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In today’s complex and rapidly evolving business landscape, risk management has become a paramount concern for organizations across various industries. Traditional risk management methods, while effective, often fall short when it comes to dealing with the intricacies and unpredictability of contemporary risks. However, emerging technologies are reshaping the risk management landscape, and one of the most promising tools in this domain is Generative AI (GenAI). This innovative technology is revolutionizing the way organizations tackle a wide range of risks, including environmental, social economic, reputation, geopolitical, infrastructure, and technology risks.

 

MitKat Advisory, a leader in risk management and intelligence, offers a cutting-edge solution for comprehensive risk management. Its operational Risk Intelligence Platform, datasurfr.ai, harnesses the power of GenAI to help organizations pre-empt the risk events, mitigate emerging risks and build resilience.

 

The process outlined below describes the utilization of GenAI by datasurfr.ai for processing risk data and intelligence.

Data Collection: The first step is to collect news articles from open-source media sources. These articles cover a large spectrum of real-time worldwide events and serve as the foundation for datasurfr.ai’s database.

Semantic Annotation: The articles in the database are then semantically annotated, which refers to the analysis of the content of the articles to identify entities such as individuals, organizations, locations, and so on, as well as the relationships mentioned within them. This is done using a named entity recognizer (NER) and natural language processing (NLP) tool. Semantic annotation allows for a better understanding of the significant elements of articles.

Data Reference and Identification of Location: Once the key elements of the articles are identified, the next step is to extract the date and location using a machine learning model and geocoder. This aids in understanding the duration and scope of the event described in the article.

Cross-Lingual Article Matching: This step involves the matching of articles across languages in the database. This step is done because different news sources can publish information about the same event in various languages. Cross-linguistic article matching allows data aggregation from multiple sources.

Detection and Removal of Duplicates: As indicated in the step above, multiple sources may report on the same event. The duplicate articles are found and eliminated in this step. As a result, the result is of higher quality.

Clustering based on Similarity: Following the removal of duplicate articles, the AI goes on to find articles with similar topics and groups them according to the level of similarity. The similarity can be determined using various natural language processing techniques.

Cross-Lingual Cluster Matching: Similar to cross-lingual article matching, this stage identifies the articles that are similar in content across languages and groups them according to similarity.

The latter four steps aid in the compilation of data and the reduction of clutter, both of which raise output quality.

Information Extraction and Event Formation: The information about the event is retrieved from the clustered events, including the specifics of what transpired, the areas affected, the time of the occurrence, and the individuals involved and affected. After that, a summary of the article’s key points is provided.

Event Template: After being compiled and extracted, the data is entered into an event template with pre-filled fields to help with data organization.

Identification of Related Events: When an event occurs, numerous articles may address diverse aspects of the event, along with related events occurring in various locations. This stage involves the identification and establishment of connections between events that share contextual relevance.

Human Oversight in GenAI Event Processing: Lastly, a human layer manually administers the events that GenAI assisted in identifying, summarizing, clustering, and connecting. Our team of analysts examines the information’s quality and accuracy, often involving data curation and verification.

User-Friendly Interface: Following an overview from the analyst team, the data is accessible via an intuitive front-end interface. It can also be accessed through an Application Programming Interface (API), the interface enables users to browse, search, access, and interact with the data.

 

Hence, through leveraging GenAI technology, datasurfr.ai empowers businesses to proactively identify and address operational risks such as security, environmental, social, economic, reputation, geopolitical, infrastructure, and technology risks. It provides actionable insights, allowing organizations to make informed decisions, mitigate risks, and protect their assets and reputation.

 

To learn more about how datasurfr.ai can benefit your organization and enhance your risk management strategies, please reach out to MitKat Advisory at contact@mitkatadvisory.com or Request a free Demo: https://datasurfr.ai/contact-us. Embrace the future of risk management with GenAI and ensure your organization is well-prepared to tackle the challenges of today and tomorrow.

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