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Fintech and Beyond


Digitalisation of financial transactions has both commercial and regulatory implications and it impacts all aspects of capital markets from investments, transactions, settlements and fundraising. The core objective of FinTech is to enhance operational capabilities, improve user experience & reduce the cost base of the industry.

The University of Manchester excels at interdisciplinary research; we also have more graduates working in the emerging FinTech sector than any other UK Higher Education Institution (for reference see below). FinTech, Data Analytics & Cyber Security have been identified as key strategic areas for applied research across faculties.

Law and Technology

As with FinTech, legal services transactions are seeing innovations in automation, from text-mining and interrogation of case documents to machine-learning and the advent of so-called robo-lawyers. But what does this mean for incumbent providers? What will be the impacts on clients? How does the sector meet future talent needs?

As well as advancing development and application of the technologies themselves, academic research & consultancy projects can help address key business questions such as these. We have active projects with industry partners involving academics from our schools of Business, Computer Science, Maths and of course Law. Our School of Law is also undertaking a curriculum re-development to introduce LegalTech teaching, so that our future graduates will be ahead of the curve.

Read more about our Law, Technology & Money initiatives on the School of Law webpages.


A brief overview of applied research across The University with specific relevance to financial and professional services.

  • Credit Risk and Market Risk: credit rating, asset pricing, risk model validation, securitization, syndicated loans, forecasting
  • Cryptocurrencies & Blockchain in FinTech and their impacts on the wider economy (CS, Maths, Law, SoSS)
  • Compliant and Secure Data Management in FinTech (CS, SoSS, Law)
  • Data Analytics, Machine Intelligence & Mobile Security (CS, AMBS)
  • Data mining approach to pricing decision support (AMBS, CS)
  • Equity forecasting including prediction of long term stock price movement using machine learning (CS)
  • Financial Regulation: impact of regulation on risk taking, systemic risk and financial stability, regulatory capture, market manipulation, financial crime, regulation of credit rating agencies (AMBS)
  • Behavioural Finance: estimation of preferences, risk-on / risk-off patterns, loss aversion, corporate governance, responsible investment (AMBS, Maths)
  • Data Privacy, Authentication and Transparency (CS, SoSS, AMBS)
  • Smart Contracts (CS, Law)
  • Market impacts of new technology on the Financial Sector (AMBS, SoSS)
  • Social impacts of FinTech (AMBS, SoSS)
  • Regulatory impacts of new technologies in financial services (AMBS, Law)

Key Researchers