When making treatment selection decisions, it is essential to include a causal effect estimation analysis to compare potential outcomes under different treatments or controls, assisting in optimal selection. However, merely estimating individual treatment effects may not suffice for truly optimal decisions. Our study addressed this issue by incorporating additional criteria, such as the estimations' uncertainty, measured by the conditional value-at-risk, commonly used in portfolio and insurance management. For continuous outcomes observable before and after treatment, we incorporated a specific prediction condition. We prioritized treatments that could yield optimal treatment effect results and lead to post-treatment outcomes more desirable than pretreatment levels, with the latter condition being called the prediction criterion. With these considerations, we propose a comprehensive methodology for multitreatment selection. Our approach ensures satisfaction of the overlap assumption, crucial for comparing outcomes for treated and control groups, by training propensity score models as a preliminary step before employing traditional causal models. To illustrate a practical application of our methodology, we applied it to the credit card limit adjustment problem. Analyzing a fintech company's historical data, we found that relying solely on counterfactual predictions was inadequate for appropriate credit line modifications. Incorporating our proposed additional criteria significantly enhanced policy performance.

Keeping ChatGPT running is expensive as heck, so OpenAI needs access to plenty of cash to make sure the lights stay on. A day after the company said it had secured 6.6 billion in funding -- the biggest ever funding round for a startup -- it confirmed that it has a new 4 billion revolving line of credit. OpenAI has yet to tap the credit line, which it obtained from JPMorgan Chase, Citi, Goldman Sachs, Morgan Stanley, Santander, Wells Fargo, SMBC, UBS and HSBC. Some of those banks are also among OpenAI's customers. All told, OpenAI now has a war chest of over 10 billion in liquid funds.

Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the causal impact of credit lines on spending, previous studies have employed causal estimators, based on direct regression (DR), inverse propensity weighting (IPW), and double machine learning (DML) to estimate the treatment effect. However, these estimators do not consider the notion that an individual's spending can be understood and represented as a distribution, which captures the range and pattern of amounts spent across different orders. By disregarding the outcome as a distribution, valuable insights embedded within the outcome distribution might be overlooked. This paper develops a distribution-valued estimator framework that extends existing real-valued DR-, IPW-, and DML-based estimators to distribution-valued estimators within Rubin's causal framework. We establish their consistency and apply them to a real dataset from a large e-commerce platform. Our findings reveal that credit lines positively influence spending across all quantiles; however, as credit lines increase, consumers allocate more to luxuries (higher quantiles) than necessities (lower quantiles).

Before the pandemic, AI in banking was primarily used to automate routine tasks. But banks now see it as a vital tool to support product innovation, develop new business models, and provide a personalised experience for every customer. A recent Economist Intelligence Unit (EIU) survey of banking executives for Temenos found that 85% have a "clear strategy" for adopting AI to develop new products and services. It revealed over a third are prioritising AI to improve customer experience through personalisation. Some are also looking to acquire or partner with fintech companies to enhance their customer experience through a personalised experience when offering investments, saving deposits, and retail lending.