TL;DR:
- Sigma uses AI to offer robust risk management solutions for financial institutions.
- Their AI combs through millions of articles to filter out negative news and categorize risks.
- Utilizes large language models (LLMs) and transfer learning for cutting-edge performance.
- Adverse News model extracts relevant risk-related news from diverse sources.
- Named Entity Recognition (NER) technology spotlights specific entities from articles.
- Sigma aims to establish strong entity relationships for improved risk identification.
- The Sigma360 Platform allows swift assessment of connections between entities.
- Sigma’s AI platform is favored by leading financial institutions for real-time risk intelligence.
Main AI News:
In a transformative move that has earned them a spot in the prestigious AIFinTech100 for 2023, Sigma is at the forefront of innovation, harnessing the power of AI to offer robust and scalable risk management solutions. With its unique and cutting-edge use of AI technologies, Sigma has established itself as an indispensable platform for both current and prospective clients.
At the heart of Sigma’s AI capabilities lies its ability to comb through a staggering 4.5 million articles each month from over 200,000 publishers. The primary objective here is to filter out any negative news or events involving companies or individuals that could pose potential risks. By employing advanced event tags, the AI swiftly identifies and categorizes these risks, streamlining the entire due diligence review process. Notably, articles related to the same real-world event are intelligently grouped together, facilitating efficient analysis.
The key to Sigma’s groundbreaking performance in news and adverse news screening can be attributed to its expert utilization of large language models (LLMs). These models, highly proficient in handling various natural language processing (NLP) tasks, have proven to be the backbone of Sigma’s success.
Central to their AI prowess is the Adverse News model, selectively extracting news deemed significant for risk assessment from an impressive pool of over 200,000 news sources. This model benefits from transfer learning derived from the powerful DistilBERT LLM, while also leveraging event tags identified by a supervised deep learning model built on the RoBERTa LLM.
To achieve such impressive accuracy, Sigma’s model has been meticulously trained on a proprietary and continuously expanding corpus of expertly tagged news articles. It encompasses a wide range of events, spanning from terrorism and financial crime to environmental crime, legal risk, arms trafficking, and human rights violations.
Sigma has put the power in the hands of risk managers, allowing them to fine-tune their teams’ efficiency by choosing specific event tags that align with their interests. Moreover, Sigma’s model goes above and beyond by considering news events that may not be directly adverse but still hold significance for risk managers.
Another standout feature is Sigma’s Named Entity Recognition (NER) technology, which expertly spotlights and extracts specific entities such as organizations, individuals, and locations from news articles. This cutting-edge technology significantly enhances the relevance of articles linked to the entity of interest, surpassing outdated keyword matching systems. Sigma’s NER model capitalizes on BERT (another LLM), driven by self-attention and other advanced methodologies.
Looking forward, Sigma is steadfast in its commitment to improving its AI platform. Their future plans include displaying only those articles where the risky actors match the entities of interest to users. By extracting pertinent entity relationships from news articles, Sigma aims to establish robust links between entities, providing risk and compliance teams with an unparalleled advantage in identifying network-based risks.
Sigma’s matching system stands as an industry leader, boasting a ten-fold increase in speed, thanks to the implementation of Golang. As part of their ongoing efforts to enhance accuracy, Sigma is set to incorporate AI and deep learning models into their matching algorithms.
In recognition of the changing landscape of global crime networks, Sigma is gearing up to launch the revolutionary Sigma360 Platform. This platform will address the shortcomings of traditional strategies that focus solely on individual transactions or client screening. The Sigma360 Platform empowers users to swiftly and efficiently assess connections between entities on a large scale. The platform’s network graph feature enables users to explore an entity’s connections to the broader world, delivering a single stream of related risk intelligence.
The Sigma AI platform has garnered widespread acclaim among leading financial institutions and high-risk businesses. Its ability to identify, screen, monitor, and review clients and their relationships in real-time has made it the platform of choice. Offering seamless integration of data updates on news, corporate registries, sanctions, and enforcement actions through a comprehensive dashboard, Sigma’s AI is truly a game-changer. Additionally, boasting best-in-class entity resolution and a network-based knowledge graph, Sigma ensures that no aspect of risk goes unnoticed.
Conclusion:
Sigma’s innovative AI-driven platform is revolutionizing risk management in the financial market. By harnessing the power of AI technologies, Sigma offers robust and scalable solutions to identify, categorize, and monitor risks from a vast pool of news articles. Their emphasis on large language models and transfer learning ensures cutting-edge performance, providing clients with unparalleled risk intelligence. The Sigma360 Platform further enhances risk assessment by exploring connections between entities swiftly and efficiently. Overall, Sigma’s AI platform positions them as a trailblazer in the industry, catering to the evolving needs of risk management and compliance in an increasingly complex market. Financial institutions that adopt Sigma’s AI technology gain a competitive advantage in staying ahead of the curve and mitigating potential risks effectively.
