Fact-checking real-world claims, particularly numerical claims, is inherently complex that require multistep reasoning and numerical reasoning for verifying diverse aspects of the claim. Although large language models (LLMs) including reasoning models have made tremendous advances, they still fall short on fact-checking real-world claims that require a combination of compositional and numerical reasoning. They are unable to understand nuance of numerical aspects, and are also susceptible to the reasoning drift issue, where the model is unable to contextualize diverse information resulting in misinterpretation and backtracking of reasoning process. In this work, we systematically explore scaling test-time compute (TTS) for LLMs on the task of fact-checking complex numerical claims, which entails eliciting multiple reasoning paths from an LLM. We train a verifier model (VERIFIERFC) to navigate this space of possible reasoning paths and select one that could lead to the correct verdict. We observe that TTS helps mitigate the reasoning drift issue, leading to significant performance gains for fact-checking numerical claims. To improve compute efficiency in TTS, we introduce an adaptive mechanism that performs TTS selectively based on the perceived complexity of the claim. This approach achieves 1.8x higher efficiency than standard TTS, while delivering a notable 18.8% performance improvement over single-shot claim verification methods. Our code and data can be found at https://github.com/VenkteshV/VerifierFC

Inquisitive questions -- open-ended, curiosity-driven questions people ask as they read -- are an integral part of discourse processing (Kehler and Rohde, 2017; Onea, 2016) and comprehension (Prince, 2004). Recent work in NLP has taken advantage of question generation capabilities of LLMs to enhance a wide range of applications. But the space of inquisitive questions is vast: many questions can be evoked from a given context. So which of those should be prioritized to find answers? Linguistic theories, unfortunately, have not yet provided an answer to this question. This paper presents QSALIENCE, a salience predictor of inquisitive questions. QSALIENCE is instruction-tuned over our dataset of linguist-annotated salience scores of 1,766 (context, question) pairs. A question scores high on salience if answering it would greatly enhance the understanding of the text (Van Rooy, 2003). We show that highly salient questions are empirically more likely to be answered in the same article, bridging potential questions (Onea, 2016) with Questions Under Discussion (Roberts, 2012). We further validate our findings by showing that answering salient questions is an indicator of summarization quality in news.

The advent of Blockchain technology (BT) revolutionised the way remittance transactions are recorded. Banks and remittance organisations have shown a growing interest in exploring blockchain's potential advantages over traditional practices. This paper presents a data-driven predictive decision support approach as an innovative artefact designed for the blockchain-oriented remittance industry. Employing a theory-generating Design Science Research (DSR) approach, we have uncovered the emergence of predictive capabilities driven by transactional big data. The artefact integrates predictive analytics and Machine Learning (ML) to enable real-time remittance monitoring, empowering management decision-makers to address challenges in the uncertain digitised landscape of blockchain-oriented remittance companies. Bridging the gap between theory and practice, this research not only enhances the security of the remittance ecosystem but also lays the foundation for future predictive decision support solutions, extending the potential of predictive analytics to other domains. Additionally, the generated theory from the artifact's implementation enriches the DSR approach and fosters grounded and stakeholder theory development in the information systems domain.

Trade finance refers to products and financial instruments used to facilitate the export and import of trade and commerce--and, thereby, the smooth conduct of business. Some of the most popular instruments in trade finance are letters of credit (LC), bank guarantees (BG), documentary collections and remittances. Essentially, these instruments have one primary function: enabling parties to the trade to make a transaction and mitigate the associated risks related to supply and payment. Trade finance drives the global economy. This segment will only grow in the future, notwithstanding temporary setbacks like the Covid-19 pandemic or geopolitical conflicts.

Ever since microfinance came into the picture several decades ago, it has been continuously transforming the lives of the economically backward across the world. Micro-lending has been instrumental in pulling millions out of the clutches of poverty. It has helped millions of small-scale entrepreneurs realise their dreams. Starting from providing loans to people with minimal to no access to traditional banking, microfinance has now graduated to micro-savings, micro-insurances, fund transfers, payment services and remittances. Microfinance companies have been continuously looking for avenues to expand their reach into the potential markets and investing heavily in doing so.

Mumbai: The financial sector in India is driving investments into chatbots and artificial intelligence (AI) to augment customer service, but bankers are convinced that there would not be job losses as these new tools will only complement staff. When it comes to AI it is not upstarts but big guns of banking with resources, which are driving investments. State Bank of India (SBI) is working with IBM to make use of Watson -- an answering computer software to assist staff and employees. HDFC Bank has tied up with artificial intelligence firm Niki (funded by Ratan Tata and Ronnie Screwvala) to bring in conversational banking. Last week, Yes Bank partnered Payjo to launch AI-led digital initiatives.

Many corporations still have manual steps in the way they process outstanding invoices. But Rodney Gardner of Bank of America Merrill Lynch -- and other bankers -- are starting to offer their corporate clients a way to automate the handling of incoming payments. The service is proving to be valuable both for the banks and the corporations. Gardner invites clients to give him a ledger of a month's worth of the money owed to them by their customers. He then runs the information through a system of cloud-based software that BofA calls Intelligent Receivables.