Financial Data Analytics
We have just started a new commercial research project working with our partners, Tungsten to develop a fully functional state-of-the art spend analytics system.
At the heart of spend analysis is the general problem of forming an accurate, detailed semantic understanding of items from the raw text information that is available to the system (e.g. product descriptions). This data must be analysed using the existing knowledge base; there may, however, sometimes not be enough current 'context' to unambiguously 'understand' this data; in such circumstances it may be necessary to enrich information via additional user interaction and/or web spidering. To help solve such semantic issues there is scope for application of new AI techniques; for example, deep learning and reservoir computing and the newly emerging area of quantum linguistics. Learning algorithms for classification based on clean data Access to our partner's massive database opens up new opportunities to research state-of-art machine learning techniques (e.g. deep learning reservoir computing; echo-state networks) which potentially could also offer a significant improvement in classification performance.
Ontologies are structural frameworks for organising information and are used in artificial intelligence as a form of knowledge representation about the world (or some part of it). An ontology formally represents knowledge as a set of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts. Automatically developing contextually sensitive ontologies will significantly improve the classification system.
To explore our partner's database to identify economic trends in purchasing via the application of advanced machine learning techniques; the expectation is that with access our partner's huge database, new learning algorithms could be trained to make commercially useful time-series predictions (e.g. to highlight strategic opportunities for investment etc.).