Advancements in cameras used for computer vision, sensors to detect motion, pressure, sound, and atmospheric conditions, as well as ubiquitous high speed data networks have come together to deliver vast amounts of data, the Internet of Things, and Technologie 4.0. When applied to this data, machine learning algorithms can improve efficiency across industries. Process automation, real time detection, document processing, and predictive maintenance are a few of the areas where Aicadium customers are benefiting.

Investment Decision Making

Investment intelligence is an area growing rapidly within AI for financial services. Natural language processing, or NLP, can be used in conjunction with sentiment analysis and intelligent news screening in order to monitor content sources for insights which can be used to better investment decision making. Topic modelling and visualisations via dashboards enables issues to be identified early on and intelligent insights can aid with decisions to take action or divest from particular companies within a FSI’s portfolio.

Sentiment Analysis

Topic modelling and sentiment analysis can be used to gain insights and intelligence by businesses for a variety of different purposes. An AI-powered media sentiment engine monitors digital content and extracts relevant trending topics from news feeds for different industries. The engine recognises which topics are being discussed, then filters and rates them with a sentiment score. Intelligent news screening organisations assign ‘relevance scores’ to news articles in order to rank them and focus a team’s attention on the most pertinent ones. For example, tracking content on an emerging sustainability topic, such as ESG, or articles with information that might lead to a potential business deal.

Verifiable ESG Monitoring

Our clients are using Aicadium’s AI-powered dashboards to analyse ESG reports and media sentiment which allows efficient assessment and reviewing of material topics required for ESG reports. Our consent based data platform is used for ESG risk scoring and provides verifiable ESG performance to be shared between the data provider, borrower, and lender. This in turn provides faster underwriting and a lower cost of monitoring for ESG financing. Intelligent news screening and sentiment analysis are a couple of ways that FSIs are achieving verifiable ESG monitoring.

Case Studies

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