Insurance, as a critical component of the global financial system, demands high standards of accuracy and reliability in AI applications. While existing benchmarks evaluate AI capabilities across various domains, they often fail to capture the unique characteristics and requirements of the insurance domain. To address this gap, we present INSEva, a comprehensive Chinese benchmark specifically designed for evaluating AI systems' knowledge and capabilities in insurance. INSEva features a multi-dimensional evaluation taxonomy covering business areas, task formats, difficulty levels, and cognitive-knowledge dimension, comprising 38,704 high-quality evaluation examples sourced from authoritative materials. Our benchmark implements tailored evaluation methods for assessing both faithfulness and completeness in open-ended responses. Through extensive evaluation of 8 state-of-the-art Large Language Models (LLMs), we identify significant performance variations across different dimensions. While general LLMs demonstrate basic insurance domain competency with average scores above 80, substantial gaps remain in handling complex, real-world insurance scenarios. The benchmark will be public soon.

Participatory urban planning is the mainstream of modern urban planning and involves the active engagement of different stakeholders. However, the traditional participatory paradigm encounters challenges in time and manpower, while the generative planning tools fail to provide adjustable and inclusive solutions. This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback iteration, solving a community-level land-use task catering to 1000 distinct interests. Empirical experiments in diverse urban communities exhibit LLM's adaptability and effectiveness across varied planning scenarios. The results were evaluated on four metrics, surpassing human experts in satisfaction and inclusion, and rivaling state-of-the-art reinforcement learning methods in service and ecology. Further analysis shows the advantage of LLM agents in providing adjustable and inclusive solutions with natural language reasoning and strong scalability. While implementing the recent advancements in emulating human behavior for planning, this work envisions both planners and citizens benefiting from low-cost, efficient LLM agents, which is crucial for enhancing participation and realizing participatory urban planning.

As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business. However, there is no publicly available dataset so far and most existing approaches are limited to an extremely small scope of brands, which seriously hinders the research on site recommendation. Therefore, we collect, construct and release an open comprehensive dataset, namely OpenSiteRec, to facilitate and promote the research on site recommendation. Specifically, OpenSiteRec leverages a heterogeneous graph schema to represent various types of real-world entities and relations in four international metropolises. To evaluate the performance of the existing general methods on the site recommendation task, we conduct benchmarking experiments of several representative recommendation models on OpenSiteRec. Furthermore, we also highlight the potential application directions to demonstrate the wide applicability of OpenSiteRec. We believe that our OpenSiteRec dataset is significant and anticipated to encourage the development of advanced methods for site recommendation. OpenSiteRec is available online at https://OpenSiteRec.github.io/.

A new text-based artificial intelligence (AI) tool called ChatGPT is making waves in the technology industry for its ability to accurately answer questions and complete a wide range of tasks, from creating software to formulating business ideas. Launched on November 30, 2022 by OpenAI, the AI program has already impressed users and technologists with its ability to mimic human language and speaking styles, all the while providing coherent and topical information. In the span of just a couple of days, the service managed to cross the one million user threshold. Now, industry observers and commenters are theorizing on the technology's potential implications in the finance and banking sector. According to Ethan Mollick, an associate professor of management at The Wharton School of the University of Pennsylvania, ChatGPT is a tipping point for AI and proof that the technology can be useful to a broader population of people.

Lately, I've been meeting a lot of people who are interested in making a career shift into data science. One of the first things they always ask me is, "what does a typical day look like?". I've seen a lot of articles that give an overview of the skills and tools Data Scientists use, but I don't see very many that provide real examples of daily tasks. While every day is different, these tasks represent a typical day for me as a Senior Data Scientist at a large financial institution. I typically start my work day around 8:30 am after I roll out of bed at 8:20.

The Bank of England has published its report "Machine Learning in UK Financial Services". The report sets out its findings, following a survey of around a hundred regulated firms in the UK. It highlights the growing use of machine learning, especially in insurance, and the challenges of explainability, legacy systems, the skills gap and regulatory uncertainty. The number of UK financial services firms using or developing machine learning (ML) applications is increasing, and this trend is set to continue across a greater range of business areas within financial services. The largest expected increase in use, in absolute terms, is in the insurance sector, followed by banking.

In my last two posts, I've defined what composable banking is and its potential scope. In both these posts, I've highlighted BIAN as a way of defining the composable modules. One thing I'd like to add though is that BIAN represents the business model of banking first and then how it can be supported by technology. It's not about the modularisation of software components that can be easily interchanged, it is very much about creating flexibility and agility in banking by creating a canonical representation of the business of banking. The biggest challenge for any bank is how do they reach such a vision of composable banking when over decades of investment in technology automation they have hundreds or thousands of systems, with some sharing data through extraction, some integrated through technical bridges and maybe a few more modern solutions through APIs?

Founded in 1991, India-headquartered HCL Technologies is a global technology company that helps enterprises reimagine their businesses for the digital age. The company specializes in key areas, including digital, IoT, cloud, automation, cybersecurity, and analytics, amongst others. With the company increasingly having a presence in the RegTech space, how does it see the sector changing? How is RegTech changing compliance? According to Daryl Wilkinson – Senior Executive, Strategic Initiatives, Financial Services UK&I at HCL Technologies, "I think you can look at this through two lenses. First, there appears to be a consensus that the global RegTech market is expected to achieve $30bn by 2027 – so that alone is changing the compliance market –new investment is disrupting incumbent models and is changing the way regulators engage with businesses. The second lens is cost; financial services rely heavily on legacy technology – RegTech's nature is to find that niche to solve those problems at a much lower cost than the banks and insurers might otherwise do themselves."

When Alan Turing published his paper titled "Computing Machinery and Intelligence" in 1950, he was trying to answer a simple question -- Can machines think? He introduced the Turing Test in the paper, where a human had to converse with a bunch of people. Among the people, there was also a machine disguised as a human. The goal was to check whether the human would identify the machine or not. Though the test was not definitive, it opened doors to Artificial Intelligence that we see around us now.

Artificial intelligence and machine learning are two facets of modern technology that can have a massive positive impact on businesses today. However, many innovations within the AI and ML space are among the most cutting-edge technologies currently available, and even some of the more widely used business technologies can break the bank if not implemented with a smart plan in place. However, thanks to more commercial providers entering the arena, the investment required for adopting either of these solutions is no longer excessive. With careful budgeting and a detailed plan, even small and medium-sized enterprises can implement relevant AI and ML technologies. Here, 14 professionals from Forbes Technology Council offer valuable tips for companies that want to successfully incorporate AI/ML on a budget.