Optimizing credit limits, interest rates, and loan terms is crucial for managing borrower risk and lifetime value (L TV) in personal loan platform. However, counterfactual estimation of these continuous, multi-dimensional treatments faces significant challenges: randomized trials are often prohibited by risk controls and long repayment cycles, forcing reliance on biased observational data. Existing causal methods primarily handle binary/discrete treatments and struggle with continuous, multi-dimensional settings. Furthermore, financial domain knowledge mandates provably monotonic treatment-outcome relationships (e.g., risk increases with credit limit). To address these gaps, we propose Multi-Treatment-DML, a novel framework leveraging Double Machine Learning (DML) to: (i) debias observational data for causal effect estimation; (ii) handle arbitrary-dimensional continuous treatments; and (iii) enforce monotonic constraints between treatments and outcomes, guaranteeing adherence to domain requirements. Extensive experiments on public benchmarks and real-world industrial datasets demonstrate the effectiveness of our approach. Furthermore, online A/B testing conducted on a realworld personal loan platform, confirms the practical superiority of Multi-Treatment-DML in real-world loan operations.

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.

Interest-free promotions are a prevalent strategy employed by credit card lenders to attract new customers, yet the research exploring their effects on both consumers and lenders remains relatively sparse. The process of selecting an optimal promotion strategy is intricate, involving the determination of an interest-free period duration and promotion-availability window, all within the context of competing offers, fluctuating market dynamics, and complex consumer behaviour. In this paper, we introduce an agent-based model that facilitates the exploration of various credit card promotions under diverse market scenarios. Our approach, distinct from previous agent-based models, concentrates on optimising promotion strategies and is calibrated using benchmarks from the UK credit card market from 2019 to 2020, with agent properties derived from historical distributions of the UK population from roughly the same period. We validate our model against stylised facts and time-series data, thereby demonstrating the value of this technique for investigating pricing strategies and understanding credit card customer behaviour. Our experiments reveal that, in the absence of competitor promotions, lender profit is maximised by an interest-free duration of approximately 12 months while market share is maximised by offering the longest duration possible. When competitors do not offer promotions, extended promotion availability windows yield maximum profit for lenders while also maximising market share. In the context of concurrent interest-free promotions, we identify that the optimal lender strategy entails offering a more competitive interest-free period and a rapid response to competing promotional offers. Notably, a delay of three months in responding to a rival promotion corresponds to a 2.4% relative decline in income.

Credit cards are an essential part of modern financial life; according to the Consumer Financial Protection Bureau (2021), 175 million North Americans, more than half of its population, own credit card products. On the other hand, the same cannot be said for developing countries; according to the World Bank, an average of only 55% of Latin Americans had a bank account in January 2020, and only approximately 20% have a credit card (World Economic Forum, 2022). However, companies that use financial technology, known as fintechs, have enabled digital financial services that can help the unbanked population overcome difficulties such as costs, geographical impediments, long waiting times, and lack of financial history in accessing traditional banking products (Khera, Ng, Ogawa, & Sahay, 2022; Rojas-Torres, Kshetri, Hanafi, & Kouki, 2021). The number of fintech companies in Latin America has risen rapidly, and their appearance has altered the behavior of traditional banks, which are now seeking innovation and changes to customercentered approaches (Vives, 2019) and have decided in some cases to create alliances with these new companies (Bejar et al., 2022). Because the financial industry is primarily based on information, financial process reports have been more easily transitioned to the digitization stage; this situation is in contrast with the consumer goods industry, which includes a physical element (Puschmann, 2017). In addition, emerging "super-apps", which are mobile applications that offer different services and products in the same environment (e.g., goods deliveries, social networks, and financial services), collect a large amount of alternative data (Siddiqi, 2017) that are generated by the use of the given application and are supplementary to the traditional financial data. Several researchers have found that the use of alternative information is valuable in the financial sector because it allows for improvement in the performance of some models; for instance, Roa et al. (2021) showed that the inclusion of variables such as the number of payments with errors and orders paid with the superapp's own credit cards can add significant predictive value in the problem of default prediction.

November 24, 2021 - The Federal Trade Commission has issued limited guidance in the area of artificial intelligence and machine learning (AI), but through its enforcement actions and press releases has made clear its view that AI may pose issues that run afoul of the FTC Act's prohibition against unfair and deceptive trade practices. In recent years it has pursued enforcement actions involving automated decision-making and results generated by computer algorithms and formulas, which are some common uses of AI in the financial sector but may also be relevant in other contexts such as health care. In FTC v. CompuCredit Corp., FTC Case No. 108-CV-1976 (2008), the FTC alleged that subprime credit marketer CompuCredit violated the FTC Act by deceptively failing to disclose that it used a behavioral scoring model to reduce consumers' credit limits. If cardholders used their credit cards for cash advances or to make payments at certain venues, such as bars, nightclubs and massage parlors, their credit limit might be reduced. The company, the FTC alleged, did not inform consumers that these purchases could reduce their credit limit, neither at the time they signed up nor at the time they reduced the credit limit.

In a rapidly changing financial environment, the race is on for AI implementation. It's no longer if an organization is using AI, it's when they get it and how they implement it in business. The big question is should they develop in-house, buy off the shelf or get a custom solution? Here's what the options offer and what to look for when choosing an AI solution. Surveys of major financial organizations show they recognize the need to use AI to leverage their complex data and mitigate business risks. MIT Sloan Management Review did a survey of more than 3,000 managers and interviewed executives, learning that a majority of companies have tried developing AI, but only 1 in 10 gained significant financial benefits.

Faire is an online wholesale marketplace built on the belief that the future is local -- there are over 1M independent retailers in the U.S. and Canada doing more than twice the revenue of Walmart and Amazon combined. At Faire, we're using the power of tech, data, and machine learning to connect a thriving community of over 100,000 brands and local retailers around the world. Picture your favorite boutique in town -- we help them discover the best products to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses can compete with these big box and ecommerce giants. We're looking for smart, resourceful and passionate people to join us as we power the shop local movement.

When we think of artificial intelligence, the first things that usually come to mind are depictions from popular culture. Artificial intelligence (AI), however, doesn't just exist in futuristic movies. It's already a part of the way we live, shop, work and bank. Rather than a robot gone rogue, AI is simply technology programmed by humans with the ability to memorize information, learn from experience, communicate facts, and/or make decisions. And because humans are the ones creating AI, we must ask the question: what are we teaching our machines, and what are they learning from us?

Nowadays consumer loan plays an important role in promoting the economic growth, and credit cards are the most popular consumer loan. One of the most essential parts in credit cards is the credit limit management. Traditionally, credit limits are adjusted based on limited heuristic strategies, which are developed by experienced professionals. In this paper, we present a data-driven approach to manage the credit limit intelligently. Firstly, a conditional independence testing is conducted to acquire the data for building models. Based on these testing data, a response model is then built to measure the heterogeneous treatment effect of increasing credit limits (i.e. treatments) for different customers, who are depicted by several control variables (i.e. features). In order to incorporate the diminishing marginal effect, a carefully selected log transformation is introduced to the treatment variable. Moreover, the model's capability can be further enhanced by applying a non-linear transformation on features via GBDT encoding. Finally, a well-designed metric is proposed to properly measure the performances of compared methods. The experimental results demonstrate the effectiveness of the proposed approach.

Whatever it is they're selling, businesses today are under more pressure than ever to provide a five-star experience. Customer service is not a'nice-to-have', it's taken for granted by customers who have plenty of eager competitors at their disposal and a multitude of public forums to share their negative experiences. In times of need, if customers can't open a chatbot or pick up a phone to quickly get resolution to their issues, they'll start shopping around – customer experience is now part of the package. Meeting these real-time demands, then, is a deal-breaker, but there are also rewards. According to McKinsey, 70 percent of buying experiences are based on how the customer feels they are being treated, and if you do it right, they will stick around.