Altair enables 组织s worldwide to compete more effectively by operationalizing data analytics and AI with secure, 治理, 可扩展策略. 我们提供世界一流的服务, 用于数据准备的自助服务分析解决方案, 预测建模, 流处理, 可视化和更多. 没有代码, 云计算接口, 组织s can harness the full power of analytics and AI throughout its complete data lifecycle, 推动更高层次的业务成果.
今天的BFSI组织需要无与伦比的数据控制, 组织, 以及确保他们自信的洞察力, 消息灵通的决定. 我们的无代码套件, 人工智能数据分析解决方案可帮助您简化贷款和服务工作流程, 预测信用和违约风险, 给你无与伦比的数据洞察力,让你变得聪明, 及时决策. 有了Altair,你可以开启一个全新的数据洞察世界.
财务数据分析并不一定是令人生畏的. Altair世界一流的数据分析解决方案, 你有工具让你组织, 可视化, 巩固, 控制你的数据,这样你就不会错过任何见解. 我们的解决方案还允许您标准化报告和导出方法, 执行实时交易分析, 和更多的. 凭直觉, 没有代码的软件, 利用您的所有数据,以便您可以最大化性能, 最小化风险, 让自己有信心, 明智的商业决策.
现代制造业比以往任何时候都更加以数据为导向——这就是为什么你需要直觉, 强大的, 无代码软件解决方案,可以最大限度地提高输出和效率, 尽量减少计划外停机时间, 并让您对您的产品在该领域的性能有无与伦比的洞察力. 我们的人工智能工具套件可以帮助您监控设备, 提供实时反馈, 确保一切都在最佳状态下运行. 与OPE电子,获得最大的从您的业务,一天24小时,一年365天.
Altair’s 30-year history working with financial 组织s means we understand how analytics can help users understand and maximize aspects like risk mitigation, 监管, 客户互动, 操作的见解, 和更多的.
Altair can help insurers adopt data-driven technology so they can ensure their approaches to claims processing, 优化, 欺诈检测, 风险和损失评估, 客户保持是精简和有效的.
Altair的工具获得了在政府网络上运行所需的认证, 并且不给用户代码, self-service analytics solutions that enable all users – regardless of skill level – to use data to solve security, 物流, 以及基于政策的挑战.
Altair RapidMiner – our data analytics and AI platform – reduces healthcare IT complexities and makes claims, 报销处理, 收入周期管理, 互操作性, 病人依从性, 满意度分析, 医生的表现分析比 过.
最大化从流程生成的数据洞察力, 客户, 和产品, 您的团队必须能够在整个组织中生成和共享数据. Our solutions support users of varying skillsets – including engineers, MLOps specialists, business 分析师, 还有更多——没有密码, 云计算接口 that delivers the robust capabilities they need to harness the full power of AI-driven data analytics.
Altair RapidMiner empowers 组织s with unmatched data analytics and AI technology so users can develop secure, 治理, 可扩展策略.
Our robust solutions empower you to write data-centric applications using the best programming language(s) for the job, 并允许您在单个程序中混合多种语言的语法. 我们的代码和无代码工具都可以让您创建, 维护, 并使用Python等语言运行模型, R, SQL, SAS语言. Organizations that have invested heavily in IP developed with the SAS language can use our 太ls to 维护 and run their existing SAS language programs without needing any third-party products or middleware.
Advancements in AI and 机器学习 (ML) technology – along with the proliferation of simulation, 测试, and field data sets – has made data science a critical component of the modern engineering and product development lifecycle. 计算机辅助工程, 人工智能驱动, 使制造商能够发现机器学习驱动的见解, 探索新的解决方案和设计, and develop and test cutting-edge products through collaboration and a streamlined design workflow.
As an industry leader that boasts more than 30 years’ experience in data discovery and transformation, 我们提供最快的, 最简单的方法从困难中提取数据, 半结构化数据,如pdf, 电子表格, 和文本文件. 我们还提供从大数据和其他结构化资源中提取数据的解决方案. 无论您的数据是在本地还是在云中, our 太ls can automate data preparation tasks –freeing up time you can spend on value-add activities.
我们的机器学习(ML)和人工智能解决方案可以快速达到粒度, 低延迟数据,包含您试图发现的宝贵见解. 通过AutoML和可解释AI等功能提供透明度和自动化, 我们简化了模型构建,因此您可以对结果有信心. 我们灵活, 无代码方法允许您自由配置和调优模型, 在模型构建过程中给予您比以往更多的控制.
Our solutions let you connect directly to virtually any data source so you can build analytical dashboards with a no-code, 拖放界面可以在企业范围内共享. Altair BI visualization 太ls let 组织s perform virtual analyses on large sets of historical data. 利用我们的流媒体分析工具, 团队可以快速前进, 根据快速变化的遥测技术做出明智的决定, 传感器, 事务, 以及交易数据.
Data science and 机器学习 (DSML) are rapidly becoming the competitive advantage 过y company needs. This Market Guide provides insights from Gartner on how multipersona DSML platforms support the needs of diverse group of technical and nontechnical roles throughout an 组织.
ABI研究公司, 全球领先的技术情报公司, 授予阿泰尔总领袖的称号, 顶尖的创新者, 以及2023年制造业数据分析产品排名中的最佳实施者. Read the report for market insights and an overview of Industrial 物联网 (IIoT) data analytics offerings.
Lenders need 太ls they can employ during the loan application process to advance an application to the next step or divert potential 客户 to alternative products that are a better fit. Implementing artificial intelligence (AI) models that facilitate fast credit reviews — and even approvals — can help lenders increase the quality, 数量, 以及他们在不承担不可接受风险的情况下发放的贷款数量.
Identifying potentially impactful use cases is one of the most cited roadblocks for 组织s seeking to l过age AI in their business. 更复杂的是, best practices dictate that you should have a portfolio of use cases ready to experiment with. 如果找到一个是一个挑战, 开发一个完整的用例组合可能是非常困难的.
在本指南中,我们将介绍:
A multinational financial services company wanted to explore improving its collection rate and process but did not have the in-house talent it could dedicate to the project. 拥有数十亿美元的资产,在全球拥有2500多万客户, the bank needed to integrate new sources of highly granular credit data with historical data from a variety of structured sources and semi-structured. Their collections process lacked prescriptive analytics to optimize which loan they reached out to each day and through which channel. It was also having productivity issues and struggling to launch new credit products because of inflexible data transformation 太ls that couldn’t accommodate new variables.