How Modern Taxation can benefit from Big Data Analytics..

We have repeatedly discussed how Predictive Analytics built on Big Data capabilities can create a tremendous amount of differentiation and value for Banking and Insurance companies. However, one of the most important areas of public finance – taxation- is just waking up to the possibilities of using this game changing technology – with the goal of  increasing public revenues via the collection of indirect taxes. In this blogpost, we will examine both perspectives – the taxation authority as well as the Corporate under the burden of tax reporting.

Tax

National Tax Authorities begin information sharing..

The last few years have seen increased digitalization of national tax accounting systems. More and more citizens opt to file their returns electronically. Reporting rules for businesses (such as Banks) that have wealthy account holders who have substantial financial assets spanning continents have also gone up. Different legislations have been promulgated in an effort to ensure that the exchange of information on tax matters is as seamless as it can possibly be – with the goal of curbing tax evasion.

The US Government’s tax arm – the IRS (Internal Revenue Service)- introduced the FATCA (Foreign Account Tax Compliance Act) in 2010 with the intention of detecting and tracking US nationals that reside abroad and owe the US taxes. The FATCA intends to prevent US taxpayers who hold non-US financial assets from avoiding taxation by ensuring that foreign banking institutions report on the US account holders or face a withholding of 30% on all US income.

The Organization for Economic Cooperation and Development (OECD) in conjunction with the US authorities has adopted the Common Reporting Standard (CRS), as an information standard for the automatic exchange of taxpayer information. As part of CRS, more than 90 countries now share information on residents assets and incomes in conformance with the reporting standard. CRS is far wider than FATCA and imposes a significant burden on the compliance team in a bank.

As an example, both the US and the UK governments enforce compliance with CRS. This enables bilateral information sharing between both national taxation authorities. [1]

To that end, national and regional tax authorities have begun using Big Data techniques refined in the private sector to increase the collection of taxes from citizens while reducing fraud and waste in the system.

Across a range of national compliance regimes, Big Data can help improve both the tax collection and improve risk scoring process. It can do this  by way of advanced analytics that operates on a much richer and wider set of data than was possible before. On the business side it can help with ensuring compliance reporting and not overpaying.

Catalogued below are the important areas in which Big Data Analytics can present a huge impact in the field of public taxation.

# 1: Help both Taxation Authorities and Finance Departments Efficiently Store and Process Enormous Amounts of Taxation Data 

The point is well made that the entire indirect taxation process is a complex process in terms of both the breadth and the depth of documentation. This is for both corporate tax departments and of the authorities. Businesses need to look at a complex range of multifaceted tax data across all of the thousands of national, state and local jurisdictions they operate across. Not being able to store, process the data in one place induces all the challenges caused by data silos. The Data Lake is a natural fit to store historical tax documents, Accounts Payables/Receivables, Expense information, business receipts, emails, call transcripts etc. Data from various source systems that drive the tax process should be from Master Data, Reference Data (containing fresh & accurate tax rate/jurisdiction data), data from various Book of Record Systems, ERP, finance and legacy financial systems. The key point here is to automate this data movement and cut down manual ingestion processes thus improving both the speed and quality of the process at the first & most important step itself.

                                                    Click on Image to view a blogpost on Data Lakes 

# 2: Perform Accurate Customer Due Diligence (CDD) by Creating a Single View of Compliance

Given the complexity of both compliance regimes, Big Data can help automate Customer Due Diligence (CDD)/ know your customer (KYC). This is important in helping improve both the tax collection and improve risk scoring process;

Big Data Analytics can also make the CDD highly automated as opposed to a manual laborious process especially around tax avoidance watch lists or suspicious accounts. One of the first steps here is to help create a 360 degree view of entity whether a high risk individual or a corporate entity for taxation. Doing so enables better account servicing as well as provides a holistic activity of fraud. Adding a machine learning process on this can help detect micro patterns of fraud across 10s of accounts across geographies. KYC programs are becoming increasingly daunting undertakings due to issues such as difficulty in identifying customers across multiple lines of business, and lack of a consistent view of customer bank product use and transaction activity. Further complicating these challenges is the advent of new risks such as digital currencies, new and unique payment methods, and continued variation in global data privacy regulation—all of which are resulting in enhanced regulatory scrutiny of banks readiness in this area. 

# 3: Predicting Tax Yields (or Liabilities) More Accurately

Leveraging the ingestion and predictive capabilities of a Big Data based platform, tax authorities can create a full picture of an individual or entity across all accounts or geographies. Internal Bank compliance teams can do the same with their client accounts. This can be used to predict better tax yield and compliance numbers.

# 4: Vastly Improve Tax Fraud & Evasion by improving Risk Scoring & Detection

The Banking industry has already begun leveraging sophisticated fraud detection strategies using socio-demographic data and taxpayers behavior. Big Data can be used to look at 10s of attributes per individual that were previously missed out before even for internal structured data. Adding a machine learning process on this can help detect micro patterns of fraud across 10s of accounts across geographies. 

Big Data based analytics also operate at scale thus eliminating manual and cumbersome spreadsheet based analysis – which bedevils the ability to quickly and visually detect tax fraud and evasion.

# 5: Improve the Auditability & Accuracy of Regulatory Reporting

Most businesses currently use ERP systems and engines within those to help with their taxation process. An example is to calculate VAT (Value Added Tax) obligations using the same.  These traditional tax engines and tools suffer from all the issues that plagued traditional tax data storage – operating on limited data sets which may or may not be accurate, manual processes and reconciliations lead to audits more regularly.  Big Data with its focus on data quality and overall governance can help remedy these issues. It can help improve the quality, timeliness and overall confidence in the reporting thus leading to lower number of audits.

Conclusion..

Opening the door to the latest data storage and processing techniques can help taxation authorities introduce a higher degree of automation into their core business functions. This will allow them to reduce manual data operations, avoid costly reconciliation & reporting discrepancies – thus reducing costly audits.  This will enable them to focus on tasks such as strategic forecasting and better tax planning.

References..

[1] Indirect Tax Compliance in an Era of Big Data – Gillis, McStocker and Percival, KPMG –   http://www.bna.com/indirect-tax-compliance-n17179927659/

How Big Data & Advanced Analytics can help Real Estate Investment Trusts (REITS)

                                                         Image Credit – Kiplinger’s

Introduction…

Real Estate Investment Trust’s (REITS) are financial companies that own various forms of commercial and residential real estate. These assets include office buildings, retail shopping centers, hospitals, warehouses, timberland and hotels etc. Real estate is growing quite nicely as a component of the global financial business. Given their focus on real estate investments, REITS have always occupied a specialized position in global finance.

Fundamentally, there are three types of REITS –

  1. Equity REITS which exclusively deal in acquiring, improving and selling properties with the aim of higher returns for their investors
  2. Mortgage REITS only buy and sell mortgages
  3. Hybrid REITS which do both #1 and #2 above

REITS have a reasonably straightforward business model – you take the yields from the properties you own and reinvest the funds to be able to pay your investors (a mandated 95% of dividends). Most of the traditional REIT business processes are well handled by conventional types of technology. However more and more REITs are being challenged to develop a compelling Big Data strategy that leverages their tremendous data assets. 

The Five Key Big Data Applications for REITS… 

Let us consider at the five key areas where advanced analytics built on a Big Data foundation can immensely help REITS.

#1 Property Acquisition Modeling 

REITS owners can leverage the rich datasets available around renters demographics, preferences, seasonality, economic conditions in specific markets to better guide capital decisions on acquiring property. This modeling needs to take into account land costs, development costs, fixture costs & any other sales and marketing costs to appeal to tenants. I’d like to call this macro business perspective. Also from a micro business perspective, being able to better study individual properties using a variety of widely available data – MLS listings for similar properties, foreclosures, closeness to retail establishments, work sites, building profiles, parking spaces, energy footprint etc can help them match tenants to their property holdings. All this is critical to getting their investment mix right to meet profitability targets.

                                  Click on the Image for a blogpost discussing Predictive Analytics in Capital Markets

#2 Portfolio Modeling 

REITS can leverage Big Data to perform more granular modeling of their MBS portfolios. As an example, they can feed in a lot more data into their existing models as discussed above. E.g.  Demographic data, macroeconomic factors et al.  

A simple scenario would be if Interest Rates go up by X basis points – what does that mean for my portfolio exposure, Default Rate, Cost Picture, Optimal times to buy certain MBS’s etc ?  REITS can then use that info to enter hedges etc to protect against any downside. Big Data can also help with a range of predictive modeling across all of the above areas as discussed below.  An example is to build a 360 degree view of a given investment portfolio.

                                                         Click on Image for a Customer 360 discussion 

#3 Risk Data Aggregation & Calculations 

The instruments underlying the portfolios themselves carry large amounts of credit & interest rate risk. Big Data is a fantastic platform for aggregating and calculating many kinds of risk exposures as the below link discuss in detail. 

  

                                            Click on Image for a discussion of Risk Data Aggregation and Measurement 

 

#4 Detect and Prevent Money Laundering (AML)

Due to the global nature of investment funds flowing into real estate, REITS are highly exposed to money laundering and sanctions risks. Whether or not REITS operate in high risk geographies (India,China, South America, Russia etc) or have complex holding structures – they need to file SAR (Suspicious Activity Reports) with the FinCEN.  There has always been a strong case to be made that shady foreign entities and individuals were laundering ill gotten proceeds to buy US real estate. In early 2016, the FinCEN began implementing Geographic Targeting Orders (GTOs). Title companies based in the United States are now required to clearly identify the real owners of either limited liability companies (LLCs) or any other partnerships, and other legal entities being used to purchase high end residential real estate using cash.

AML as a topic is covered exhaustively in the below series of blogposts (please click on image to open the first one).

                                                         Click on Image for a Deepdive on AML

#5 Smart Cities, Net New Investments and Property Management

In the future, REITS would want to invest in Smart Cities which are positioned to be leading urban centers offering mobility, green technology, personalized medicine, safe services, clean water, traffic management and other forward looking urban amenities. These Smart Cities target a new kind of client- upwardly mobile, technologically savvy, environment conscious millenials. According to RBC Capital Markets, Smart Cities presents a massive investment opportunity for REITS. Such investments could provide REITS offering income yields of around 10-20%. (Source – Ben Forster @ Schroeders).

Smart Cities will be created using a number of high end technologies such as IoT, AI, Virtual Reality, Device Meshes etc. By 2020, it is estimated that these buildings will be generating an enormous amount of data that needs to be stored and analyzed by landlords.

As the below graphic from Cisco attests, the ability to work with IoT data to analyze a range of these micro investment opportunities is a Big Data challenge.

The ongoing maintenance and continuous refurbishment of rental properties is a large portion of the business operation of a REIT. The availability of smart sensors and such IoT devices that can track air quality, home appliance malfunction etc can help greatly with preventive maintenance.

Conclusion..

As can be seen from some of the above business areas, most REITS data needs require a holistic approach across the value chain (capital sourcing, investment decisions, portfolio management & operations). This approach spans various horizontal functions like Customer Segmentation, Property Acquisition, Risk, Finance and Business Operations.
The need of the hour for larger REITS is to move to a common model for data storage, model building and testing.  It is becoming increasingly obvious that Big Data can provide massive business opportunities for REITS.

Why the Internet of Things (IoT) is about Data Driven Ecosystems (& not really about the Devices)..

The Internet of Things (IoT) will have a great impact on the economy by transforming many enterprises into digital business and facilitating new business models, improving efficiency, and generating new forms of revenue.However, the ways in which enterprises can actualize any benefits will be diverse and, in some cases, painful” – Jim Tully, vice president and distinguished analyst at Gartner – 2015.

The IoT is one of the most hyped paradigms floating around at the moment. However the hype is not all unjustified. Analyst projections have about 25 billion devices connected to the internet by 2020 delivering cumulative business value of $2 trillion[1] across many industry verticals. Enterprise IT need to now begin developing capabilities to harness this information to serve their end customers. This blogpost discusses foundational IoT business elements that are common across industries.

                                                         Image Credit – ThinkStock

The Immense Market Opportunity around IoT 

The IoT has rapidly become one of the most familiar — and perhaps, most hyped — expressions across business and technology. That hype, however, is entirely justified and is backed up by the numbers as one can glean from the below graphic. The estimated business value of this still nascent market is expected to be around $10 trillion plus by 2022.

                                                         Credit – Tamara Franklin (Oracle Research)

Thinking around IoT has long been dominated by passive devices such as Industrial Sensors, RFID tags and Actuators. As pointed out in my “Gartner’s Trends for 2017” article, these devices are beginning to form a smart mesh. Field devices now have increased ‘smart’ capabilities to communicate with each other and with the internet – typically using an IP protocol -resulting in the combined intelligence of groups of such ‘things’.  The IoT now enables not just machine to machine communication but also the human to machine and human to IoT ecosystems. While the media plays up stories of IoT aware devices such as Google Nest or Amazon Echo etc – it is also shaking up vertical industries.

Virtually every industry out there has a significant amount of connected devices that have been deployed. This includes Retail, Energy & Utilities, Manufacturing, Healthcare, Transportation and Financial services etc.  Having said that, let us consider the five key industrial uses for the IoT space that will yield tremendous business value over the short to medium term – the next 2-5 years.

The Six Key Industry Applications of IoT 

Consider the above graphic (courtesy the BCG), the real business value in IoT lies in Analytics and Applications built on these analytics. In fact, BCG expects that by 2020 these higher order layers will have captured 60% of the growth from the [3]. In such a scenario, the rest of the technology elements – connected things, cloud platforms & data architectures merely enable the upper two layers in delivering business value.

Let us then consider the key industrial use cases for IoT –
  1. Retailers implementing IoT are working to ensure that their customers gain a seamless experience while browsing products in the store.For example the industry has begun adopting smart shelves that restock themselves, installed beacons in stores that communicate with shopping apps on consumers smartphones and NFC (Near Field Communications) that enable customers to make contact-less payments.  Internal operations such as Supply Chains are benefiting in a big way in their ability to gain realtime insight into the
  2. In the area of Commercial real estate, facilities management is an area where companies spend massive amounts of money on energy consumption. According to Deon Newman, at IBM Watson[3], global conglomerates like Siemens own hundreds of thousands of building which produce tens of thousands of millions of emissions. In this case, IoT analytics is being leveraged to reduce such huge carbon footprint.
  3. In the Utilities Industry – as Smart Meters have proliferated in the industry, IoT is driving use-cases ranging from Predictive Maintenance of equipment to optimizing Grid usage. For instance, in water utilities, smart sensors track everything from quality to pressure to usage patterns. Utilities are creating software platforms that provide analytics on usage patterns and forecast demand spikes in the grid .
  4. The Manufacturing industry is moving to an entirely virtual world across its lifecycle, ranging from product development, customer demand monitoring to production to inventory management. This trend is being termed as Industry 4.0 or Connected Manufacturing. As devices & systems become more interactive and intelligent, the data they send out can be used to optimize the lifecycle across the value chain thus driving higher utilization of plant capacity and improved operational efficiencies.
  5. The biggest trend in the Transportation industry is undoubtedly self driving connected cars & buses. The Connected Vehicle concept enables a car or a truck to behave as a giant smart app – sending out data and receiving inputs to optimize it’s functions. With the passing of every year, car makers are adding more and more smart features. Thus, vehicles have more automatic features builtin – ranging from navigation, requesting roadside assistance, self parking etc etc. Applications are being built which will enable these devices to be tracked on the digital mesh thus enabling easy inter vehicle communication to enable traffic management, pollution reduction and public safety.
  6. With Smart Cities governments across the globe are increasingly focused on traffic management, pollution management, public services etc – all with a view to improving quality of life for their citizens.  All of these ecosystems will be adopting IoT technology in the days and years to come.

Conclusion..

It can be seen from the above that the applications are myriad. Thus, while one cannot recommend a generic IT approach to IoT thats applicable to every industry – familiar themes do emerge that apply from a core IT capability standpoint.

The next post will consider the five key & common technology capabilities that Enterprise CIOs need to ensure that their organizations begin to develop to win in the IoT era.

References

[1] Gartner July 2015 – “The Internet of Things is a Revolution waiting to happen” – http://www.gartner.com/smarterwithgartner/the-internet-of-things-is-a-revolution-waiting-to-happen/

[2] BCG Analysis – “Winning in the IoT is about business processes” – https://www.bcgperspectives.com/content/articles/hardware-software-energy-environment-winning-in-iot-all-about-winning-processes/

[3] “Cognitive Computing and the future of smart buildings” – Deon Newman, IBM Watson IoT

https://www.ibm.com/blogs/internet-of-things/cognitive-computing-future-smart-buildings/