FinTech has a reputation for being a hard area for R&D claims primarily because of the requirement to demonstrate an advance in a field of science and technology.
Despite this, we have made a number of successful claims by identifying the technical advances and challenges with the underlying software modelling or means of software deployment.
Our principals have successfully filed over 2000 claims across a range of sectors and we can quickly work with you to identify the potential for any claim.
What you need to know:
How Long Does It Take to make a claim?
We find that many of our FinTech clients are concerned about the length of time that key staff will be required to provide detailed explanations of their complex work.
We minimise the time required often to less than 30 minutes by detailed analyst preparation. For example, for a recent Actuarial Modelling claim our analyst (a PhD in the use of neural network modelling technologies) read 7 keys research papers including a 71-page research report written by the world leader in the field of the claim – thus minimising the involvement of the senior actuary at the claimant company.
Sensitivity of proprietary (or client funded) algorithms
Despite the fact that the R&D claim is a confidential HMRC report many FinTech clients are wary about making a claim for fear of disclosing a sensitive proprietary algorithm.
In some cases, the technology may have been subsidised by one of their clients and they have strict Non-Disclosure agreements in place.
We are skilled at asking technical questions in such a way as to reveal sufficient information for us to be able to document the required advance in science and technology for HMRC, without revealing commercially sensitive information.
Unlike many unregulated R&D Tax credit consultants we are also Chartered Accountants and Chartered Tax Advisors and thus bound by strict regulations regarding client confidentiality.
Patenting of Software for the implementation of financial services and Patent Box taxation saving
Patenting of software for the implementation of financial services is something that is much harder in the UK than Europe (and far harder than the USA where it has been historically much easier to patent such developments). This difficulty together with the need for public disclosure has meant that historically there has been little interest in the UK for FinTech firms to attempt to patent their technology developments.
With the considerable taxation benefits of Patent Box as well as the growth in the number of early stage FinTech companies seeking VC investment, there is a growing interest in the field of patenting software solutions for the implementation of financial services.
Given the highly specialised nature of this field, even with our experience of drafting and filing patents for UK applications we do not directly advise clients on patentability in this field – instead, we refer them to a Patent Attorney who is a specialist in this complex area.
However, for those rare but increasing number of cases where a company has been successful in obtaining a granted UK (or certain European countries) patent in the software implementation of a financial service we are able to offer specialist Patent Box taxation advice.
We have experience of acting as Patent Box advisors to Fintech companies both under the notional tax royalty scheme and for infringement/damages income for the licensing of their patented technology.
Areas where May Figures has made successful claims:
- Cloud deployments (Financial service solutions (overcoming security concerns and appreciably improving workflow efficiency)
- Data Contract Synchronisation (Regulatory compliance efficiency improvements)
- Development of Financial Document Management Solutions (Improving the efficiency of cash collection)
- Data Communications between disparate financial services (TOMS, TradeWeb, ECNs)
- Test and Development deployment architectures (Improving the efficiency of the development of novel financial service solutions by means of Behavioural Driven Development software)
- Real-time financial data streaming (Kaazing integration)
- Actuarial data modelling (Parodi / Peril based / Monte Carlo Methods)
- Machine learning algorithms (Neural Networks for financial modelling)
- Data Communications in Supply Chain Financing
- Architectures for real-time trading (Client orientated architectures)