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Showing 211 to 220 of 771 search results for identification of poor.
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Four individuals face fraud charges
FCA starts criminal proceedings against 4 individuals for conspiracy to commit fraud and for conspiracy to carry out regulated activity without authorisation. -
Fast-growing firms (FGFs) multi-firm review
We summarise our findings and set out our expectations of FCA solo-regulated fast-growing firms to identify, assess and manage the risks arising from their activities. -
FCA business plan and risk outlook published
The Financial Services Authority (FSA) has published the business plan and risk outlook for the Financial Conduct Authority (FCA) for 2013/14. The FSA will be replaced by the FCA and the Prudential Regulation Authority (PRA) on 1 April 2013. -
A credit market that delivers for consumers
Speech by Roma Pearson, Director of Consumer Finance, delivered at Credit Summit 2023. -
Global regulation in the post-crisis era
Speech by John Griffith-Jones, Chairman, FCA, delivered at the TheCityUK Annual Conference on 30 June 2016. -
Financial promotions data 2022
The FCA publishes data on the number of financial promotions that it has taken action on to mitigate non-compliance with the FCA's rules. This data is for January 2022 to 31 December 2022. -
FCA performance scorecard - comparison metrics for personal current accounts 2020
This performance scorecard for 2020 highlights some of the information available on personal current accounts, and can help customers choose their provider. -
FCA sets out priorities for 2020/21
The Financial Conduct Authority (FCA) has today set out its business priorities for the year ahead – with specific focus on the challenges presented by the Coronavirus (Covid-19) pandemic. -
The journey to a sustainable credit market
Speech by Christopher Woolard, Director of Strategy & Competition, FCA, delivered at the Credit Summit. This is the text of the speech as drafted, which may differ from the delivered version. -
Speech: Beyond economics?
Many predictors of credit risk are available but we need a parsimonious model for interpretability and to avoid poor out-of-sample performance from overfitting the data.We use machine learning