Unlocking Potential with R&D Tax Credits for Mathematics: Changes and Opportunities
Introduction
Pure mathematics is often viewed as an abstract and theoretical field. However, its impact reaches far beyond academia. In fact, pure mathematics plays a crucial role in driving innovation and technological advancements across various industries. By recognising the value of pure mathematics in research and development (R&D), businesses can leverage R&D Tax Credits for mathematics to support their groundbreaking projects which can potentially advance their prospective fields in science and technology.
Changes in DSIT Guidelines
Prior to 1 April 2023, claiming R&D Tax Credits for mathematics was not an option – i.e. there was a limitation preventing mathematics cost from being included in R&D Tax Credit claims. Recently, the Department for Science, Innovation & Technology (DSIT) guidelines have been updated to clarify that activities relating to pure mathematics can now be considered as a category of qualifying costs and are eligible for R&D Tax Relief for accounting periods starting on or after 1 April 2023.
What Does Pure Mathematics Involve?
Pure mathematics involves the exploration of new mathematical concepts, the development of new theories, and the discovery of new mathematical relationships and patterns. This includes the development of new mathematical models, the study of mathematical structures and symmetries and the study of the foundation of mathematics.
Whilst pure mathematics is not developed with a specific practical applications in mind, it often provides foundation for many applied areas of science, engineering and software.
Applications of Pure Mathematics
Engineering
- Engineering design, simulations and optimisation, including ordinary differential equations (ODEs), partial differential equations (PDEs), and their numerical solution methods like finite difference (FDM), finite element (FEA) and finite volume methods (FVM).
- Engineering Control systems including, feedback control systems, stability analysis, pole placement, robust control, or optimal control.
- Signal processing using Fourier analysis, wavelet analysis, and statistical signal processing.
- Reliability and risk analysis using probability theory, statistical modelling and uncertainty qualification.
- Electrical design and circuits using partial differential equations (PDEs), fourier analysis, and probability theory.
- Robotics and automation using linear algebra, differential calculus, Jacobians and their derivatives, Bayesian filtering, probability distributions and statistical models to analyse and reduce noise.
Computing and software:
- Algorithm development, for example, numerical algorithms using linear systems, optimisation problems and interpolation.
- Cryptography and data security using number theory, algebra, probability theory and statistics.
- Machine learning and Artificial Intelligence using linear algebra (matrix operations, eigenvectors, singular value decomposition (SVD), and dimensionality reduction techniques), calculus principles (gradient descent optimisation, backpropagation algorithm, or optimisation of neural network architectures) and probability theories.
- Software optimisation using constrained, nonlinear and/or metaheuristic optimisations, numerical stability and efficiency.
- Parallel computing using parallel algorithms, parallel processing architectures, and distributed computing.
- Quantum computing including, quantum states, gates and measurements, analysing quantum systems using wavefunctions and probability amplitudes, and contour integration using residue calculus, and quantum field theories.
Healthcare:
- Medical Imaging reconstruction and segmentation using fourier transform, tomographic reconstruction, image deconvolution.
- Biomedical signal processing using wavelet transforms, fourier analysis, or spectrograms to analyse and extract features from biomedical signals such as electrocardiograms (ECG), electroencephalograms (EEG), or electromyograms (EMG).
Pharmaceuticals:
- Drug formulation, delivery and simulating drug profiles using partial differential equations, statistical modelling and numerical modelling.
- Pharmacokinetic Modeling and Simulation using partial differential equations to model drugs absorption, distribution, metabolism, and excretion in the body.
Claiming R&D Tax Relief
Revenue expenditure in several cost categories is eligible for R&D tax relief. These include:
- Staffing Costs
- Subcontractors
- Externally Provided Workers (EPW’s)
- Consumables
- Software licenses
- Utilities (Heat, light, water, fuel)
- Data sets
- Cloud computing
Conclusion
As businesses explore the integration of pure mathematics into their R&D process, consulting with tax advisors and experts familiar with the R&D Tax Credit guidelines is crucial. Innovation Tax and its team of tax and technical professionals have extensive experience in helping businesses claim R&D Tax Credits for mathematics. We ensure your claim is robust, maximised, and fully compliant with the prevailing legislation.
Contact our team of specialists to discuss how we can support your business with not only R&D Tax Credits but other incentives such as Capital Allowances, Patent Box and Grant Funding.