Financial crime vaccines
Lead Participant:
FINCRIME DYNAMICS LTD
Abstract
Trustworthy AI is a set of principles and practices to ensure that AI systems are transparent, unbiased, and reliable. In financial crime analytics, AI-based solutions are increasingly used to detect fraudulent activities. However, using AI in financial crime analytics raises concerns about models' transparency, bias, and robustness. We implemented trustworthy AI concepts into our solution called Synthetizor, a tool for creating and deploying financial crime vaccines.
The use of trustworthy AI in financial crime analytics is important because it can help improve the solutions' effectiveness and increase the trust of financial institutions and regulators in the solutions. Trustworthy AI can help to ensure that the models used to detect and prevent financial crime are transparent, unbiased, and robust and that the decisions made by the models are interpretable and explainable. It can increase the accuracy of the models, reduce the risk of false positives and negatives, and ensure compliance with regulations.
Building a consortium for financial crime vaccines is an exciting opportunity for potential partners from academia and industry to collaborate and tackle a pressing problem collaboratively and innovatively. By working together, partners can leverage their strengths and expertise to develop cutting-edge solutions that address the specific needs of financial institutions.
The project aims to conduct a feasibility study for building a consortium of partners from academia and industry to address the problem of financial crime using financial crime vaccines. The study will evaluate such a consortium's potential benefits and challenges, including the impact, required resources, and risks.
We are building a consortium for trustworthy AI in financial crime on a study case from a pioneer organisation that is currently trying the first financial crime vaccine for automated push payments fraud. By studying a pioneer organisation's experience with implementing AI solutions using financial crime vaccines, the consortium can learn about the challenges and opportunities specific to the field and the best practices to improve the performance and trustworthiness of the solutions. We also have expressed interest from three universities and several financial institutions in the UK.
The project's outcome will be a feasibility study that assesses the potential benefits and challenges of building a consortium to address the problem of financial crime using trustworthy AI. The study will recruit potential consortium partners and work with them to identify the problem and possible solutions.
The use of trustworthy AI in financial crime analytics is important because it can help improve the solutions' effectiveness and increase the trust of financial institutions and regulators in the solutions. Trustworthy AI can help to ensure that the models used to detect and prevent financial crime are transparent, unbiased, and robust and that the decisions made by the models are interpretable and explainable. It can increase the accuracy of the models, reduce the risk of false positives and negatives, and ensure compliance with regulations.
Building a consortium for financial crime vaccines is an exciting opportunity for potential partners from academia and industry to collaborate and tackle a pressing problem collaboratively and innovatively. By working together, partners can leverage their strengths and expertise to develop cutting-edge solutions that address the specific needs of financial institutions.
The project aims to conduct a feasibility study for building a consortium of partners from academia and industry to address the problem of financial crime using financial crime vaccines. The study will evaluate such a consortium's potential benefits and challenges, including the impact, required resources, and risks.
We are building a consortium for trustworthy AI in financial crime on a study case from a pioneer organisation that is currently trying the first financial crime vaccine for automated push payments fraud. By studying a pioneer organisation's experience with implementing AI solutions using financial crime vaccines, the consortium can learn about the challenges and opportunities specific to the field and the best practices to improve the performance and trustworthiness of the solutions. We also have expressed interest from three universities and several financial institutions in the UK.
The project's outcome will be a feasibility study that assesses the potential benefits and challenges of building a consortium to address the problem of financial crime using trustworthy AI. The study will recruit potential consortium partners and work with them to identify the problem and possible solutions.
Lead Participant | Project Cost | Grant Offer |
---|---|---|
FINCRIME DYNAMICS LTD | £49,994 | £ 49,994 |
People |
ORCID iD |
Edgar Alonso Lopez Rojas (Project Manager) |