Although legal departments play a key role in minimising risks and advancing corporate objectives, their investment in transformational technology initiatives tends to lag behind other functions. However, according to Gartner, corporate legal departments are under intense pressure to improve efficiency and responsiveness, and are now turning to automation and artificial intelligence techniques, including machine learning and natural language processing, to address this. These technologies streamline the identification of legal risks, facilitate more efficient operational processes, and improve overall handling time for legal tasks.
BlackSwan Technologies’ ELEMENT, a cloud-based offering trusted by world-leading organisations, is a customisable platform well suited for sophisticated corporate legal departments and global law firms looking to modernise operations through advanced intelligence. The low/no code platform enables legal/business users to design and complete proofs of concept, which integrate with existing systems and corporate-wide business processes, within days to weeks. The data sources, knowledge representations, AI models, alerts, workflows, and data visualisations can all be tailored. Below are relevant examples of ELEMENT’s AI-driven capabilities:
Reputation Due Diligence and Real-Time Risk Monitoring
Monitor potential risks associated with stakeholders in real-time to limit exposure and mitigate damages
A recent survey by Deloitte found that 87% of global executives view reputation risk as “more important” or “much more important” than other strategic risks. ELEMENT enables enterprises to monitor brand mentions by interpreting data and sentiment from thousands of sources, including news and social media, using natural language processing and contextual analysis. The platform can also pre-screen and monitor key stakeholders against specialised information, such as legal filings and economic sanctions watchlists. Moreover, ELEMENT automatically builds a knowledge graph that represents your organization’s relationships to customer and supplier entities—and business relationships amongst themselves, in order to uncover secondary relationship risks and evaluate the potential impact. Reputation monitoring can also apply to adverse media about your own firm, which can be addressed by multiple departments collaboratively using ELEMENT’s case dashboard.
Advanced Contract Review and Performance Analysis
Review contract terms for errors and acceptability, then assess performance to gauge the fulfilment of contract obligations
In a recent study by Gartner, 76% of legal and compliance leaders cited improving contract management efficiency as a top challenge. Corporate legal departments can utilise ELEMENT to improve contract management by harnessing data from internal sources to review contract terms and assess contract performance. The system uses contextual analytics to categorise proposed contract terms; and thereafter, screen for and highlight uncommon or unfavourable terms. ELEMENT can also propose specific contract terms based on automatic review and categorisation of the contracting party, while considering factors such as size, rating, location, business history, and performance, as well as, length of the contract, contract value, and contract criticality. Once the contract is accepted, the platform can monitor trends and automatically raise alerts for anticipated contract risks, while monitoring key facts of the contracting party, including ownership, performance, and adverse media. To assess the fulfilment of the contract terms, ELEMENT can compare contractual obligations with corporate supply chain data against its repository of performance terms.
Discover, index, and analyse legally-admissible documents from multiple sources and more accurately determine the relevance
AI enables legal teams to more effectively prepare for litigation by cross-analysing case facts and comparison cases. Counsels can leverage ELEMENT to securely compile relevant data from multiple internal sources, as well as from previous cases, along with metadata about source citations and related concepts. The platform can leverage natural language processing to analyse written documents about thousands of cases and identify the most relevant; and sort through reams of pre-trial documents according to relevant terms. ELEMENT also utilises advanced knowledge graphs to uncover similar parties, terminology and relationships, and creates comprehensive knowledge databases dedicated to specific cases for future reference and analytics.
Contextual Enterprise Search
Build a consolidated repository of internal corporate knowledge and more intelligently process queries and prioritise search results
More than many departments, a legal team is likely to appreciate the value of an intuitive, consolidated search across all corporate information… and to recognize the limitations to effectiveness when this doesn’t exist. Now, that law team can serve as evangelists and cross-functional collaborators to introduce such a capability while addressing other strategic initiatives. ELEMENT builds a consolidated repository of all corporate knowledge, by incorporating both structured and unstructured information through an agile “knowledge mesh” architecture that references data “where it lives.” The platform’s cognitive search engine uses natural language processing and machine learning to analyse the context of queries, thereby more intelligently retrieving and prioritising search results. ELEMENT can construct a novel “knowledge graph” that represents cross-department knowledge about parties, their activities, and their relationships, resulting in faster and more meaningful search results and clearer situational awareness. All this is possible while targeting ELEMENT for essential initiatives in areas such as customer relations and compliance.
BlackSwan Technologies’ ELEMENT is a complete enterprise AI platform that can be customized to support multiple business use cases, including Data Privacy Management, Compliance Intelligence, Vendor Risk Assessment, and Cyber Threat Detection and Remediation.