus on the small subset of the population who cannot be confirmed and are likely are non-compliant or fraudulent.
This approach allows the majority of returns to flow through the system as they would normally, thereby reducing risk to existing processes and avoiding any delay in refund issuance to confirmed and compliant taxpayers. Of the remaining returns, most are confirmed after a more
This solution maintains the highest possible level of customer service, uses limited resources, can be maintained and adapted going forward and provides the appropriate balance between issuing timely refunds to legitimate taxpayers and preventing fraudulent refunds.
thorough automated evaluation process that uses analytical approaches. The returns that remain are automatically analyzed by the system, given a “proposed decision” and queued for manual review. A tax fraud examiner then makes the final determination to either release the refund or issue a notice to the taxpayer requesting further information to confirm their identity and qualification for refund and/or certain refundable credits.
The project includes legacy system integration components, use of advanced data mining analytical techniques to develop the pre-validated list and fraud patterns, configurable processes to score potential fraudulent refunds, a queuing process for refunds held for manual review, as well as detailed reporting components that are integral to the success of this project and measurement of project benefits.
The process performs an automated review of the subset of incoming returns that cannot be confirmed as pre-validated, as those that are potentially fraudulent. The process uses some simple points of integration with the current Integrated Tax System (ITS) where suspicious returns are placed into holding problem sets and cleared returns are released for normal processing.
For the first filing season in use, the system successfully prevented $15 million in suspected fraudulent refunds from being issued, with limited complaints from legitimate taxpayers who were asked to provide additional information. The Massachusetts DOR has seen benefits of approximately $60 million to date, and the solution continues to be operated and adapted by the DOR.
4.1.A.2.b Tax Preparer Fraud Model Example
RSI has a specific configuration of the Revenue Premier product for detecting Tax Preparer Fraud, which has been employed fully at the South Carolina DOR, and conducted more partially in other states. The preparer fraud model is an outlier-based approach, where preparers are scored based upon their deviation from a number of “normative” measures. Thus, if a preparer appears very different (in a manner that indicates a greater likelihood of fraud) from other preparers that serve a similar demographic segment, then that preparer gets a higher score.
The approach involves developing preparer profiles and scoring preparers based upon their deviation from the “typical” or “normal” preparer. A profile for a preparer represents a portfolio of metrics which are derived from the tax returns that are filed by the preparer. Based upon these profiles, the preparer score reflects how a particular preparer is compared to others. One caveat considered when applying this technique is that preparer characteristics developed from the filed returns might differ due to explainable factors. For example, preparer characteristics may vary depending on the client segment they serve. These are discernable and legitimate reasons. Therefore, in the process of constructing a preparer profile and measuring its deviation from the “norm,” RSI gives careful consideration to neutralizing the effect of any explainable factors which may skew one or more metrics for a given preparer. The overall process to develop and continuously refine the tax preparer fraud model involves a number of configured steps, depicted in Figure 4-2, below.
Figure 4-2: Applying RSI’s Taxpayer Portfolios and Modeling Framework to Generate Tax Preparer Fraud Scores
The first step in the process is to develop a portfolio of “risk indicators” at the individual tax return level. Examples of “risk indicators” are: Miscellaneous Deductions over AGI, Total Itemized Deductions over AGI, Medical Deductions over AGI, High Schedule C losses, etc. Next, these factors are aggregated at the tax preparer level, to create preparer level metrics. As indicated previously, the goal is to enable the identification of preparers that have, for example, an unusually high ratio of Miscellaneous Deductions over AGI across all of their prepared returns.
Two additional steps are performed to create the preparer level metrics. First, to achieve stable statistics, only preparers with more than a minimum threshold of returns are considered (this threshold can be determined by Minnesota, based on requirements). Second, the normalized preparer level metrics that remove the effects of explainable causes of deviations must be created.
This segment-adjusted and standardized set of metrics is then averaged at a preparer level to create preparer level metrics. For each preparer, this creates a numeric score along each risk indicator dimension that reflects the deviation of the preparer from the norm for those dimensions.
These models are adjusted throughout the filing season as more returns are filed, enabling the refinement of the models and rapid identification and incorporation of new preparer fraud trends. Preparers (and their associated returns) shown to be outliers are then reviewed by auditors.
4.1.A.2.c Ongoing Fraud Detection Experience
RSI has been continuously working on the refund fraud problem and the application of advanced analytics to it for five years, and is working daily with several other state clients (including North Carolina, Maine, Massachusetts, and Vermont) to identify and stop new schemes. Years of experience across several states has been incorporated into common fraud detection queries that have been found to be effective on prior implementations. Those
configured selections are readily adapted for a new Revenue Premier customer, and serve as an effective base from which to start.
The existing library of selections includes common rules for considering an identity “pre-validated” based on prior filing history and verification of identity, address, TIN, employer, and withholding credits prior to that taxpayer filing a return for the current tax year.
Selections also exist for common situations of identifiable fraud, including claiming of wages and withholding credits from a company that was not a legitimate employer, not actually registered for and paying Withholding tax that year, or where the taxpayer was not ever employed.
As part of the initial implementation and over time, Minnesota would adapt these known fraud pattern selections, add new ones, and apply these “known issues” to new returns so that the right refunds are suspended for further evaluation, based on more data-driven analytical techniques.
In addition to fraud scheme-specific pattern identification techniques, predictive models can be developed to identify likely refund fraud at a general level. These models identify some taxpayers as being high-risk fraud cases, which none of the scheme-specific techniques would select. Using these models is one additional way of identifying refund fraud cases that may be the vanguard of new fraud schemes. Cases that are identified by these predictive models but not picked out by the existing techniques or business rules may be included in regular analyses of fraud cases to quickly identify new fraud schemes, and to develop either further scheme-specific predictive models or specific business rules to address a particular scheme. In addition, models and rules for newly identified schemes can be applied to historical returns to identify cases that may be earlier instances of this new type of fraud.
4.1.A.3.
Audit Selection Experience
Historically, lead selection has been at the auditor’s discretion and their application of industry knowledge to their “known” universe of potential candidates. Recently, that has begun to change. Audit departments now often routinely audit certain cases or types of cases, on a regular basis and use centralized selection techniques. However, beyond these mandated audits, many agencies still allow their auditors to select their own leads. These selections are frequently based on the auditor’s geography, prior history, or industry preferences. These methods can bias an agency’s overall pool of taxpayers that are ultimately subjected to an audit.
Audit departments are now seeking to optimize the productivity and effectiveness of their staff through the use of more data. Specifically, states are starting to use advanced analytical techniques that use audit history to identify profiles of audits that typically result in effective audits and then bias future selection towards similar taxpayer filing patterns. Scoring the taxpayer base to identify taxpayer assessment probabilities enables cases to be prioritized in terms of their probability of yielding high hourly assessment amounts – maximizing the impact of the audit department’s discretionary audit hours.
In addition to determining the probability of yielding a positive assessment, cases should be scored on other factors as well. For example, most tax audit departments comprise auditors with varying skill levels. Some are very experienced while others are relatively new and essentially in training. Therefore, audit complexity can be forecast so that leads can be matched with the auditor’s skill level. Indeed, the process of assigning leads to auditors should not only be of high probability leads, but should also reflect the geographic distribution of auditors and leads, the expected complexity of the audit, and other factors.
Once the models are developed and taxpayers are scored, those scores are used to prioritize audit leads. This can occur in two ways. A specific prioritization (a Selection View or query) can be developed to prioritize all taxpayers. This Selection View would include, besides the scores and associated details, all other factors that the Minnesota Department of Revenue deems to be important in terms of selecting audit leads. For example, these associated details could include: the NAICS code, the previous audit history of each taxpayer, the current contact information for the selected tax type(s), the DOR regional office to which the taxpayer “belongs,” etc. This type of Selection View provides the overall perspective on all taxpayers and allows the very best cases, i.e. the most productive in terms of hourly yield, to be identified.
An additional approach is to provide the scores as a resource which examiners and audit management can include in existing case selection queries. Typically, selections of taxpayers that meet certain audit criteria cannot prioritize the leads selected in terms of expected performance or audit outcome. By including “predicted yield” scores with the selection, the DOR is enabled to prioritize their selections, and possibly drop certain leads when productivity levels are forecast to fall below a pre-defined level.
Whichever way audit selection scores are used, their result is to increase the productivity of the audit department. This is achieved by selecting leads which yield high hourly assessments, and (inversely) by reducing the number of no-change, or very limited adjustment cases. These benefits are direct outcomes of the model development process and the proper application of analytics to this very specific business problem. When developing models, some cases are defined as “good” and the others are defined as being “bad.” Good cases will be those audits that yielded at or above a given hourly assessment per auditor hour. For example, cases yielding $1,000 per hour or more could be deemed to be good. A corollary of this is that no change audits are, by definition, flagged as being bad cases. As a result, when cases are scored, no change audits will tend to be amongst those with lower scores. Clearly, cases with high scores and, therefore, high probabilities of yielding $1,000 per auditor hour or more, cannot be no-change audits. Not working no-change audits will increase auditor productivity. The Connecticut Department of Revenue Services has implemented such a system. All Sales and Use taxpayers were scored and prioritized. Of those, some were selected for audit. In addition, about 1,400 existing audits were dropped because of their low probability of being high yield audits. Within the first year following implementation, the audit bureau realized an increase in total Sales and Use Tax assessments of 26%.
4.1.A.4. Probability-Based
Collections
Collection actions range from noticing, phone calls and correspondence to enforced collection actions such as the application of levies to enforce payment or liens to protect the long-term interests of the State in the monies owed. In the absence of predictive scoring, prioritization of collection cases is usually based on the case balance or age. No distinction is typically made between cases that may pay quickly, or cases that will not respond to simple collection actions. In many states, the aging process provides months, or even years, before more effective actions are applied. This enables other creditors to collect their debts first, leaving nothing for the State.
Collection departments are now seeking to optimize the productivity and effectiveness of their staff. Scoring collection cases enables them to be segmented and prioritized by probability of repayment, and then assigned to different collection treatment streams, each designed specifically for given levels of risk. These treatment streams recognize that most low-risk cases will respond to initial notices, not requiring calls from collectors or other types of
correspondence. In fact, it is a waste of time for collectors to work these cases because most of these taxpayers will pay of their own accord. High-risk cases, on the other hand, will not respond to simple collection actions and should, therefore, be accelerated into enforced collections as soon as possible, while still being provided due-process. Consequently, front-end collectors should focus on medium-risk cases – these are cases they can have an impact on by encouraging the taxpayers to pay earlier than they otherwise would. Work queues can be prioritized by ‘expected yield’ – derived by combining the case balance with the probability of repayment. Its effect is to float the most collectible dollars to the top of the work list. In fact, the entire benefit of a risk-based approach to collections is that ‘at risk’ dollars are worked sooner, before other creditors reach them.
RSI has implemented probability-based collections in several states. These projects typically involve the creation of predictive models, treatment strategies, and assignment rules for assigning collection cases to treatments. In addition, they include integration with data sources for scoring purpose and with the collection system for the delivery of scores, yields and assignments. Finally, performance tracking and reporting is necessary for reporting on the effectiveness of the assignments made.
RSI has implemented probability-based collections at the Massachusetts Department of Revenue. A suite of nine predictive models was developed based on historical collections case data, filing data, and various external data sources. The models were designed to be used to score all cases entering collections, and to assess repayment risk. Business rules were then developed to assign collection cases to different treatments based on predicted repayment risk. Decision effectiveness reports were implemented to show the effectiveness of the collection process on cases with different probability levels.
The probability-based collection solution was developed and implemented within RSI’s Revenue Premier platform, and integrated with the department’s legacy tax processing system.
Since implementation this solution has delivered benefits of approximately $50M
annually in increased collection revenues. Originally implemented in 2007, the predictive
models have been maintained on a regular basis, most recently in 2010.
4.1.A.5.
Analytics and Differentiated Treatment of Non-Filers
States have traditionally monitored the filing of business trust taxes from registered trust tax accounts, due to the sensitive nature and responsibility that businesses have to collect and remit the tax. If a business is in financial trouble, one of the first places that they turn to is the payment of trust taxes. If bills are coming in from suppliers, utilities, etc., and a business is short of cash, it is often the filing and payment of sales and/or withholding taxes that takes a lower priority. States are aware of this and try to be as proactive as possible to identify businesses that have not filed and paid their trust taxes for the expected period, based on their filing frequency as determined by pre-registration criteria. If a return is missing, a notification to the taxpayer is issued indicating that a return is due and that it should be filed and paid.
The fundamental problem states face when pursuing non-filers relates primarily to the complexities associated with trust tax accounting, tax system limitations, and the volume of taxpayers and periods associated with trust taxes. The approach typically used to identify non-filers has fundamental limitations, in that it will identify taxpayers who are truly delinquent and owe the filing and payment of tax for the period in question (i.e., “non compliant taxpayer” non-filer cases), as well as taxpayers who are compliant but appear to have missing returns due to a closed business, education issues, tax system limitations, etc. (i.e., “generally compliant taxpayer” non-filer cases).
The business problem states face when addressing the business trust tax non-filer question comes down to the use of resources and the difference between identifying and pursuing the business that is truly on the brink of going bankrupt and is using trust taxes to keep them afloat, versus the business that is not fundamentally non-compliant and delinquent in the payment trust taxes collected, but appears so to a rudimentary identification program. The resultant selection of non-filer cases requires a resource-intensive process to correct accounts and deal with the fallout that results when high volume noticing programs equally target both taxpayers that have actual outstanding tax liabilities and those that do not.
To enable a differentiated approach to addressing non-filers, RSI has developed predictive models for scoring filers in terms of their probability of being compliant (i.e., valid non-filers with outstanding tax liabilities), versus being generally compliant cases mistakenly identified as non-filers or where the non-filed returns - if filed - would not represent actual taxes owed (i.e., they instead represent $0 sales months or a closed business). Based on those models, and business rules for assigning cases to treatments, taxpayers can be confidently classified as compliant or non-compliant. This classification allows an agency to apply different treatments to the non-filer cases based on the outcome that is predicted by the model. At least three distinct assignments can be made, as follows:
Non-Compliant Cases: Group 1 – Accelerate through the existing non-filer work
process and get into the hands of Collectors as soon as possible. These are true positive cases with high-probability of large taxes due for the non-filed periods.
Non-Compliant Cases: Group 2 – Keep the status quo treatment that is done
today. These are true positive cases, but of far lesser risk and exposure, typically because the outstanding likely liabilities are small and the likelihood of eventual recovery through traditional approaches is strong.
Compliant Cases: – Handle via a separate notice campaign that does not include
non-filer assessment creation. Alternatively, they can be flagged for manual review at a later time, as resources are available. These are cases with a very low probability of outstanding taxes due for the non-filed periods, which are instead much more likely to be non-filed periods attributed to no sales, a missing return, a misapplied payment, or a closed business.
The South Carolina Department of Revenue management requested that RSI study the issue, and determine if there are opportunities to provide more robust analytics to the non-filer identification and treatment assignment process. The goal is to continue to target true non-compliant non-filer cases, but reduce the number of non-compliant taxpayers identified in the process, and thus reduce the amount of resources it takes to resolve compliant non-filer cases.
B. A description of the data analytics services you are proposing to offer in this subject matter category (be specific, noting that agencies may seek to contract for a subset of the offered services).
RSI Response:
4.1.B
Data Analytics Services
4.1.B.1.
Introduction
RSI is proposing several data analytic services as well as the optional implementation of an analytical software foundation, the TaxAnalytics Framework. The software foundation leverages SAS software and, through a series of specialized extension nodes maintained by RSI, provides functionality designed explicitly for the support of applying analytics to the compliance management activities within tax agencies.
Thedataanalytics services proposed include:
x preparation of taxpayer-focused data into analytical databases that can support a wide range of predictive modeling and ad hoc data mining activity;
x automated development of predictive models for compliance purposes such as probability-based collections, audit lead prioritization and selection, identification of non-filer false positives, and taxpayer education. Figure 4-3 shows the broad range of compliance activity to which analytics can be applied;
x automated deployment of those models to score cases;
x assignment of compliance cases to compliance treatments using business rules;
x development of treatment strategies across external or internal case management and work management systems;
x implementation of Business Intelligence reports to track the performance of predictive models, treatment strategies, challenger strategies; and
x implementation of dashboards to show the overall performance (or "impact analysis") of compliance activities at the Department of Revenue level, with drill down to compliance departments, and lower if required.
Figure 4-3: RSI's TaxAnalytics Framework is a "Revenue Ready" Approach for the Application of Data Analytics to Taxpayer Compliance
Client data stores are used to update the Analysis Repositories (including modeling data mart specialized designed to support tax-specific modeling problems). This in turn enables model development, deployment of models to score cases and assign them to treatments. Cases are then routed to case management systems where they can be reviewed and worked in prioritized order. The data cycle continues as the case management systems generate performance data and as subsequent returns are filed. Predictive models can be re-developed and performance reports can be generated at any time based on the updated performance data maintained in the Analysis Repositories.
4.1.B.2. Analytics
Foundation
A lot of tax goes unpaid every year. All tax agencies spend considerable time and effort trying to minimize unpaid taxes. Making those efforts more effective without increasing staff levels is one of the management challenges facing DORs. Any organization that makes a given decision multiple times (such as selecting audits or collecting taxes due) will experience a wide range of variability in the effectiveness in those decisions. Some will be extremely effective: the highest yielding cases will have been audited; the taxes due will have been collected very quickly. Other decisions will not be so good: audits will result in refunds, no-change assessments, or very low assessments; collection cases will not pay, or will only partially pay. How do you always select the next best audit case? Is a one size fits all approach to collection the most effective? If not, how do we assign different cases to different collection strategies? How do we know how well our decisions are working out? How do we know when we need to
TaxAnalytics
Framework
•Analytical Records • Scoring Attributes • Use of Prior History • Rebuild/Recalibrate
Case Creation, Automation and Prioritization
Refund Fraud
-Return Validation
-Audit Selection
-Desk Audit Prioritization
-Collection Treatments
-Non-Filer/Delinquency Actions
-Abatement Handling
-Proactive Taxpayer Education
-Business Intelligence
• Decision Effectiveness • Model Validation • Performance Dashboard • Test & Control
Impact Analysis • Voluntary Compliance • Tax Segment Analysis • Revenue Forecasting
Analysis Repositories
Data Stores (Internal & External Sources)
Model Development Case Management Tax Processing & Accounting • Scoring • Assignments • Decisions • Define Treatments Analytics Deployment
‘tweak’ this strategy or that? In other words, how do we maximize our compliance effectiveness?
Analytics is the answer to overcoming this variability. Analysis of all the audited cases can identify the characteristics of the very best cases, the refund cases, the no-change cases, the low performing cases. Putting all this analysis into predictive models enables taxpayers to be prioritized from the very best to the very worst – thus enabling enhanced decision making regarding the selection of cases. For examples:
x Analysis of collection cases can identify those cases that will pay the taxes owed very quickly, as well as those taxpayers who will only pay when enforcement actions are applied. This allows collection cases to be prioritized in terms of their likelihood of paying – which in turn enables collection cases to be assigned to treatment strategies specifically designed for a given level of risk. This, in turn, allows the appropriate assignment of collection staff to treatments. Low-risk treatments should be developed to be entirely automatic (i.e., notices and reminder letters) for a short period of time, such as six months. Experience shows that approximately 75% - 80% of low-risk cases self-cure within this period. The remaining cases should then be routed through an increasingly severe set of actions, culminating in enforcement activities such as liens and levies. Similarly, high risk-cases will tend to not respond to notices and early collection actions, therefore the treatment strategy designed for these cases should comprise due-process followed by accelerated application of enforcement activities. Not expending collector resources on the early collection of low- and high-risk cases means that collectors can focus on medium-risk cases. Experience also shows that medium-risk cases are where the majority of the taxes owed are to be found. Focusing collectors on these cases increases the revenues collected.
x Taxpayers can be prioritized in terms of their probability of being good audit candidates. This enables only those with the highest probabilities to be selected. This has several benefits. Assessments will increase as auditors work cases with high probabilities of yielding high assessments per auditor hour spent on the audit. Assessments will also increase as auditors work fewer no-change audits, thus increasing the proportion of productive hours worked.
x The prioritized audit leads have an additional benefit. It is almost certain that, after the existing auditor resources have been fully utilized by assigning the best cases to them, ‘good’ audit leads remain. Almost certainly nothing will be done with these leads, unless an automated process is developed. Such a process could entail the assignment of these cases to some form of self-audit such as the re-filing of certain returns or provide more information regarding eligibility for specific credits, specifically pointing out each taxpayer’s risk factors to guide them in which line-items to pay specific attention to.
x Abatement cases can be prioritized in terms of their probability of resulting in a reduction or entire elimination of the assessed amount. This enables those cases with the highest probabilities to be worked first, thus minimizing the interest that may have to be paid to these taxpayers.
x Business non-filers are the bane of most collection departments. Models can be developed to separate false-positives from true-positives. In turn, this enables non-filer cases to be effectively assigned to appropriate work queues. For example, cases with high probabilities of being true-positives can be accelerated
into examiner or collector work queues, those with low probabilities can be assigned to a purely automated treatment, and medium-risk cases can be assigned to either the existing non-filer treatment or to a new treatment strategy reflecting the risk and likely balances owed.
x Educational correspondence or targeted messaging can also be leveraged to increase compliance. For example, predictive models can be developed to forecast the likelihood of taxpayers becoming non-compliant in the near future, such as over the next three months. Those with high probabilities can be sent an appropriate letter describing their risks and the subsequent audit and collection activities that will ensue, with the aim of deterring non-compliance.
x Analytics can also be used for outreach purposes. It is straightforward to identify those taxpayers most likely to be affected by a tax law, using a tax law change impact model. Applying that model to recent taxpayers enables those most likely to be affected by the change to be identified and then sent materials explain the change in the law and how that is likely to affect how they should complete their next return. In every tax law change there are taxpayers who benefit and those who pay more tax. Clearly, different educational materials would be required for these two groups.
Clearly, many aspects of compliance management decision-making can be improved through the use of analytics. Analytics enables enhanced decision making across all aspects of compliance management. Analytics will multiply the effectiveness of a given level of compliance effort. Analytics can enable increases in compliance revenues without an increase in compliance staff.
4.1.B.2.a The Solution
Having discussed some of the ways in which analytics can be applied to taxpayer compliance management, we now turn to a discussion of enabling technology that Departments of Revenue can deploy to support one or more these applications. Effectively deploying “analytics” into your agency is not a simple task. Just developing a regression equation is not going to do it for you. There are many related questions and issues, such as:
x How do we get the best audit cases to auditors?
x How do we ensure that complex cases are assigned to the most experienced staff?
x How do we get collection cases to the right collection strategy?
x How do we know how well that decision making is working?
x How do we evaluate changes to the assignment process and to the treatments to which cases are assigned?
x Which areas of the organization should receive analytical support first?
RSI has solved this problem with a single, integrated, and proven offering, drawing on a decade of experience: our analytics-driven TaxAnalytics Framework. TaxAnalytics Framework enables the prioritization of audit cases by fully integrating with DOR data sources, automatically developing the predictive models that enable prioritization, deploying those models, scoring all cases and making the scores and probabilities available for inclusion in all selection programs. If you already run queries to select cases, simply adding another column
to your query will enable prioritization. Alternatively, you can build a new query that prioritizes all taxpayers and use that to select new audit cases from.
Similarly, TaxAnalytics Framework enables the effective assignment of collection cases to treatment strategies. RSI’s experienced staff will work with your collection organization to develop treatment strategies for cases with different risk levels. Simplistically, these could be for low-, medium-, and high-risk cases. TaxAnalytics Framework integrates with your data sources enabling the development of the predictive models that identify the risk level of each case. TaxAnalytics Framework then deploys those models to score all cases entering collections, performs decision making about which strategy each case should be assigned to based on risk (and possibly other factors), and integrates that information, together with the probability of collection and the expected yield, to the collection case management system where it will be used to route different cases through different collection processes.
Being able to do this in an agile fashion requires the abstraction of multiple capabilities into a single system, TaxAnalytics Framework. TaxAnalytics Framework includes the following features:
x Integration with DOR data sources to enable the construction of very broad analytical databases;
x Automated development of predictive models;
x Automated deployment of those models for scoring purposes;
x Automated deployment of business rules for assigning cases to treatment strategies;
x Integration with audit selection and collection case management systems;
x Automated updating of compliance case performance in the TaxAnalytics Framework internal database;
x Performance reporting on all decisions and models; and,
x Automated re-building and deployment of predictive models on an as required basis.
TaxAnalytics Framework enables the DOR to manage compliance strategy rules in a way that integrates risk models (probabilities) into business rules, policy, realities of volumes, etc. Another key feature is the ability to monitor results and adapt/change over time. And the abstraction of the model development process provides the ability to refine models/strategies and the various uses of “analytics” without having to have a staff of analysts or hire a vendor. Of course, an analytics-based compliance platform like TaxAnalytics Framework, once in place, can be used for many more types of compliance than simply audit and collections. TaxAnalytics Framework could be used to drive taxpayer education programs, prioritization of abatements, fraud detection, voluntary compliance measurement, revenue forecasting, and a complete range of preventive compliance programs. It addresses taxpayer compliance “holistically” – tailoring to each taxpayer and considering all areas of work/interaction (abatement, refund, receivable, registration, audit, etc). TaxAnalytics Framework enables an organization to start using analytics in one area, such as collections or refund fraud, and then expand into another area. This expansion can be achieved with a high-degree of re-use of many of the TaxAnalytics Framework functional components. The portfolio approach to analytical records for predictive modeling is identical across all applications. The automated
predictive model development process is identical across all applications. Model deployment is identical across all applications, though all applications require a certain amount of integration work to prepare cases for scoring and to deliver the scores and prioritized cases back to the case management systems. Decision effectiveness reporting and model validation reporting is common across all applications. Compliance case performance tracking is standard across all applications. There is a large amount of re-use of the TaxAnalytics Framework functionality across multiple applications. This means that expansion does not require a lot of new development and training. A core DOR team can manage the entire operation and provide consulting support to their ‘client’ organizations, i.e. examination, collection, etc. TaxAnalytics Framework enables the management of multiple models across multiple applications.
TaxAnalytics Framework is a proven solution, with these capabilities being used by several clients. Predictive models, of various types, have been developed by RSI in MA, CT, NC, and SC. In MA, predictive models are being used to drive the assignment of collection cases to pre-existing treatment strategies. Over $50M of increased collection revenues annually has been ascribed to this program. In CT, predictive models are being used to drive the selection of Sales and Use Tax audit candidates. During the first year following implementation assessments increased by 26%. RSI is currently developing a TaxAnalytics Framework implementation for North Carolina that will support both audit selection and the collection process. In South Carolina, the TaxAnalytics Framework methodology was followed to measure the voluntary compliance impact of the DOR individual income tax audit program. The DOR audit program was shown to yield $4 in voluntary compliance for every $1 in direct assessments.
Instead of thinking about one targeted solution to a single problem, there is a better way. Start with a product that works instead of developing a custom solution. Integrate this with your existing systems environment and rules, in all their complexity, and drive additional value out of these technologies. TaxAnalytics Framework enables the rapid and effective development and deployment of analytically based solutions cheaply and easily.
4.1.B.2.b Enabling Technology
RSI has partnered with SAS – the worldwide leader in analytics software – to deliver TaxAnalytics Framework. The TaxAnalytics Framework builds on a foundation of integrated taxpayer data to enable analysis of taxpayer behavior and the development of predictive models. In turn, these then allow the creation of compliance cases and the assignment of those cases to treatments within the client’s case management system. Compliance case results are aggregated within TaxAnalytics Framework to enable the measurement and reporting of results. That performance also enables the refinement of the models and treatments in the pursuit of continuous performance improvement.
4.1.B.2.c The Right Partner
RSI’s staff has been at the forefront of this technology and approach for the last 15 years. We appreciate all facets of the compliance management problem – we have implemented many audit and collection systems over the years, including analytics. We have seen how analytics integrates with the client’s compliance systems and leverages the effectiveness of those systems to improve overall taxpayer compliance and revenues.
Several SAS components, with RSI developed extensions, form the basis of TaxAnalytics Framework. RSI has encapsulated that experience into extensions to SAS to "make analytics work" more effectively, at less risk, and at less cost to tax agencies. TaxAnalytics Framework is specifically configured for taxpayer compliance management, and makes it easier to deliver analytical products, such as scores, to the compliance case management process. In this way, TaxAnalytics Framework raises the effectiveness of the compliance organization without making major changes to the business processes of the organization and to the various legacy supporting systems, such as tax processing and case management.
Once implemented, TaxAnalytics Framework can become an enterprise analytics foundation. Enabling predictive models to be developed and applied for additional compliance management purposes, including: selecting cases for educational purposes, prioritizing abatement requests, and to enable risk-based compliance initiatives. In each of these areas the models enable the assignment or selection of cases based on risk, as well as ensuring that auditors/collectors are always working the next best case. Further, individual assignments of cases to auditors/collectors can be optimized to match case characteristics with staff member experience and skill.
Using models as a key component of managing compliance workflow is a major step to becoming a data-driven organization, in which a scientific interpretation of the data enables the organization to achieve previously unattainable levels of compliance, effectiveness and productivity.
4.1.B.3.
Proposed Data Analytic Services
To leverage the power of TaxAnalytics Framework a range of data analytics services are required. These services will ensure the integration of TaxAnalytics Framework with the data sources available within your organization, and with the application area within the Department of Revenue which is receiving analytics. They will also ensure that models, rules, probability-based treatment strategies, and performance tracking reports and dashboards are developed and implemented. In a very real sense, these services provide the organization with the necessary training that will enable the organization to do its own future expansion into additional application areas using TaxAnalytics Framework.
RSI will implement TaxAnalytics Framework and work with the Minnesota Department of Revenue to provide the following services to achieve Minnesota’s stated goals for:
1. Enhancing the State’s tax collection process; 2. Increasing revenue collection;
3. Detecting and preventing tax fraud;
4. Increasing delinquent tax revenue collection;
5. Identifying and dealing with compliance issues before tax return processing is complete; and,
6. Create and leverage optimization algorithms that will assist with maximizing revenues collected with current levels of compliance staff.
4.1.B.4.
Data Preparation Services
RSI has processed over 100 different data sources into the Revenue Premier Portfolio-based analysis database using the proven Portfolio Management process – without requiring custom coding or database schema modifications to the core data model. Proven configurations for most of these sources are used as a starting point for every data source, which significantly reduces risk.
It is assumed that various data sources will be used to construct taxpayer history into complete portfolios, where everything that is known about a given taxpayer is brought together into one logical record. This enables the development of richer predictive models and more effective treatment assignments. RSI has developed many data load configurations to ensure that taxpayer portfolios are developed accurately. These have been developed for data sources such as: Federal sources, common wage reporting and W-2 data sources, and motor vehicle licensing and registration information. Proven data validations, standardizations, name parsing, data matching, TIN validity, and other functions are embedded in these configurations and need not be recreated from scratch.
Historical data is critical to successfully understanding the compliance behavior of the taxpayer population. New data, in a steady stream, is necessary to allow analytical steps to remain current into the future. Near real-time data is required for fraud detection and prevention purposes, throughout the filing season. RSI has deep experiencing working on data interfaces from legacy processing environments, and can implement any interfaces that may be required to get legacy data into the analysis database.
The existing warehouse may be used as a source of aggregated taxpayer information, including historical data.
Because the analytical database brings together everything that is known about each taxpayer, it is a data structure that can be used for a broad range of analytical and modeling purposes. Modeling for collection or fraud purposes can be easily be achieved simply by changing the selection of cases in the analysis database, and the definition of the dependent variable in the analysis.
In addition, TaxAnalytics Framework maintains updated compliance case performance data. This enables new analytical databases to be created at any time. This, in turn, means that predictive models can be readily updated at any time. In the past, the time required for the creation of the analysis database has been a major component of all predictive modeling projects. With TaxAnalytics Framework, this constraint has been removed. Another benefit of the updated compliance case performance data is that reports and dashboards will always be current.
4.1.B.5.
Predictive Modeling and Data Mining Services
The RSI Modeling Framework within TaxAnalytics Framework is a set of reusable functions that provides structured capabilities to rapidly develop and deploy predictive models (e.g., collection models or fraud risk models), integrate those models with case selection rules and case management actions, and enables performance reporting. This powerful asset represents over a decade of effective taxpayer behavior modeling experience, successfully predicting non-filing, underreporting, fraud, or payment behaviors – in order to make more informed and risk-based decisions. Some providers of simple “tool-based” solutions do not
bring this experience and proven modeling methodology to an engagement, which introduces risk, inefficiency and delays. The Modeling Framework abstracts many of the complexities of model development, accelerates the delivery of models, incorporates years of best practices, and therefore will insure greater model effectiveness for Minnesota.
RSI’s Modeling Framework includes:
x Procedures for creation and maintenance of comprehensive analytical data analysis marts for model development purposes that contain relevant history about each taxpayer. The data marts are specifically designed to support a structured approach to model development by eliminating judgmental decision making and allowing the “data to talk.” Once integrated with Minnesota Department of Revenue’s data sources, modeling data marts based on taxpayer portfolios that capture all relevant information about each taxpayer, can be created in hours. This further facilitates the ability to dynamically re-develop and deploy models as new data is assembled and as new case outcomes are captured.
x Automated creation of empirical predictive models. A process that typically takes competitors months has been reduced to hours using RSI’s Modeling Framework. This facilitates the regular redevelopment or adjustment of predictive models. Predictive models leverage the historical behavior of taxpayers to forecast their risk of non-compliance behaviors such as filing fraudulent returns or paying their overdue tax debts.
x Automated deployment of predictive models to score returns/taxpayers in order to select and prioritize cases. Integration with rule processing and case management, together with the dynamic internal deployment of models, enables the evaluation of cases for scoring, assigning actions, and distribution for auditor review or other action.
x Automated updating of the modeling data marts to support regular model refinement. As returns/taxpayers are scored, the modeling data mart is updated with those scores and assignment decisions. As cases are worked, the resulting dispositions are updated into the modeling data mart as well. These dispositions, coupled with the rapid creation of new analytical data marts, enable the regular redevelopment of predictive models at very low cost. These dispositions also enable reporting on the effectiveness of the models over time.
For Minnesota, the Modeling Framework will enable:
x Development of predictive models for collection and fraud prevention purposes. This will enhance Minnesota Department of Revenue’s collection effectiveness, and leverage your previous fraud detection results and extend them into the wider population of taxpayers. In collections, it’s likely that several models will be developed, reflecting the results of a segmentation analysis, one model per segment. For fraud detection and prevention, each known fraud scheme will have its own model developed and deployed – thereby adjusting rules as necessary, statistically validating the effectiveness, and integrating into the complete set of fraud detection business rules.
x Rapid re-development and re-deployment of predictive models. This will ensure that scoring always reflects the results of recent compliance cases, especially collection and fraud cases, particularly new fraud schemes. Minnesota Department of Revenue will have confidence that cases are accurately ranked in
terms of their risk. Collection cases can be assigned to treatments based on risk, and perhaps other factors such as balance owed. Fraud cases can be ranked by their probability of being fraudulent, enabling identification of the next best case to be assigned to an auditor for review and thus ensuring maximum staff productivity.
x Reducing false positives. Continuously incorporating the results of recent cases into new models will lead to more precise models that, in turn, will reduce the number of false positives. This applies especially to predictive models used to identify fraud cases and to non-filer false positives. As a result, more monies will be saved by preventing payment of fraudulent or inflated refunds. And costs will be reduced by not trying to collect likely non-filer false positives, enabling collectors to focus on real cases. In addition, more precise models will minimize inconvenience to compliant taxpayers.
x Identification of, and focus on, working more fraudulent returns. This will have a direct impact for Minnesota in terms of monies saved. It will also have an indirect effect on voluntary compliance. As the number of cases worked – and worked effectively – increases, so also will the voluntary compliance effect increase as more taxpayers will know that Minnesota is more effectively identifying and pursuing fraudulent cases and inflated refund requests.
4.1.B.6.
Probability-Based Treatments Strategy Consulting Services
RSI has worked with multiple clients to design, develop, and implement probability-appropriate treatment strategies. A treatment strategy is a set of actions put together in a specific manner to achieve a specific goal. Typically, treatment strategies reflect the policy of the given department on how to deal with certain types of taxpayer.
RSI will work with the Minnesota Department of Revenue to develop probability-appropriate treatment strategies. These strategies will most likely reflect probability or risk. They may also reflect a dollar number such as the balance owed or the refund amount requested, if desired. Treatment strategies will be developed in a series of working sessions with the DOR bringing its understanding of how to work compliance cases and RSI will bring its understanding of the meaning of risk and our previous experience in other states. Together, we will design a series of treatment strategies to cover all possible types of case. Usually, these strategies are then implemented in existing systems, such as a collection system, by our clients. If required, RSI can support or perform this implementation service. RSI will provide a level of integration between the case management systems housing the treatment strategies and TaxAnalytics Framework. RSI will ensure that all necessary scores, probabilities and assignments are made available to the case management systems via an integrating table. Normally, it is the responsibility of the host system to pick up those scores, probabilities and assignments and use them to drive cases through the designed treatment strategy until the case resolves.
Predictive Models are enablers, enabling different types of interactions to take place with different types of taxpayer. Collection treatment strategies reflect a philosophy about collecting cases from delinquent taxpayers with different types of risk. Statistically, low-risk cases will respond to little or no collection actions. They have a very high probability of paying their debt within a short period of time. It is, therefore, a “waste of time” to have collectors actively working these cases – these taxpayers are going to pay anyway!
Similarly, high-risk taxpayers have a very low probability of responding to soft collection actions. Therefore, it is a “waste of time” trying to collect them. These cases should be accelerated into enforced collection activity as soon as possible.
By not actively working low-risk cases and high-risk cases, collectors can focus on those cases they can really influence – medium-risk cases. In this way, medium-risk cases experience an effective increase in the collector resources working them.
Treatment strategies can be developed for all categories of taxpayer in collections, each designed to apply appropriate actions to a specific group of taxpayers. A risk-based approach to collections gives the tax agency the opportunity to optimize its collection interactions with taxpayers. All taxpayers must be given due-process. Beyond that, the tax agency has some liberty to collect taxes in the most effective way it can. Behavior models enable the tax agency to identify, on day one, the probability that a taxpayer will “pay-in-full” – or otherwise resolve, their debt, and to then assign that taxpayer to the most appropriate treatment scenario.
Examples of two possible strategies are:
1. A low intensity treatment scenario applied to low-risk cases reminds the taxpayer, through notices and automated correspondence, of the debt owed, and provides customer service by allowing plenty of time for the taxpayer to pay. Typically, seventy-five percent to eighty percent of low risk taxpayers will fully pay their debt. Following a reasonable time period, the remaining taxpayers are subjected to increasingly severe actions, culminating in enforced collection actions such as liens and levies.
2. High-risk taxpayers may be assigned to a brief, but relatively intense, scenario. By definition, these taxpayers are not likely to respond to collection treatments. The goal in this case is to provide due process and then move the taxpayer on to more effective enforced collection actions as soon as possible.
In addition to assigning cases to appropriate treatment strategies, the queues worked by collectors should be sorted by “expected yield.” Expected yield is the statistical value of the account after the probability of paying has been taken into consideration. Sorting work lists in descending order of potential value, floats the most collectible dollars to the top. In this way, if there are too few collectors to work the entire work list, they would at least be working the most collectible dollars and maximizing collection revenues. In addition, it may not be cost effective to work cases at the bottom of the queue below a specified potential value threshold.
Collection treatment strategies would be implemented in the Minnesota collection system. Other treatment strategies can be developed for other purposes. For example, workflows to handle refund fraud cases from different fraud schemes could be developed, as could workflows for cases with different levels of fraud probability. It should also be noted that modeling for audit selection typically prioritizes all taxpayers. Many “good” audit leads remain after the existing field and desk audit capacity has been fully assigned the best leads. These remaining audit leads will never be worked because of the shortage of capacity to audit them. This is essentially leaving money on the table. If audited, many of these cases would result in additional assessments and, ultimately, additional tax payments. Treatment strategies, such as self-audits or certified audits, could be created to match the probability profiles of these remaining “good” cases, thus ensuring the maximum return from predictive modeling.
4.1.B.7. Business
Intelligence
Service
Best practice in the use of analytics to enable the identification, selection and prioritization of compliance cases requires the use of reporting and dashboards to track the performance of the predictive models and business rules used, and the decisions made. In addition, dashboards can be used to measure and track the overall compliance results at the organization level, with drill down to the departmental level if required. Over time, the
performance of predictive models will deteriorate. Tracking this performance can give a good indication as to when the predictive models should be re-built, which within the TaxAnalytics Framework is easy to do. Organizationally, in a data-driven compliance management environment, it is helpful to know the overall results of compliance activities, in terms of revenues assessed and collected, and the costs of those efforts. Being able to drill down to the departmental level would enable comparisons to be made between discovery, audit, and collections in terms of such dimensions as average collection per staff member, additional revenues collected per dollar invested, or the revenues collected per $1 of costs.
Performance Reports are also an integral component of the champion/challenger decision making process. Decision Effectiveness Reports, when applied to collection cases, will show the relative performance of each treatment strategy to which collection cases are being assigned. Evaluation of the performance of each strategy can guide management when determining which challenger strategy to evaluate next. Challenger strategies are treatments that are, in some way, different from the treatment to which some cases are currently being assigned. They have been created to improve the level of performance of a type of compliance case. For example, collection cases of a particular risk profile may currently be being assigned to the medium-risk treatment strategy, but it may be believed that these cases can be just as effectively collected by assigning them to the low-risk strategy, and thus free-up the collector resources currently being expended to collect them. Before rolling the challenger strategy out to this category of case, it must be evaluated to determine that it is, in fact, more effective than the current, or champion, strategy. If not then it is counter-productive to roll it out. To do this a small proportion of these cases are assigned to the challenger strategy while the remainder are assigned to the current champion. Comparing the subsequent performance of these cases, using the Decision Effectiveness Report, will show whether the challenger strategy is at least as effective as the current champion and thus enable a data driven decision regarding the assignment of cases to treatments.
In this example no changes were made to the treatment strategies, the only thing that changed was that the business rules for assigning cases to existing treatments were modified. In addition, the Decision Effectiveness Reports were being used to ensure that performance for a given group of cases was just as good in the low-risk strategy as it had been in the medium-risk strategy. However, it should be noted that other challenger strategies for other goals can be evaluated. These include challenger strategies to increase the revenues collected, as well as changes to a given strategy such as changing the colle
