25 March, London: 4most, the leading employee-owned credit risk analytics consultancy, has been ranked 241st in the Financial Times (FT) list of Europe’s Fastest Growing Companies 2019. This recognition comes after five years of consistent double-digit organic growth for the company.
Machine Learning has been one of the hot topics in finance over the last few years, with benefits observed in most areas – many large institutions have prototyped and implemented techniques across decisioning, strategy optimisation, and fraud. The other key area that Machine Learning can bring significant benefits to, is automation.
A recent Bank of England working paper highlights the link between banking market competition and financial system stability. Consistent with earlier work, it highlights that as competition increases, the banking system overall responds typically by moving to higher risk lending. However, in addition, it describes how individual banks tend to converge with the most-risky becoming more secure as competition increases.
21 September 2018, London: 4most, the global credit risk consultancy, has appointed Alvin Ng as their new Chief Financial Officer (CFO). Alvin joins from the UK arm of the Weston family’s private office, Galewest Investments Ltd, where his role spanned strategic tax planning and global private assets management. At 4most, he will be responsible for directing and managing all aspects of the finance function and will be a key component in 4most’s leadership team.
Welcome to the first edition of RegRadar. With publication of Basel III reforms in December 2017, the ongoing EBA RWA harmonisation programme and both the BoE & the ECB consulting on the Definition of Default for credit risk, forward planning and timely implementation is key to understanding how reforms will impact capital and compliance costs of current and future business plans.
The financial services industry has recently undergone a major change due to the introduction of IFRS 9 impairment requirements. This has come generally at increased costs due to either the redirection of internal resource or engagement of third parties to develop compliant models.
IFRS9 Expected Credit Losses (ECL) are commonly calculated as the sum of the marginal future expected losses in each period following the reporting date, using PD, LGD and EAD components. ECL can also be calculated directly from expected future cash flows. This could be an attractive option for many short-term lenders, especially for those that cannot leverage existing PD, LGD and EAD models, as it requires developing a single cash flow model.
Over the past decade, there has been a significant shift in patterns of consumer behaviour in relation to purchasing of new cars. UK private car registrations were 39% higher in 2016 than they were in 2011, a trend which has in part been driven by the expansion of the Personal Contract Purchase (PCP) deals. Some 82% of private new car purchases was financed in this way in 2016. PCPs contribution to the rise in unsecured borrowing is firmly on the radar of both the Bank of England (BoE) and Financial Conduct Authority (FCA).
Last week we heard that the European Central Bank had closed ranks with the Bank of England to avert the Brexit crunch. With the vote of the EU Referendum hanging in the wings, the European Central Bank has pledged to flood the financial system with euro liquidity if credit markets seize up after a Brexit vote.