Three quarters of SMEs provide trade credit. They provide the goods or services upfront and get paid later. However, many SMEs have customers that pay them late or not at all. Such 'late payment' is a major problem which increases costs, reduces cash flow, and consumes funds that could otherwise be used to drive business growth. In the extreme, it can force a viable business to close. For the SMEs who are often buying these goods or services, accounts payable is particularly time-consuming, requiring significant time spent in manual data entry. Without rigorous systems in place, overdue invoices get missed and late payment can escalate. Business accounting software provides an accurate and efficient solution for businesses to manage their finances, but it requires much keying of data. Information will come in many formats, both paper and electronic, from which someone needs to identify the essential supplier, address, amount and, crucially, the due-date. Artificial intelligence (AI) tools can read the paperwork with over 99% accuracy and eliminate the need for everything to be manually checked. This saves time and money which could be better spent doing productive activity, and facilitates the timely payment of invoices. This trial tests an idea to speed up mainstream adoption of AI. We believe most SME owners would rather be working on their business than keying in their accounting paperwork. But we understand the natural reluctance to change without proven demonstration of impact within their particular business. Hence we provide a prolonged free trial of the software. This should tackle the cost barriers. And we intend to hold the hand of SMEs through the learning barriers by providing tailored support, webinar masterclasses, accounting advice and tips to make it easy to integrate Evolution AI into bookkeeping practice. We compare this to the effectiveness of a more basic self-service model, where the SME is provided the Evolution AI product for free (but not the 'hand-holding' support package). That is the control intervention of the trial. It remains a financially attractive way to use Evolution AI, but we suspect it will not be as effective at stimulating SMEs to make the change. After the free trial period we have a second experiment that tests SMEs' willingness to pay for continued usage of what we believe will be a demonstrated cost-saver. Such preparedness to commit the SME's own money to the technology is the acid-test of innovation adoption.

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Many businesses hold vast volumes of semi-structured information in the form of business documents (e.g. contracts, invoices, delivery notes, payslips) and financial documents (e.g. compliance statements, regulatory filings, accounts) as scanned images or pdfs. These complex documents are not easily processed or 'understood' by technologies available today, including Natural Language Processing (NLP), a type of machine learning, and Optical Character Recognition (OCR). In this project we propose to develop a product to extract information accurately from semi-structured documents containing forms and tables, obviating the need for manual keying by human beings. Accuracy rates for such use-cases are currently very low, ~87%, well below human cognitive capability. The partners propose to solve this problem using a disruptive 'big data' approach exploiting a new class of algorithms which has recently emerged from the research base. Our project is a highly innovative industrial R&D collaboration. It is led by between Evolution AI, a UK SME which provides products that read and understand human language autonomously; backed by Evolution's customer (Dun & Bradstreet), the global market information and data solutions provider; and a world-leading university (University of Warwick, WMG Data Science Group). We will build on Evolution AI's capability in NLP and its proprietary big data processing architecture, along with WMG's expertise in deep learning and computer vision and Dun & Bradstreet's data, to develop new algorithms based on deep artificial neural networks, culminating in a market-ready product. Our innovation provides dramatic opportunities to make efficiencies in back-office processing, introduce automation and improve productivity; and also to capture new, intelligent knowledge at scale. We expect our product to create significant productivity gains for Evolution AI's customers and to help accelerate its growth into an export market worth $3.4 - $4.5Bn by 2023 growing at an estimated 16% - 38% per year. This will create at least 42 new jobs and an ROI to the public purse of approximately 7-12 times investment by 2023\. Evolution AI was incorporated in 2015 and provides five core capabilities: 1\. Information extraction; 2\. Semantic matching; 3\. Deep categorisation; 4\. Autonomous web crawling; 5\. Natural language generation. Evolution AI's proprietary Natural Language Processing (NLP) algorithms exploit state-of-the-art advances in deep learning technology using neural networks, running on our high-performance compute architecture. Its clients are major blue-chip organisations in banking, insurance, professional services, government, health, retail and media.

In April of 2016 the European Parliament released the General Data Protection Regulation (GDPR) in which any individual subject to automated profiling has the right to “meaningful information about the logic involved." This puts the onus on machine learning software to not only replicate human performance, but also to translate algorithmic complexity into human interpretable reasoning. Evolution AI have been building machine learning software for large enterprises for two years and our experience is that users will distrust or ignore machine learning tools which do not provide sufficient justification for their decisions, irrespective of their accuracy. Innovate UK have collected significant volumes of operational data. We will use this data to build a machine learning solution to automate a chain of human specific tasks. Our solution will read applications, match assessors to applications and provide readable justification for the proposed assessors in keeping with the rights outlined in the GDPR . This novel technology is only possible due to the latest advances in deep learning (a subfield of machine learning). By building on work resulting from a collaboration with University College London (UCL) we will develop two new algorithms to improve the operational efficiency of Innovate UK by decreasing the time taken to allocate assessors to an application.