“The Company, Get Onbord Limited, sought to develop a novel, automated artificial intelligence (AI) analysis process for ‘know your client’ (KYC) verification and risk profiling. The main objective of this project was to develop AI-enabled holistic analysis of a new counterparty during a financial services customer onboarding process that could achieve a superior outcome to human analysis and meet all regulatory and legislative requirements.”
“(2) “Research and development” means activities that fall to be treated as research and development in accordance with generally accepted accounting practice. This is subject to subsections (3) and (4). (3) Activities that are “research and development” for the purposes of section 1006 of ITA 2007 as a result of regulations under that section are “research and development” for the purposes of this section. (4) Activities that are not “research and development” for the purposes of section 1006 of ITA 2007 as a result of regulations under that section are not “research and development” for the purposes of this section.”
“(2) “Research and development” means activities that fall to be treated as research and development in accordance with generally accepted accounting practice. This is subject to subsection (3). (3) The Treasury may by regulations specify activities which– (a) are to be treated as being “research and development” for the purposes of this section, or (b) are to be treated as not being “research and development” for the purposes of this section. 1006 (4) The regulations may– (a) make provision by reference to guidelines issued by the Secretary of State, and (b) contain incidental, supplemental, consequential and transitional provision and savings.”
“For the purposes ofsection 837A of the Income and Corporation Taxes Act 1988 – (a) activities that fall to be treated as research and development in accordance with the “Guidelines on the Meaning of Research and Development for Tax Purposes” issued by the Secretary of State for Trade and Industry on5 March 2004 , are research and development; and (b) activities that do not fall to be treated as such in accordance with those guidelines are not research and development.”
“3. R&D for tax purposes takes place when a project seeks to achieve an advance in science or technology. … Advance in science or technology 6. An advance in science or technology means an advance in overall knowledge or capability in a field of science or technology (not a company’s own state of knowledge or capability alone). This includes the adaptation of knowledge or capability from another field of science or technology in order to make such an advance where this adaptation was not readily deducible. 7. An advance in science or technology may have tangible consequences (such as a new or more efficient cleaning product, or a process which generates less waste) or more intangible outcomes (new knowledge or cost improvements, for example). 8. A process, material, device, product, service or source of knowledge does not become an advance in science or technology simply because science or technology is used in its creation. Work which uses science or technology but which does not advance scientific or technological capability as a whole is not an advance in science or technology. 9. A project which seeks to, for example, a) extend overall knowledge or capability in a field of science or technology; orb) create a process, material, device, product or service which incorporates or represents an increase in overall knowledge or capability in a field of science or technology; orc) make an appreciable improvement to an existing process, material, device, product or service through scientific or technological changes; ord) use science or technology to duplicate the effect of an existing process, material, device, product or service in a new or appreciably improved way (e.g. a product which has exactly the same performance characteristics as existing models, but is built in a fundamentally different manner) will therefore be R&D. 10. Even if the advance in science or technology sought by a project is not achieved or not fully realised, R&D still takes place. 11. If a particular advance in science or technology has already been made or attempted but details are not readily available (for example, if it is a trade secret), work to achieve such an advance can still be an advance in science or technology. 12. However, the routine analysis, copying or adaptation of an existing product, process, service or material, will not be an advance in science or technology. … Scientific or technological uncertainty 13. Scientific or technological uncertainty exists when knowledge of whether something is scientifically possible or technologically feasible, or how to achieve it in practice, is not readily available or deducible by a competent professional working in the field. This includes system uncertainty. Scientific or technological uncertainty will often arise from turning something that has already been established as scientifically feasible into a cost-effective, reliable and reproducible process, material, device, product or service. 14. Uncertainties that can readily be resolved by a competent professional working in the field are not scientific or technological uncertainties. Similarly, improvements, optimisations and fine-tuning which do not materially affect the underlying science or technology do not constitute work to resolve scientific or technological uncertainty. … Overall knowledge or capability 20. Overall knowledge or capability in a field of science or technology means the knowledge or capability in the field which is publicly available or is readily deducible from the publicly available knowledge or capability by a competent professional working in the field. Work which seeks an advance relative to this overall knowledge or capability is R&D. 21. Overall knowledge or capability in a field of science or technology can still be advanced (and hence R&D can still be done) in situations where: - several companies are working at the cutting edge in the same field, and are doing similar work independently; or - work has already been done but this is not known in general because it is a trade secret, and another company repeats the work; or - it is known that a particular advance in science or technology has been achieved, but the details of how are not readily available. 22. However, the routine analysis, copying or adaptation of an existing process, material, device, product or service will not advance overall knowledge or capability, even though it may be completely new to the company or the company’s trade. Appreciable improvement 23. Appreciable improvement means to change or adapt the scientific or technological characteristics of something to the point where it is ‘better’ than the original. The improvement should be more than a minor or routine upgrading, and should represent something that would generally be acknowledged by a competent professional working in the field as a genuine and non-trivial improvement. Improvements arising from the adaptation of knowledge or capability from another field of science or technology are appreciable improvements if they would generally be acknowledged by a competent professional working in the field as a genuine and non-trivial improvement. 24. Improvements which arise from taking existing science or technology and deploying it in a new context (e.g. a different trade) with only minor or routine changes are not appreciable improvements. A process, material, device, product or service will not be appreciably improved if it simply brings a company into line with overall knowledge or capability in science or technology, even though it may be completely new to the company or the company’s trade. 25. The question of what scale of advance would constitute an appreciable improvement will differ between fields of science and technology and will depend on what a competent professional working in the field would regard as a genuine and non-trivial improvement. … System uncertainty 29. System uncertainty is scientific or technological uncertainty that results from the complexity of a system rather than uncertainty about how its individual components behave. For example, in electronic devices, the characteristics of individual components or chips are fixed, but there can still be uncertainty about the best way to combine those components to achieve an overall effect. However, assembling a number of components (or software sub-programs) to an established pattern, or following routine methods for doing so, involves little or no scientific or technological uncertainty. 30. Similarly, work on combining standard technologies, devices, and/or processes can involve scientific or technological uncertainty even if the principles for their integration are well known. There will be scientific or technological uncertainty if a competent professional working in the field cannot readily deduce how the separate components or sub-systems should be combined to have the intended function. … Content delivered through science or technology 43. Information or other content which is delivered through a scientific or technological medium is not of itself science or technology. However, improvements in scientific or technological means to create, manipulate and transfer information or other content can be scientific or technological advances, and resolving the scientific or technological uncertainty associated with such projects would therefore be R&D.”
“... the provisions form a detailed and meticulously drafted code, with a series of defined terms and composite expressions, and a large number of carefully delineated conditions, all of which have to be satisfied if the relief is to be available…a detailed and prescriptive code of this nature leaves little room for a purposive construction…”
“The Company considered that the development of the ONBORD system constituted an appreciable improvement in the technology associated with AI for KYC analysis. The development of the ONBORD system contained significant technical uncertainties, involving knowledge that could not be readily deduced by the Company’s competent professionals. Before starting this project, the company investigated the state of the art in the use of AI KYC for financial services organisations to determine whether there was an existing development in this discipline that could meet the objectives of the project. The company determined that there was no existing technology that was available in the public domain or readily deducible by the Company’s competent professionals that would allow it to provide an automated, AI-enabled onboarding solution. As there was no existing development, the Company was not certain if it would be possible to develop the algorithms In computing, an algorithm is a step-by-step procedure for calculations or other problem-solving operations. Algorithms are used for calculation, data processing, and automated reasoning. required to translate an existing risk policy into an automated onboarding process. Therefore, to successfully complete the ONBORD project, the Company would have to go through an iterative experimental development cycle as well as extensive testing and analysis. The Company considered that overcoming these challenges would constitute an appreciable advance in technology, as the technological advance sought would be new in the area of automated, AI-enabled onboading (sic) processes for companies across Europe. The Company was unsure if it could develop the algorithms required to perform data normalisation, to ensure that the identification of a person on a particular sanctions list, or a director or shareholder of a company, was consistent across various data sources. Unless these individuals were uniquely identified, it was impossible to leverage information from multiple data sources and create a master list of information. The alternative to this process was to create vast quantities of false positives – what traditional methods produce because they do not factor in context or attempt to determine unique persons. The Company did not know if it could develop a method to optimise the data increase the speed of a client journey and ultimately reduce the time from start of client sign-up to completion of onboarding and KYC checks. The Company was also uncertain if it could integrate and normalise data from various data sources to achieve this goal, using AI to validate information and auto populate any inputs required. As more and more data were added to confirm the identification of the counterparty and ensure all regulatory requirements were met, it was necessary to develop AI methods to call the data needed for each part of the process only when required. This was needed as an alternative to calling all the data upfront, as the processing necessary to do so would slow the KYC process to a standstill and/or make the client interface unusable. The Company was uncertain if it could develop an API Application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API is a set of clearly defined methods of communication between various software components and specifies how those components should interact. so that data could be transferred into and out of any client system. Development of the API required the Company’s software developers to ensure that the processes could be adapted to transfer data into and out of any client system, regardless of client use case. Extensive work was required to ensure security of the data exchanged while maintaining the data structure previous developed.”
“Example area 1: Compliance flags on an individual: This algorithm was designed to replicate the manual checks that a person would need to perform to meet the requirements of customer due diligence in theMoney Laundering, Terrorist Financing and Transfer of Funds (Information on the Payer) Regulations 2017 . The Company’s objective in undertaking the development of this algorithm was to create a robotic process automation engine that could approve or decline an individual based on identity documents and information available through searches of primary data sources. The first step in this work involved translating the process described in the legislation into engineering documents. The Company diagrammed the legislative requirements for customer due diligence in Lucid Charts. Potential sources and methodologies were then identified for each potential screening check that could be performed in order to meet these legislative requirements. The Company’s software developers then used these diagrams to develop user stories, which are the smallest units of work in an agile framework. A user story details, in an informal way, the software features required from an end user’s perspective. The user stories were then used by the development team to divide work into functional increments that were planned into agile iterations. The Company developed new methods of creating compliance flags and warnings that had not previously existed by leveraging the big data sources available to ONBORD. These sources would not have been checked by a manual process and included IP location and website domain name information. As part of this work, the Company had to ensure that the individual for whom the compliance check was being performed was established as a unique individual, and that there were no false positives or false negatives due to similarities in names or other information. ‘Uniquing’ people required a number of different filters and strategies - it is actually a process that is running behind the system rather than just a one-off process as the Company realised that additional data could produce better results, but the system had to be able to attempt to do it even with minimal data. If identity documents were available after an optical character recognition (OCR) scan of the document, the system would have the middle name and date of birth to use as additional uniquing strategies. E-mail addresses and phone number data, where available, were also leveraged. The edge cases were the real issue: salutations may need to be considered, and the system needed to be able to distinguish between a parent and a child with the same name in a shareholder register, for example. In addition, the same person with a married and maiden name required adjustment in the code. All of this refinement needed to be done to provide the foundations of a robust system. The primary data sources used in the compliance checks are of the Office of Foreign Assets Control (OFAC) list, the United Nation’s sanctions list, the Interpol red list, the EU sanctions list, the UK sanctions list, the Company’s own PEP database developed from a number of other sources, and a Google News feed. The Company developed an automatic system process that ran on a routine basis to download and scrape the data from these primary data sources, to keep the information up to date. In order for the compliance check processes to be scalable, the Company developed the ONBORD system as a multithreaded application in which some of the processes that were part of the larger customer due diligence procedures ran as background processes. This work is still ongoing, as the format and nature of the primary data sources change over time, and new sources are added when available.”
“AI, algorithms, the data manipulation and database design may have been novel and difficult to achieve, however we have had no evidence that there has been an advance to any technologies in the process. According to para 12 of the BIS guidelines, “the routine analysis or adaptation of an existing product, process, service will not be an advance in science or technology”
“Where I stand currently is that the product produced by Get Onbord Ltd is impressive, but it does not meet para 6 of the guidelines based on the information I have. I believe the product produced, has used existing processes and technologies that were readily deducible to produce a new innovative product.”
“We can see from your letter, that your final decision is to refuse the claim. However, we feel that the R&D taking place is not being completely understood, which may be partly down to us trying to respond in a manner that is not too technical and in plain English. Machine learning software models relating to KYC is a highly innovative, rapidly changing and ground-breaking area. We are concerned that you consider new machine learning models/training that make an appreciable improvement in technology doesn’t qualify. We are also concerned about the wider precedent this sets for all other software technology claims.”
“[GOL’s project] is an ambitious goal which will require significant innovation in software development relating to multiple big data sources, robotic process automation and machine learning models. As a starting point, the Company leveraged existing technology where possible, to avoid developing software that did not need to be built. The Company then sought to make appreciable improvements to robotic software automation and machine learning models in order to meet the objectives of the project.”
“From the perspective of advancing the field of computer sciences, the Company initially developed tasks that could be automated based on their rule-based nature and incorporated structured digital data to support decisioning. These tasks were then assigned to a bot that performs them according to the overall master program (which the Company refers to internally as “the Metatron”). This master program is a set of algorithms that decide which information is required and must be requested from the end client or from various data sources to build the full picture of the individual or business and produce a comprehensive client file. The Metatron itself is composed of hundreds of smaller rules-based decisions that would be almost imperceptible to a human digesting the final client file, integrating hard data such as passport number and issuing country with less obvious things like IP address location and phone number.”
“The Company created a machine learning model where two multivariate decision trees were built for 1) fraud/compliance and 2) credit. They both start from the same data set but are run separately for each individual or business. These models are not static but must necessarily adapt as new data points are added to the system and changes are made in the environment and behaviour of bad actors. There were three main reasons for choosing decision trees over other machine learning models: A decision tree does not require normalisation of data Missing values in the data don’t affect the process of building a decision tree to any significant extent A decision tree model is very intuitive and easy to explain to clients; auditability was essential Decision tree models fall within the category of “white-box” techniques, which trade some predictive power for transparency, which is useful for explaining to a regulator. Most machine learning models tend to be “black-box” systems, such as neural networks, random forests, and gradient boosting, which are well-suited for finding patterns but are impractical to decipher. The Company continues to explore and experiment with both “white box” and “black box” models, with the aim of making incremental gains in predictive power while maintaining transparency. A hybrid machine learning model is also being considered.”
“Although this term [the ‘evidentiary burden of proof’] is widely used, it has often been pointed out that it simply expresses a notion of practical common sense and is not a principle of substantive or procedural law. It means no more than this, that during the trial of an issue of fact there will often arrive one or more occasions when, if the judge were to take stock of the evidence so far adduced, he would conclude that, if there were to be no more evidence, a particular party would win. It would follow that, if the other party wished to escape defeat, he would have to call sufficient evidence to turn the scale. The identity of the party to whom this applies may change and change again during the hearing, and it is often convenient to speak of one party or the other as having the evidentiary burden at a given time. This is, however, no more than shorthand, which should not be allowed to disguise the fact that the burden of proof in the strict sense will remain on the same party throughout – which will almost always mean that the party who relies on a particular fact in support of his case must prove it.”
“[30] The judge noted that the special commissioners had expressed their conclusion as to the central management and control of Eulalia (at paragraph 145 of their decision) in terms which suggested that they had based that conclusion on what they saw as the taxpayers' failure to discharge the onus which was placed upon them by section 50(6) TMA 1970. As the special commissioners had put it: “the Appellants have failed to satisfy us that the central control and management was not in London from18 July 1996 when CIL became its shareholder”
“[59] I wish to say more about the way in which the Commissioners have based their decision on what they see as the failure of Mr and Mrs Wood to discharge the burden of proving a negative. I accept, despite a submission of Mr Goldberg to the contrary, that, when an Inspector of Taxes makes an adjustment to a taxpayer's self-assessment and the taxpayer appeals against the adjustment, the statutory burden on appeal rests on the taxpayer to show that the adjustment is wrong. That is the effect ofs.50(6) of the Taxes Management Act 1970 : 'If, on an appeal, it appears to … the Commissioners … by evidence – (c) that the appellant is overcharged by an assessment … the assessment … shall be reduced accordingly, but otherwise the assessment … shall stand good.'“ But he went on (ibid): “ . . . However, there plainly comes a point where the taxpayer has produced evidence which, as matters stand then, appears to show that the assessment is wrong. At that point the evidential basis must pass to the Revenue.”
“[63] . . . in so far as the Commissioners decided this appeal against Mr and Mrs Wood on grounds relating to the burden of proof (and the opening part of paragraph SC145 suggests that those were the critical grounds for the decision), I consider that they were in error.”
“[60] In this case, at the beginning of the appeal before the Special Commissioners the position was that the Revenue had made an adjustment on the basis that Mr and Mrs Wood were liable to CGT, and that Mr and Mrs Wood had to show to the civil standard of proof that the adjustment was wrong. I accept that the onus was on them to show that Eulalia was not resident in the United Kingdom, but rather was resident in the Netherlands. [Park J then set out what Mrs and Mrs Wood had shown and went on] Surely at that point they can say: 'We have done enough to raise a case that Eulalia was not resident in the United Kingdom. What more can the Special Commissioners expect from us? The burden must now pass to the Revenue to produce some material to show that, despite what appears from everything which we have produced, Eulalia was actually resident in the United Kingdom.” [32] As the judge pointed out, the revenue had produced no positive material to show where the central control and management of Eulalia was. It was not enough (as the judge thought) for the revenue to criticise the lack of evidence from some of those at Price Waterhouse and ABN AMRO who had been involved in the transaction in 1996. The special commissioners had said that they would not have been assisted, to any material extent, by oral evidence of events then some seven years in the past. Nor was it enough to demonstrate, as counsel for the revenue had done convincingly, “that the steps taken were part of a single tax scheme, that there were overall architects of the scheme in Price Waterhouse, and that those involved all shared the common expectation that the various stages of the scheme would in fact take place”
“[69] In the reports submitted to HMRC and in its case before us, the Appellant through Optimal Compliance made assertions as to the aim of the project and as to the technology it had sought to develop to achieve the project’s aims (and why it said that constituted an advance in technology). The Appellant, through Optimal Compliance, also referred to a number of uncertainties that it said it faced and how it had sought to overcome them. However, to meet the burden on it, the Appellant needed provide evidence that proved: (1) the technology it sought to develop was not already readily available; (2) the technology it sought to develop to achieve the project’s aims amounted to an advance in technology within the meaning of the Guidelines and, specifically that it amounted to more than “routine…copying or adaptation of an existing product [or] process…”; and (3) that there were technological uncertainties which a competent professional working in the field could not have readily resolved. … [73] We find it remarkable that the Appellant did not provide evidence from someone that was contemporaneously involved in the project (such as Mr Jones or Mr Philby) and/or from someone with relevant expertise who, having reviewed records of the project, might have been able to address the issues set out at paragraph 69 above preferably by reference to supporting materials.”
“In order to satisfy the burden of proof, the Appellant would have needed to provide witnesses who could have testified to the facts necessary for me to conclude that the criteria set out in the Guidelines were satisfied and who could then have been subjected to cross-examination by the Respondents. In the absence of that, I am unable to conclude that, on the balance of probabilities, the expenditure in question satisfied the relevant criteria.” [66] In his skeleton argument, he said that the term “competent professional” is not defined, but that: “its natural meaning is self-explanatory, and that it goes beyond having an intelligent interest in the field…to be accepted as a competent professional, an individual would need to be able to demonstrate appropriate qualifications, experience and up-to-date knowledge of the relevant scientific and technological principles involved.” [67] He submitted that neither Mr Wells nor Mr Herbert was a competent professional. FTP’s claim related to the digitisation of FTP’s archive and making that archive available to users; in that context a competent professional would have up-to-date software, programming and computing skills and knowledge. Instead, Mr Wells was a publisher with some familiarity with computing, but no IT qualifications; he was plainly not a professional in that field. Mr Herbert was familiar with using computers but not a professional in the fields of programming or software development. As a result, said Mr Lewis, FTP could not show that these key provisions in the Guidelines were met.”
“[The Appellant] has failed to show that the Project had either (a) resolved uncertainties which could not have been resolved by a competent professional or (b) made an improvement which a competent professional would have acknowledged as being “non-trivial”