Bank credit ratings, assigned by agencies like Standard & Poor's, Moody's, and Fitch, evaluate a bank's financial health based on factors such as asset quality, profitability, and market position (White 2010). These ratings are critical indicators of a bank's ability to repay debt and significantly influence economic players: for businesses, they affect borrowing costs and market trust; for economies, they impact financial system stability. Sudden rating changes can trigger volatile capital flows and market fluctuations, influencing economic growth and financial stability. During financial market instability, predicting bank credit ratings, especially for the upcoming quarter, becomes crucial. These predictions provide the data needed for informed decision-making, prompt regulatory adjustments, and the protection of investors and the public. The 2023 bankruptcy of Silicon V alley Bank (SVB), which triggered collapses like those of Signature Bank and First Republic Bank, underscores the resulting financial turmoil (Aharon et al. 2023). Graph neural networks (GNNs) have become a pivotal technology in financial risk prediction, particularly excelling in node classification and link prediction tasks (Wu et al. 2022). These models effectively leverage edge information to represent the propagation of financial risk within networks.

Structured finance, which involves restructuring diverse assets into securities like MBS, ABS, and CDOs, enhances capital market efficiency but presents significant due diligence challenges. This study explores the integration of artificial intelligence (AI) with traditional asset review processes to improve efficiency and accuracy in structured finance. Using both open-sourced and close-sourced large language models (LLMs), we demonstrate that AI can automate the verification of information between loan applications and bank statements effectively. While close-sourced models such as GPT-4 show superior performance, open-sourced models like LLAMA3 offer a cost-effective alternative. Dual-agent systems further increase accuracy, though this comes with higher operational costs. This research highlights AI's potential to minimize manual errors and streamline due diligence, suggesting a broader application of AI in financial document analysis and risk management.

It was October 2021, and Imane, a 44-year-old mother of three, was still in pain from the abdominal surgery she had undergone a few weeks earlier. She certainly did not want to be where she was: sitting in a small cubicle in a building near the center of Rotterdam, while two investigators interrogated her. But she had to prove her innocence or risk losing the money she used to pay rent and buy food. Imane emigrated to the Netherlands from Morocco with her parents when she was a child. She started receiving benefits as an adult, due to health issues, after divorcing her husband. Since then, she has struggled to get by using welfare payments and sporadic cleaning jobs.

We decided to have some fun with ChatGPT, the scary-good chatbot from OpenAI that's been garnering headlines. We fed it a fake letter, cobbled together with common tropes, and asked it to reply in a few different ways. I'm recently engaged and in the throes of planning my early 2024 wedding. My handsome fiancé, the timing, my mother's own hand-me-down ring--it's all felt like a perfect fairytale. Until I heard what my mother-in-law has in store for us.

American Express (Amex) is a globally integrated payments company, providing customers with access to products, insights and experiences that enrich lives and build business success. And inside the company, the Amex Credit Fraud Risk business unit's mission is all about minimising credit and fraud losses while promoting business growth and delivering superior customer service. Nothing about this will surprise you so far, we're presuming. What may: while the financial services industry uses digital for just about every process imaginable, there's one surprising remaining exception-the commercial card underwriting process, which to you and me is'Are you going to lend my small business any money?' In a lot of Europe, this process is still completely manual and takes an underwriter a good chunk of time to complete.

By Nikhil Bedi & Vivek Bhamodkar With evolving business models, increased use of tech and a changing regulatory landscape, fraud management is fraught with newer and more complex challenges. These are further exacerbated during cross-border investigations, where varied levels of standardisation, languages, local laws and regulations, along with specific cultural attributes, bring additional complexities--mandating an investigation methodology standardisation and requiring tools for quick insights. Emerging technology and artificial intelligence (AI) can help make investigations efficient, generate insights, and/or aid reviews. Optimal use of AI demands the knowledge of'possibilities and limitations' of such techniques, either in the form of'special purpose software' or the ability to combine various methods. As significant a role as these tools and technologies may play in the fight against fraud, use of AI, NLP and other technologies come with their own set of challenges.

When the sale of Heather Lord's house completed, her conveyancing solicitor attempted to transfer funds to her bank account. An automated message warned him that her name did not match the name on the account he was paying into, and that the transfer would therefore be at his own risk. "I phoned the bank and asked who I was," she says. "They told me I was Ms Heather Audrey Lord. This was also refused by the system! As we didn't want to risk my money disappearing, I asked for a cheque, made out to Heather Lord, which was processed with no problem."

Life sans credit is impossible to imagine. Since the cost of living is rising and income is stagnant, people are relying on borrowing to meet their unexpected expenses. It is hard to set aside money for a rainy day when the cash stream flowing in is very thin. With each passing year, the number of borrowers are rising and therefore hinging on credit score and repaying capacity for the loan approval have become more requisite than ever. Banks and other financial institutions need to consider numerous factors when it comes to signing off on loan.

DeepQuanty Artificial Intelligence Labs applies computer vision and artificial intelligence to solve problems in various sectors including BFSI, retail, e-commerce, and healthcare. All the three of them have other successful businesses in the analytics and technology domains. Their products extract data from images using computer vision and process the data using machine learning techniques for better end-use. "Our mission is to drastically improve customer experience (CX) while improving cost efficiencies," says Mahadevan Jayaram, Co-Founder, DeepQuanty. Currently focusing on the BFSI sector, DeepQuanty later aims to leverage the patent-pending technologies in other domains such as healthcare, retail and such.

The online lender Enova, which has been using artificial intelligence to make credit decisions for years, has lately been expanding its use of AI to handle additional tasks: spotting fraud, determining who should receive product offers, and projecting possible losses once a loan is booked. Most recently, it has also been been having AI scour paper documents to uncover false information, verify income and employment, and conduct know-your-customer checks. It also sponsored a survey, conducted by Harvard Business Review and set to be released Thursday, that benchmarks how businesses are using AI. Generally speaking, it finds companies are adopting AI slowly: though 68% of executives say AI will be a competitive differentiator within the next year and 64% are investigating or piloting AI projects, only 15% of organizations have AI-powered analytics in place due to technical and cultural challenges. In its back office, Enova has been training AI engines to review documents such as bank statements and pay stubs and automating decisions based on the information in those documents.