The two year project will enable Portafina, a specialist pension advice firm targeting the \u201cadvice gap\u201d mass market, to apply state-of-the-art machine learning techniques to improve workflow and data processing. This will enable Portafina to increase their efficiency, reduce timescales and enable rapid scalability.<\/p>\n
The partnership are looking forward to recruiting a recently qualified postgraduate, known as the Associate, to deliver the strategic project. The Associate will be based at Portafina\u2019s offices in Rochester and work under the guidance and support of a team of \u00a0academics from the University\u2019s School of Computing. The academics will be bringing a wealth of expertise in a number of areas including developing algorithms for machine learning and text mining tasks, and techniques in natural language processing.<\/p>\n
Jamie Smith-Thompson, MD of Portafina, said: ‘We are very excited to be working with the University of Kent on a number of innovative projects that will have a real impact on our efficiency. Our intention is to make it possible to provide much-needed financial advice to a wider mass-market client base by reducing our back-office costs. We are confident these projects will also enable us to increase service quality at the same time. We can\u2019t wait to get started.’<\/p>\n
Carole Barron, Director of Kent Innovation and Enterprise believes that Knowledge Transfer Partnerships are one of a range of ways in which the University\u2019s academic experts link up with national and local businesses to offer practical advice based on their research.<\/p>\n
Carole said: ‘We are delighted to be entering into this exciting collaboration with Portafina in the area of state-of-the-art machine learning techniques. We look forward to developing a long relationship with Portafina beyond the two year partnership.’<\/p>\n","protected":false},"excerpt":{"rendered":"
The two year project will enable Portafina, a specialist pension advice firm targeting the \u201cadvice gap\u201d mass market, to apply state-of-the-art machine learning techniques to … Read more<\/a><\/p>\n","protected":false},"author":74795,"featured_media":1440,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[282613,13897,181941,9112,228278],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/posts\/1439"}],"collection":[{"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/users\/74795"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/comments?post=1439"}],"version-history":[{"count":1,"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/posts\/1439\/revisions"}],"predecessor-version":[{"id":1441,"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/posts\/1439\/revisions\/1441"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/media\/1440"}],"wp:attachment":[{"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/media?parent=1439"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/categories?post=1439"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.kent.ac.uk\/ris\/wp-json\/wp\/v2\/tags?post=1439"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}