Finance and Insurance Industries Intelligence

Navigate the world of the Finance and Insurance industries with precision and confidence using Bitvore's Finance and Insurance Industries Intelligence data and insights. Harnessing the power of advanced AI & ML processes, our data-driven insights enable participants and investors in the Finance and Insurance sector to manage risks, monitor industry innovations and seize emerging opportunities. Key Benefits: - Informed Decisions: The latest intelligence on Finance and Insurance companies, market shifts, latest research, new technologies, regulatory changes, innovations, fintech disruptions, best practices, industry sentiments and much more. - Deep Dive Analysis: Comprehensive coverage of 168 Finance Topics and 37 unique ESG Topics, all filtered to spotlight the finance sector. - Broad Data Coverage: Monitor 60k+ quality global sources, using publicly available and premium licensed data, tracking developments related to over 72,000 global Finance and Insurance companies, both public and private. Bitvore powers data-driven actionable signals through advanced, trusted and proven AI & ML processes, enabling our customers to manage risks and identify opportunities. Our NLP and ML models analyze unstructured content and are trained to identify 17 Finance Topics (with 151 Sub Topics) and 37 unique ESG material topics (tied to SASB, the Big 4 and other taxonomies). 60k+ quality globally sources monitored for 400k+ companies globally, using publicly available and premium licensed data. Bitvore delivers timely data, insights, signals and indicators of significant developments affecting companies, industries, bonds, markets and emerging themes including Bitvore Risk, Growth, ESG, E, S and G entity and article level sentiment scores. Some sample tables are included below: - ORGS: Contains data on organizations, which represent corporate entities, companies or organizations both public and private - RECORD: a table of articles about corporate entities, companies or organizations both public and private. - SIGNALS: Contains data about topics that are identified for any given company Some Sample fields from the RECORD table are included below: - ARTICLETYPE: Type of the record, News or Press Release. - SENTIMENT: Overall Sentiment of the record within a range of -1.0 (negative) and 1.0 (positive). - SOURCENAME: Name (typically hostname) of the source of the record. - ESGSENTIMENT: ESG Sentiment of the record within a range of -1.0 (negative) and 1.0 (positive). - SOURCEURL: URL for the source of the record. - KEY: 56 character unique identifier of the data record, a record may be on multiple rows if more than one company is associated with it. - PUBLISHED_DATE: Date/time the record was initially published at as yyyy-MM-dd hh:mm. - BODY: Body of any specified article - TITLE: Title of any specified article Some Sample fields from the ORGS table are included below: - ID: Bitvore ID that uniquely identifies the company - STATE: State code of the state the company’s headquarters is located in - EMPLOYEES: Number of employees for the company within a range. - TICKER: An abbreviation used to uniquely identify publicly traded companies - COUNTRY: Country the company’s headquarters is located in - LASTMODIFIED: The date a record was last changed as yyy-MM-dd hh:mm. It’s not often but on occasion a record can be changed to reflect either new information extracted from it or improvements to models to get more accurate information, such as a sentiment value. Bitvore Data Sets also include “Layer 2” advanced tags for industries, people, keywords/phrases, relationships, and geography providing programmatic access to complex values and relationships. Industries – 3 layered hierarchy of Economic Sector > Industry Group > Business Sector People – 2.55 million plus database of reference data with accurate extraction and disambiguation Keywords/Phrases – Broad lexicon of useful text tied to conceptual meaning, scored by confidence and salience Relationships – Over 17 different types of NLP-derived relationships like Person-Title-Company or Subsidiary-of with scored evidence Geography – Bottom-up tagging of geographical references including named places and latitude/longitude mapping Sentiment – Per-entity and per use sentiment scoring specific to references/co-references Sentiment Scores - Bitvore Risk, Growth, ESG, E, S and G entity and article level sentiment scores.借助Bitvore金融与保险行业智能数据及洞察，您可精准且自信地探索金融与保险行业版图。依托先进人工智能（Artificial Intelligence，简称AI）与机器学习（Machine Learning，简称ML）技术，我们以数据为支撑的洞察能够助力金融保险领域的从业者与投资者管理风险、追踪行业创新并把握新兴机遇。 核心优势： - 科学决策支持：覆盖金融保险企业、市场动态、前沿研究、新兴技术、监管政策变革、行业创新、金融科技颠覆、最佳实践及行业情绪等多维度最新情报。 - 深度剖析能力：全面覆盖168个金融主题与37个独特环境、社会和治理（Environmental, Social, Governance，简称ESG）主题，所有内容均经过筛选，聚焦金融领域。 - 广泛数据覆盖：依托公开授权与优质许可数据，监测6万余个高质量全球数据源，追踪全球超过7.2万家上市及非上市金融保险企业的动态。 Bitvore依托先进、可靠且经过验证的人工智能与机器学习流程，打造以数据为驱动的可落地信号，助力客户管理风险、识别机遇。我们的自然语言处理（Natural Language Processing，简称NLP）与机器学习模型可分析非结构化内容，经过训练后能够识别17个金融主题（含151个子主题）以及37个独特的重要ESG主题（关联可持续会计准则委员会（Sustainability Accounting Standards Board，简称SASB）、四大会计师事务所（Big 4）及其他分类体系）。平台依托公开授权与优质许可数据，监测6万余个高质量全球数据源，覆盖全球超过40万家企业。Bitvore可及时提供影响企业、行业、债券、市场及新兴主题的重大动态数据、洞察、信号与指标，其中包括Bitvore风险、增长、ESG、环境（Environmental，简称E）、社会（Social，简称S）及治理（Governance，简称G）维度的实体级与文章级情绪评分。 以下为部分示例数据表： - ORGS表：收录各类组织（含上市及非上市企业、公司或其他机构）的相关数据 - RECORD表：收录针对上市及非上市企业、公司或其他机构的相关文章数据 - SIGNALS表：收录针对特定企业识别出的主题相关数据 以下为RECORD表的部分示例字段： - ARTICLETYPE：记录类型，分为新闻（News）或新闻稿（Press Release） - SENTIMENT：记录整体情绪评分，取值范围为-1.0（负面）至1.0（正面） - SOURCENAME：记录来源的名称（通常为主机名） - ESGSENTIMENT：记录的ESG情绪评分，取值范围为-1.0（负面）至1.0（正面） - SOURCEURL：记录来源的URL地址 - KEY：数据记录的56位唯一标识符，若一条记录关联多家企业，则该记录将对应多行数据 - PUBLISHED_DATE：记录首次发布的日期与时间，格式为yyyy-MM-dd hh:mm - BODY：指定文章的正文内容 - TITLE：指定文章的标题 以下为ORGS表的部分示例字段： - ID：用于唯一标识企业的Bitvore ID - STATE：企业总部所在州的州代码 - EMPLOYEES：企业员工数量区间 - TICKER：用于唯一标识上市交易企业的缩写代码 - COUNTRY：企业总部所在国家 - LASTMODIFIED：记录最后更新的日期与时间，格式为yyyy-MM-dd hh:mm。虽不常见，但记录有时会因提取到新信息或模型优化（例如优化情绪评分算法）而更新。 Bitvore数据集还包含"Layer 2"高级标签，覆盖行业、人物、关键词/短语、关联关系及地理信息，支持通过编程方式访问复杂数值与关联关系： - 行业标签：采用三层层级结构，即经济部门>行业集团>业务板块 - 人物标签：收录超255万条参考数据的数据库，可实现精准抽取与实体消歧 - 关键词/短语标签：构建与概念语义关联的广泛实用文本词典，并基于置信度与显著性进行评分 - 关联关系标签：涵盖超过17种由自然语言处理衍生的关联关系类型，例如“人物-职位-企业”或“子公司关系”，并带有评分佐证依据 - 地理标签：采用自下而上的方式对地理引用进行标注，涵盖命名地点及经纬度映射 - 情绪评分标签：针对引用及共引用对象，提供实体级与特定场景下的情绪评分 - 情绪评分维度：包括Bitvore风险、增长、ESG、环境、社会及治理维度的实体级与文章级情绪评分。