33269A data-driven 'knowledge engine' for financial analysisClosedCollaborative R&DInnovate UKInvestment into companies is a fundamental driver of economic growth and competitiveness, helping to foster innovation and improve productivity, however research shows that 60% of M&A deals actually destroy value. The global M&A market is valued at ~$4 trillion, meaning ~$2.4 trillion is wasted. This can be attributed to poor quality data and unscientific analysis in due diligence, with analysts unable to accurately assess market risks and opportunities, resulting in overestimated deal synergies and valuations. Financial analysis is hampered by the need to analyse and extract information from multiple, hard to parse, heterogeneous data sources (company websites, research reports, financial statements, social media, etc.), which requires human analysts to sift through thousands of documents and manually crunch numbers. A lack of sophisticated tools and data science expertise means financial analysis remains unsophisticated and error-prone. The whole process is time and cost intensive and ultimately unable to scale given exponentially increasing amounts of web data. To address this challenge, Plural AI has developed a first version (Minimal Viable Product) of a data science platform for financial analysis, using natural language processing, machine learning and knowledge graph technology, which is capable of generating potential acquisition targets or estimating the size of a market. The proposed project will build upon this early achievement to develop a fully automated and scalable 'knowledge engine' specifically for financial analysis. Unlike traditional search engines, which match keywords to available third-party results, Plural AI's knowledge engine adopts a computational approach by parsing the question and generating a bespoke answer, which can be audited for sources and workings. This allows users to answer very niche/complex questions to which answers currently do not exist. This project focusses on proving the feasibility of fully automating the concept mining and knowledge base creation, enabling the engine to handle a wide array of financial queries at full web scale. This involves automatically determining which sources are trustworthy, and generating entities, concepts, and links of interest - thus providing a fully scalable, disruptive solution for improved financial analysis. The initial target application is the corporate finance industry, in particular for deal origination use cases, where need and interest has been established; however strong transferability is envisaged to any research/analysis work. Benefits include improved, rapid investment decision making, leading to increased investments and optimised returns; significant cost and time savings; ultimately bringing robust, scientific decision-making to the finance market.