Thames Water lenders offer'golden share' to head off nationalisation Image caption, Thames Water's lenders want to let local authorities have more involvement Thames Water's main lenders are offering the government a golden share and more control for local authorities in a bid to stop the troubled supplier from being nationalised. The government recently rejected a previous rescue proposal, and the BBC understands the lenders are preparing a legal challenge in case the new Andy Burnham-led government takes the firm into public hands. In his first speech as prime minister on Monday, Burnham said he wanted to see greater public control of life's essentials. The new proposal offers local authorities greater involvement in the firm, similar to the relationship between United Utilities and Greater Manchester agreed when Burnham was the city's mayor. The London & Valley Water (L&VW) consortium of lenders had already proposed a £10bn deal to prevent Thames Water from entering administration.

The lenders to Thames Water are preparing a legal challenge in case a Burnham-led government attempts to nationalise the UK's biggest water company. Burnham - who takes over as PM on Monday - has previously said he wants to see greater public control of the water and energy sectors and has called for Thames Water to be nationalised. Thames Water is about £20bn in debt. Its lenders had proposed a deal to write off nearly half of that and inject new cash in return for some leniency from future pollution fines, but this deal has previously been rejected by the government as being weak and bad for consumers and the environment. Sources close to the creditors have told the BBC that in the event of full nationalisation, they would pursue payment in full of the outstanding debts as has happened in previous cases, which could leave the government with a multi-billion-pound bill. Fears first emerged three years ago that Thames Water could collapse and on Thursday, the firm warned it has enough cash to last until the end of this year .

Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal Opportunity. In this paper we consider the effects of adopting such fair classifiers on the overall level of ecosystem fairness. Specifically, we introduce the study of fairness with competing firms, and demonstrate the failure of fair classifiers in yielding fair ecosystems. Our results quantify the loss of fairness in systems, under a variety of conditions, based on classifiers' correlation and the level of their data overlap. We show that even if competing classifiers are individually fair, the ecosystem's outcome may be unfair; and that adjusting biased algorithms to improve their individual fairness may lead to an overall decline in ecosystem fairness. In addition to these theoretical results, we also provide supporting experimental evidence. Together, our model and results provide a novel and essential call for action.

Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered plans to cut more than 7,000 jobs over the next four years as it increasingly uses artificial intelligence. The London-headquartered lender is one of the first major global banks to lay out plans to cut thousands of jobs, citing AI as a driver to make its operations slimmer as it seeks to increase its profitability and tackle competition. StanChart said on Tuesday it would cut 15% of its back-office roles by 2030, which would result in about 7,800 redundancies out of its more than 52,000 staff in such roles.

We study a class of classification problems best exemplified by the \emph{bank loan} problem, where a lender decides whether or not to issue a loan. The lender only observes whether a customer will repay a loan if the loan is issued to begin with, and thus modeled decisions affect what data is available to the lender for future decisions. As a result, it is possible for the lender's algorithm to ``get stuck'' with a self-fulfilling model. This model never corrects its false negatives, since it never sees the true label for rejected data, thus accumulating infinite regret. In the case of linear models, this issue can be addressed by adding optimism directly into the model predictions. However, there are few methods that extend to the function approximation case using Deep Neural Networks.

Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal Opportunity. In this paper we consider the effects of adopting such fair classifiers on the overall level of ecosystem fairness. Specifically, we introduce the study of fairness with competing firms, and demonstrate the failure of fair classifiers in yielding fair ecosystems. Our results quantify the loss of fairness in systems, under a variety of conditions, based on classifiers' correlation and the level of their data overlap. We show that even if competing classifiers are individually fair, the ecosystem's outcome may be unfair; and that adjusting biased algorithms to improve their individual fairness may lead to an overall decline in ecosystem fairness. In addition to these theoretical results, we also provide supporting experimental evidence. Together, our model and results provide a novel and essential call for action.

How nervous are investors about the stock market? Every week it seems US financial markets are hit by another bout of fear. The latest worries spread this week from the banking sector in the US, after two regional lenders warned they would be hit by losses from alleged fraud. But before that, markets swooned over signs of rekindled US-China tensions, as the two superpowers face off over tariffs, advanced technology and access to rare earths. The bankruptcies of car parts supplier First Brands and subprime car lender Tricolor acted as a trigger for nervous chatter in September.

Elon Musk's artificial intelligence firm xAI has acquired his social media platform X in an all-stock transaction that values the company at 33 billion, including 12 billion worth of debt, the centibillionaire announced Friday. The sale comes just weeks after Musk reportedly raised an additional roughly 1 billion in debt financing for X that valued the company at 44 billion--the same price Musk paid for it three years ago. "xAI and X's futures are intertwined," Musk wrote in an X post. "Today, we officially take the step to combine the data, models, compute, distribution and talent. This combination will unlock immense potential by blending xAI's advanced AI capability and expertise with X's massive reach."

We study a class of classification problems best exemplified by the \emph{bank loan} problem, where a lender decides whether or not to issue a loan. The lender only observes whether a customer will repay a loan if the loan is issued to begin with, and thus modeled decisions affect what data is available to the lender for future decisions. As a result, it is possible for the lender's algorithm to get stuck'' with a self-fulfilling model. This model never corrects its false negatives, since it never sees the true label for rejected data, thus accumulating infinite regret. In the case of linear models, this issue can be addressed by adding optimism directly into the model predictions.

Securities lending is an important part of the financial market structure, where agent lenders help long term institutional investors to lend out their securities to short sellers in exchange for a lending fee. Agent lenders within the market seek to optimize revenue by lending out securities at the highest rate possible. Typically, this rate is set by hard-coded business rules or standard supervised machine learning models. These approaches are often difficult to scale and are not adaptive to changing market conditions. Unlike a traditional stock exchange with a centralized limit order book, the securities lending market is organized similarly to an e-commerce marketplace, where agent lenders and borrowers can transact at any agreed price in a bilateral fashion. This similarity suggests that the use of typical methods for addressing dynamic pricing problems in e-commerce could be effective in the securities lending market. We show that existing contextual bandit frameworks can be successfully utilized in the securities lending market. Using offline evaluation on real historical data, we show that the contextual bandit approach can consistently outperform typical approaches by at least 15% in terms of total revenue generated.