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Computing and automation play an increasingly important role in finance and significantly affect the ways in which financial markets operate. Economic and financial incentives play an important role in conventional distributed systems technology, such as the internet protocols.

The Finance Hub works at the intersection of finance and computation, using a range of techniques to advance understanding of computation's role in finance and vice-versa.

Techniques include algorithm design, game theory, network analysis, scientific and high-performance computing, time-series analysis and agent-based modelling. Problems the hub tackles include market micro-structure, risk management, portfolio construction, and the design of crypto-finance and distributed-ledger technology.

The Hub has established collaborations with other departments at King's, such as the Department of Mathematics and the King's Business School, as well as key financial institutions, including the Financial Conduct Authority.

Research Questions

The Finance Hub carries out fundamental and applied research in computational finance and economics. Our current key research directions are:

  • solving financial problems with machine learning and AI techniques;
  • incentives and mechanism design in financial markets;
  • blockchain and decentralised finance;
  • detecting systemic risk and clearing of financial networks.

People

Hannah Cao

Senior Lecturer in Computer Science Education

Hubie Chen

Senior Lecturer

Hana Chockler

Professor of Computer Science

Bart De Keijzer

Senior Lecturer in Computer Science

Yali Du

Senior Lecturer in Artificial Intelligence

Karine Even-Mendoza

Lecturer in Systems & Programming Languages

Themes

435x250px-artificial-intelligence-3382507_1920
AI in finance

Our hub is world-leading in the adoption of AI in finance. AI tools have been developed, and often successfully deployed, for many tasks in different application domains. We work on a variety of tasks in the application domain (including trading, risk management, market making, and derivative pricing) and adapt and implement novel AI machinery encompassing adversarial reinforcement learning, generative AI, deep neural networks, and advanced mathematical tools such as path signatures and the intrinsic time framework. We also explore areas within finance which encourage the use of AI due to the need to process large amounts of data, including Environmental, Social, and Governance (ESG) papers, a line of work in collaboration with King’s Business School and the Department of Mathematics.

    Coloured coins network
    Incentives in finance

    Trustworthiness of artificial intelligence is vital to central banks and regulators. We build incentive-aware digital twins for finance - realistic simulation platforms that allow operators to experiment with financial markets "in the lab". We also aim to incorporate symbolic AI tools coming from game theory to model and account for the incentives of agents in the market, by combining agent-based modelling and empirical game-theoretic analysis. This approach aims to tackle various issues on market design. We also explore the herding effect in trading, and its consequences on flash crashes, as well as the systemic properties of complex systems, where risk in financial networks is a particularly prominent example here. Building on some of the theoretical work we have done, we also account for suboptimal decision making (caused by e.g. irrational behaviour) in the analysis.

      Graph of exchange rates
      Blockchain and decentralised finance

      Within the hub, we have knowledge of the protocols currently adopted and tools for their analysis and design (a.k.a., “layer 1” blockchain). We are also studying “layer 2” questions including the extent of liquidity provision, maximum extractable value, and transaction fees that are influenced by the way a market is designed.

        Graphic of various graphs
        Detecting and preventing systemic risk in finance

        Will the default of one financial institution, or bank, cascade to other banks in a financial system? Can a bank’s exposure to such a contagion risk be lowered by pardoning debts or donating assets to other banks? These example questions are paramount to the study of systemic risk in finance. In this context, financial networks have emerged as the framework of reference. A central computational challenge for financial networks is the clearing problem, which amounts to computing the payments that were to be made among financial institutions if all outstanding debts in the financial system had to be cleared at once. Such payments reveal each institution’s exposure to systemic risk in the network. The hub investigates the computational challenges involved in solving the clearing problem, and attempts to find qualitative insights into the way clearing should proceed in a financial interbank system in challenging scenarios (e.g. after a financial shock in the presence of a financial crisis).

          Publications

          News

          New tool analyses huge amounts of data at record speeds

          New tool analyses huge amounts of data at record speeds The algorithm can spot trends in hundreds of millions of data points in less than 20 minutes.

          Connected points on a graph with finger point

          Three King's scientists win prestigious New Investigator Awards

          Three King’s academics have been awarded funding from EPSRC to establish research groups.

          Dr Chiara Gattinoni, Dr Micaela Matta and Dr Bart De Keijzer

          New software detects money laundering faster than ever before

          The tool can isolate suspicious and potential criminal behaviour three times more effectively than conventional methods.

          780x440-shutterstock_2191167101

          KBS-Mathematics-Informatics Research Discussion on Climate Finance

          Virtual meeting held to discuss research collaboration in the field of climate finance.

          King's flag London

          Events

          11Apr

          AI and Finance - Collaborative Frontiers

          Join this workshop exploring the transformative potential of AI in financial systems.

          Please note: this event has passed.

          PhD research

          We welcome postgraduate research students from around the world to join our world-leading community of researchers. PhD students play a vital role in advancing innovation and cutting-edge discovery across our areas of research.

          A young women works on two computers while a robotic device sits next to her.
          PhD research

          We offer an exciting research environment where PhD students can pursue their own projects in a variety of fields including cyber security. The Department of Informatics invites applications for postgraduate research students for funded and self-funded projects starting in October each year. 

          People

          Hannah Cao

          Senior Lecturer in Computer Science Education

          Hubie Chen

          Senior Lecturer

          Hana Chockler

          Professor of Computer Science

          Bart De Keijzer

          Senior Lecturer in Computer Science

          Yali Du

          Senior Lecturer in Artificial Intelligence

          Karine Even-Mendoza

          Lecturer in Systems & Programming Languages

          Themes

          435x250px-artificial-intelligence-3382507_1920
          AI in finance

          Our hub is world-leading in the adoption of AI in finance. AI tools have been developed, and often successfully deployed, for many tasks in different application domains. We work on a variety of tasks in the application domain (including trading, risk management, market making, and derivative pricing) and adapt and implement novel AI machinery encompassing adversarial reinforcement learning, generative AI, deep neural networks, and advanced mathematical tools such as path signatures and the intrinsic time framework. We also explore areas within finance which encourage the use of AI due to the need to process large amounts of data, including Environmental, Social, and Governance (ESG) papers, a line of work in collaboration with King’s Business School and the Department of Mathematics.

            Coloured coins network
            Incentives in finance

            Trustworthiness of artificial intelligence is vital to central banks and regulators. We build incentive-aware digital twins for finance - realistic simulation platforms that allow operators to experiment with financial markets "in the lab". We also aim to incorporate symbolic AI tools coming from game theory to model and account for the incentives of agents in the market, by combining agent-based modelling and empirical game-theoretic analysis. This approach aims to tackle various issues on market design. We also explore the herding effect in trading, and its consequences on flash crashes, as well as the systemic properties of complex systems, where risk in financial networks is a particularly prominent example here. Building on some of the theoretical work we have done, we also account for suboptimal decision making (caused by e.g. irrational behaviour) in the analysis.

              Graph of exchange rates
              Blockchain and decentralised finance

              Within the hub, we have knowledge of the protocols currently adopted and tools for their analysis and design (a.k.a., “layer 1” blockchain). We are also studying “layer 2” questions including the extent of liquidity provision, maximum extractable value, and transaction fees that are influenced by the way a market is designed.

                Graphic of various graphs
                Detecting and preventing systemic risk in finance

                Will the default of one financial institution, or bank, cascade to other banks in a financial system? Can a bank’s exposure to such a contagion risk be lowered by pardoning debts or donating assets to other banks? These example questions are paramount to the study of systemic risk in finance. In this context, financial networks have emerged as the framework of reference. A central computational challenge for financial networks is the clearing problem, which amounts to computing the payments that were to be made among financial institutions if all outstanding debts in the financial system had to be cleared at once. Such payments reveal each institution’s exposure to systemic risk in the network. The hub investigates the computational challenges involved in solving the clearing problem, and attempts to find qualitative insights into the way clearing should proceed in a financial interbank system in challenging scenarios (e.g. after a financial shock in the presence of a financial crisis).

                  Publications

                  News

                  New tool analyses huge amounts of data at record speeds

                  New tool analyses huge amounts of data at record speeds The algorithm can spot trends in hundreds of millions of data points in less than 20 minutes.

                  Connected points on a graph with finger point

                  Three King's scientists win prestigious New Investigator Awards

                  Three King’s academics have been awarded funding from EPSRC to establish research groups.

                  Dr Chiara Gattinoni, Dr Micaela Matta and Dr Bart De Keijzer

                  New software detects money laundering faster than ever before

                  The tool can isolate suspicious and potential criminal behaviour three times more effectively than conventional methods.

                  780x440-shutterstock_2191167101

                  KBS-Mathematics-Informatics Research Discussion on Climate Finance

                  Virtual meeting held to discuss research collaboration in the field of climate finance.

                  King's flag London

                  Events

                  11Apr

                  AI and Finance - Collaborative Frontiers

                  Join this workshop exploring the transformative potential of AI in financial systems.

                  Please note: this event has passed.

                  PhD research

                  We welcome postgraduate research students from around the world to join our world-leading community of researchers. PhD students play a vital role in advancing innovation and cutting-edge discovery across our areas of research.

                  A young women works on two computers while a robotic device sits next to her.
                  PhD research

                  We offer an exciting research environment where PhD students can pursue their own projects in a variety of fields including cyber security. The Department of Informatics invites applications for postgraduate research students for funded and self-funded projects starting in October each year.