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Explore the demo for Intelligence-Led Investigations for AML 🔎
Connecting data at scale creates the context required for investigators to detect even the most complex financial crime, helping organizations stay one step ahead of criminals. Quantexa's Intelligence-Led Investigations provides the tools that investigators need to efficiently and effectively investigate complex money laundering cases that other solutions would fail to detect.9Views0likes0CommentsExplore the New Search Demo 🔎
Our enhanced Search functionality (aka "Search 2") is here as of version 2.7.7! With a redesigned, entity-focused UI, faster setup, and better performance, it’s built to make using Search better than ever. 💬 We want your feedback! Explore the full release details in the New & Improved Search Functionality release announcement Check out the New Search Demo on the Product Demos page 🔒member exclusive Go to the Search tile under the General Availability tab in the Product Roadmap & Ideas Portal to share your feedback 🔒member exclusive Try out the demo now and see how the new and improved Search can enhance your Quantexa experience.Coming Soon: Your New Quantexa Community Launches Wednesday 21st May!🚀
🚀🚀🚀We’re just about ready to welcome you to the new Quantexa Community, launching on Wednesday, 21st May! 🚀🚀🚀 Here’s what you can expect: ✅ Your existing login will still work – no need to reset your password. 🔗 Bookmarked URLs will continue to work – your saved links will take you right where you need to go. 🛠️ Support remains the same – the team is here if you need anything during or after the move. 🌟 Exciting new features and content – easier navigation, smarter notifications, and more ways to connect. Want to Get Involved? There’s still time to contribute to shaping the new Community: 📝 Sign up for user testing and feedback sessions 💌 Share ideas in the Community Suggestion Box 📊 Keep an eye out for post-launch surveys – your input matters! A reminder of why we're moving… As the Quantexa Community grows, it’s time for us to upgrade to a space that better matches the needs of the Community Members. Our new community platform will offer: 💬Better conversations with improved discussion features 🔔Smarter notifications so you never miss important updates 🎉More events, resources, and ways to get involved ✨We can’t wait for you to explore everything the new space has to offer. Thanks for being part of the Quantexa Community!✨23Views1like0CommentsIntroducing Q Assist:Context-aware Gen AI underpinned by the Quantexa Decision Intelligence Platform
At QuanCon 2025 in March we announced that the first release of Q Assist was coming out of a pilot phase and would soon be made available to license for both existing and new customers. Q Assist is now available in Early Access and can be deployed in solutions and platform deployments on version 2.7. So, what is Q Assist? Q Assist brings Generative AI capabilities to our Decision Intelligence Platform and the solutions we offer at Quantexa. It is a modular platform component that includes a conversational UI, orchestration capabilities, configuration settings, and scalable APIs for copilots and LLMs. It embeds itself into everyday tasks and workflows for a more productive workforce and is grounded in the connected and contextual data and functionality our Decision Intelligence Platform offers. Q Assist connects a customer licensed and deployed LLM and the Quantexa Decision Intelligence Platform. It works alongside Quantexa users, embedding itself into everyday tasks and workflows and is grounded in the full set of data and functionality our Decision Intelligence Platform offers. Check out our Q Assist FAQs for more information. Who can license Q Assist? Q Assist is available in Quantexa Platform version 2.7 and is licensable as an add-on to existing Financial Crime, Fraud, Customer Intelligence, KYC, and Risk solutions. In this release, Q Assist is limited to customers in EMEA and North America for English Language deployments only. *For more information on licensing and pricing please contact your Technical Account Partner or schedule a demo here. Core components of Q Assist This Generative AI Technology Suite is made up of 3 core components. Q Assist Copilot (Available in Early Access release) An intelligent AI copilot that delivers instant, context-driven insights through natural conversation. The copilot works alongside Quantexa users revolutionizing how you work by surfacing critical data and context, streamlining repetitive and time-consuming tasks, and generating reports through a natural language interface. The Prompt Library (Available in Early Access release) A centralized repository of trusted prompts that standardizes data interactions, report writing, and investigation and research tasks across teams. The Orchestration Layer (Coming soon) Q Assist's orchestration layer is the “conductor”. A unified AI integration hub that streamlines data flow, coordinating multiple systems and workflows seamlessly. It ensures that user inputs are processed, and contextual data is incorporated from relevant platform components, and outputs are generated in a coherent, consistent, and explainable manner. The orchestration layer handles tasks like: Ensuring that the correct context is applied to each request Retrieving and integrating information from multiple sources to enrich the response Coordinating workflows and routing requests to the appropriate models and platform components, applying business rules and guardrails, using an agentic architecture Integration with existing licensed or customer-built copilots Q Assist in Action You can see Q Assist in action with our interactive demo in our Community Demo Hub and below we have highlighted some of the core features of Q Assist highlighting the benefits of each. Research Assistance: Q Assist streamlines access to a connected and contextual data foundation through an intuitive conversational interface. This functionality empowers users to instantly retrieve critical insights by talking to their data. Insights about customers, counterparties, suppliers, transactions are available through natural language queries which informs more thorough and faster investigative and research processes with rich contextual insights. By enabling self-service access to essential data, you can: Significantly reduce dependencies on dedicated data teams, Cut down manual research time while Deliver accurate and trusted information to those who need it when they need it As a result, investigation teams, relationship managers, analysts can benefit from enriched data that highlights hidden risks and identifies overlooked opportunities, allowing for quicker resolution of complex cases. This level of immediate, detailed access not only accelerates investigations but also enhances relationship management and customer service productivity, leading to a more responsive and personalized customer experience. Prompt Library The Prompt Library feature in Q Assist is designed to centralize and streamline the creation, management, and sharing of trusted prompts across teams. This tool allows you to build role-specific queries that can be easily reused across functions, ensuring that data interactions are both consistent and secure. With the Prompt Library, you can: Foster controlled, repeatable workflows for decision-making Level the playing field across your organization—making every analyst as effective as your top performers Establish consistent and secure workflows that adhere to best practices and compliance standards The Prompt Library empowers your teams to achieve optimal efficiency and alignment with organizational goals. Report Generation Q Assist’s Report Generation feature enables you to streamline the creation of a wide range of reports from SARs and EDDs to intelligence reports, risk assessments, and executive summaries, all with a single prompt. This functionality uses predefined templates to build consistency in reporting for various types of reports, such as escalations and SARs, or even customer emails and talking scripts. As a result, users can: Dramatically reduce the time it takes to create and process standardized reports Ensure data accuracy across multiple report formats Reinforce consistent and standardized reporting processes Reduce errors through predefined workflows Overall, the Report Generation feature helps reduce the time spent preparing for customer meetings, ensuring that summarized information is readily available for strategic decision-making. Contextual RAG Q Assist grounds every AI-generated response in rich, contextual data, significantly reducing the risk of hallucinations and inaccuracies. By anchoring outputs in relevant organization specific data and information, this feature Champions transparency with fully traceable and explainable responses. Increases trust and adoption internally Ensures you meet stringent regulatory requirements. Each response is not only more reliable and accurate, but it also comes with clear explanations that empower users to understand the underlying rationale and data behind every decision, ensuring that data-driven insights remain both effective and explainable. More information on Contextual RAG: Pre-recorded Webinar: Your AI Copilot is Only as Good as Your Data Foundation Blog post: Your AI Copilot is Only as Good as Your Data Foundation Underpinned by the Quantexa Decision Intelligence Platform and tailored to critical industry use cases, Q Assist empowers teams to close the gap between data and decision igniting a more productive workforce. Get in touch today to see how Q Assist can revolutionize the productivity of your frontline staff.99Views1like0CommentsIntroducing the Quantexa Streaming Best Practice Hub
We're excited to announce the launch of the Quantexa Streaming Best Practice Hub – a curated collection of technical guidance, real-world examples, and expert insights to help you build smarter, faster, and more maintainable streaming solutions on the Quantexa Platform. Whether you're an Engineer, Architect, or Product Owner, this space has been designed to support you throughout your streaming journey. What’s in the Hub? You’ll find a growing set of resources, including: Streaming best practices – Proven architectural patterns, delivery guidance, and maintainability tips from implementations across Financial Services, Government, and other sectors. Deep-dive technical articles – Topics such as Kafka internals, message ordering, stream optimization, backpressure handling, custom scoring apps, and performance tuning. Persona-based guidance – Actionable recommendations tailored to different roles across delivery and platform teams. Solution patterns – Practical examples of how Quantexa streaming supports real-time detection, scoring, enrichment, and resolution use cases. Blog series – Honest, hands-on posts drawn from real implementation experiences and lessons learned in the field. Why this matters? Quantexa streaming is powerful, but great outcomes rely on strong solution design and platform alignment, not just configuration. This hub exists to help you get there faster and more confidently, with guidance built on real-world challenges and delivery experience. It’s also about helping you deliver resilient, scalable, and easy-to-maintain streaming solutions. Where to find it? The Streaming Best Practice Hub is now live on the Quantexa Documentation site and the Quantexa Community. You can jump straight into some key content below: Documentation Site Streaming best practices Planning a streaming solution Designing data ingestion pipelines Deploying a streaming solution Monitoring a streaming solution Debugging and troubleshooting Community Platform Architecture: Kafka Streaming Using Quantexa Kafka Streaming for the First Time Designing a Kafka Solution to Meet Functional and Non-Functional Requirements Data Streaming Design Principles to Enrich Input Messages Lessons Learned from a Streaming Lending Fraud Project Maintaining Message Ordering in Kafka Searching Entities Without Document Ingestion Optimizing Entity Resolution and Graph Expansion Help us grow this? This is just the beginning—we want this hub to evolve based on your needs. If there’s content you’d like to see added or challenges you’d like help addressing, we’d love to hear from you. Your feedback and ideas will directly shape future updates, and contributions are always welcome. To share your thoughts, feel free to leave a comment on this post or email us at community@quantexa.com.Entity Resolution Configuration & Parsing Health Checks
Entity Resolution (ER) and good Entity quality underpin all Quantexa deployments. The accuracy of Entity configuration and parsing are two areas that impact Entity quality. These articles outline Entity Resolution (ER) health checks and Parsing health checks to be carried out by the development team on deployments. This allows the team to identify, prioritize, and fix any potential underlying issues that could be reducing Entity quality. These checks must be completed as part of the initial deployment, but also periodically over the lifetime of the deployment. New product functionality, and data changing, may mean configuration needs to be changed, or enhanced over time. Topics covered: Entity Resolution Health Checks Pre-requisites Resolver JSON configuration health check steps Perform a comparison to the latest core Resolver JSON configuration Review configured Element exclusion criteria Review configured exclusions for Compounds in the relevant template Compound model health check steps Are all required Compounds being generated in ETL for the relevant Document types? Are Compounds being generated to populate elements required for exclusions in other Compounds? Do the traversals all look sensible? Do you have good coverage of unit tests? Parsing Health Checks Pre-requisites Parsing health check steps Is your deployment using the latest versions of Parsers? Has your deployment applied custom Parsing functions or wrappers? How does your Parsing compare to best practice Parsing? How well are the Parsers performing per source and country? How well-populated are the Parsed fields?New Quantexa Community Launch - Coming Soon!
We’re excited to announce we will be launching a new Quantexa Community! In mid-May, the Quantexa Community will be moving to a new home, and we can’t wait to share it with you! Why We’re Moving As the Quantexa Community grows, it’s time for us to upgrade to a space that better matches the needs of the Community Members. Our new community platform will offer: 💬Better conversations with improved discussion features 🔔Smarter notifications so you never miss important updates 🎉More events, resources, and ways to get involved What You Need to Do There is nothing to do for now! We will share all the information you need ahead of the move. Want to Get Involved? 📝 Sign up to get involved in Community testing and feedback sessions. 💌 Drop us a note into the Community Suggestion Box. 📊 We’ll be sending out surveys after launch – give us your feedback. Thank You!35Views1like0CommentsJoin us to hear from the Quantexa Product Team!
About the event Join our Product leaders for a webinar which will give insight into Quantexa's vision and roadmap. We'll explain how we prioritise what we work on and introduce our new Product & Ideas portal - which gives you more transparency into our roadmap and shows how you can influence our path forwards. “The Voice of the Customer is one of the most important inputs into our product process and helps us evaluate the development needed to best serve our customer base and target markets. The access that the Ideas Portal provides to direct customer feedback and thinking is incredibly valuable as part of the shaping of Quantexa's Product in both the short and longer term.” Adam Lawrance-Owen – VP Product RSVP here (Log in) On the agenda: Product vision and strategy Adam Lawrance-Owen, VP Product, will explain our product vision and our path to category leadership in Decision Intelligence Introduction to Product at Quantexa Adam will then explain what product management is all about and why it’s critical to the success of a software company, as well as how we do it at Quantexa Launching our New Community Product & Ideas Portal Anastasia Petrovskaia, Product Manager, will introduce the new portal, explaining the importance of Voice of Customer, Challenges faced in capturing this, and the new approach together with its benefits. Roadmap update Ana will discuss how product vision and strategy will drive a customer focused roadmap going forward How to use the Portal Quantexa's Community team will walk through how to use the Portal, from reviewing ideas to adding feedback, to watching as they progress and become a part of Quantexa Q&A Join for live Q&A 💬 You can also submit questions in advance below or by sending them to community@quantexa.com.15Views1like0CommentsQuantexa and Accenture work together to help end tax fraud!
Check our this new Quantexa and Accenture eBook - Quantexa and Accenture Work Together to Help End Tax Fraud. Together, we're working with revenue agencies, governments, corporate-level tax authorities, and other tax departments to help gain access to more accurate data they can trust. They are better able to analyze, manage, and unify disparate data, uncover hidden risks, and drive proactive, predictive decision-making at scale. With the team’s deep knowledge of complex regulatory and compliance environments, organizations can improve strategic processes. Used with other automated identity theft and fraud detection implementations, the team helps them to effectively combat criminal efforts by today’s global tax fraudsters. Download a copy of the paper here. https://www.quantexa.com/resources/quantexa-and-accenture-work-together-to-help-end-tax-fraud/56Views0likes0CommentsWIP - Score Rolling Behaviour
Please respond to the below questions: Has this content piece had a technical review by a TL (from a content accuracy and editorial perspective) Technical / Product content only The editorial review must follow guidance from the Docs team, please use the resources linked at the bottom. If part of the Tech Lead team - please use the tag 'Tech-Lead-Content' Please specify the most suitable section and sub-section of the Community Library for publishing e.g. Service Operations: Operational Support Who is your piece aimed at? (Data Engineer, Data Scientist, Investigator, Delivery Manager, etc.) Can the article be linked to another piece/area of the Community?: A Specialist User Group (Data Management, FinCrime, KYC, or Insurance) Another related article in the Community What is the purpose of your piece? Please include a summary line at the top of the blog. Have you reviewed the content classification guidelines ? Can the content be open to the public (i.e. searchable via Google) does it need to be restricted, or internal only? Please choose from the following: ✔️ Public ✔️- Content which is publicly viewable on the internet. Please note, the assumption is all content should be made public where possible due to the increased impact benefits 🔑 Restricted 🔑- Viewable by registered Customers and Partners, in addition to employees. For restricted content - Please provide a rationale for why this must be restricted. Create a short summary which can be posted in a public area of the Community - this short summary will link to the article and promote it. 🛑 Internal 🛑 - Viewable by Quantexa employees only. Is there an urgency to publishing or preferential timeframe? Editorial checklist: Have you capitalized any Quantexa terms like 'Entity', 'Document' and 'Entity Resolution'? (check the docs site Glossary if you aren't sure) Have you included a short intro paragraph at the top of the article, outlining the purpose and target audience, and a concluding paragraph including any relevant links to associated content? Have you used American (US) English - for example, 'analyze' not 'analyse'? Have you used headings, bullet points and tables to break up the content and grey boxes for code snippets? Please include aliases, otherwise known as lines or alternate keywords for any terms you have used that are known by multiple names. This helps people find your piece even if they have not used the exact terms you've included. E.g. 'Elasticsearch (or Elastic)' If you need further editorial guidance (beyond the guides below) , tag James Parry or Ffion Owen. Editorial Resources Become a better writer ↗ Content models ↗ Quantexa writing guide ↗ Editing tools: Grammarly and Hemingway This article covers rolling up and rolling down Scores. These concepts use the Score output from one level as input for another Score or Scorecard. Knowing these processes helps us build Scorecards. This approach allows us to capture risk accurately at all levels. For context on what a Score means at each level (Document, Entity, or Network), refer to the Score Output Levels page. This article is for Business Analysts, Business End Users, and Subject Matter Experts (SMEs). By grasping the concepts of rolling up and rolling down scores, they can make better decisions. This knowledge helps them aggregate risks from different data levels. These levels include Document, Entity, and Network. Definition Rolling Up Rolling up a Score means aggregating Scores from a lower level (e.g., Document) to a higher level (e.g., Entity or Network). This enables a wider risk assessment. It merges granular insights into a single, higher-level Score. For example, rolling up multiple transaction Scores to the Customer level gives a more fuller risk assessment. This approach incorporates all relevant transaction risks for that Customer. Rolling Down Rolling down a Score means distributing a Score from a higher level (e.g., Network or Entity) to a lower level (e.g., Document). This ensures that risk assessed at a broader level is reflected at more granular levels. Rolling down a Network-level Score to all linked customers allows them to inherit relevant risks from their connections. Rolling up and rolling down Scores helps group all Scores at the right level for Alerting Why We Need To Roll Up or Down Scores operate on different Quantexa objects: Documents, Entities, and Networks. In many cases, we need to analyze risk at different levels: When creating a Customer-centric Scorecard, you must combine several transaction-level (Document) Scores. This will give you the Business (Entity) level score for a Business Customer. This makes sure that all important transaction risks add to the total Business Customer risk. When we look at risk on a higher level, like the Network, we need to roll down the Network-level Score to all linked Customers. This way, we can give Customers the relevant risks based on their Network connections. To structure risk assessments well, you need to understand the output level for each Score and Scorecard. Examples Example of a Roll-Down Score An example of a roll-down Score is one that aggregates Entity-level Scores to the Document level. For example, a Customer Document may be linked to multiple Entities that trigger the same type of Score. Suppose two individuals, John Hammerling and Norris Burke, are linked to the Customer Document for Turner & Page Book Shop Ltd. They both trigger the IndividualWithMultipleNationalIDs Score because they have several national IDs. We then introduce a new score: the IndividualWithMultipleNationalIDsRolldown Score. This score adds up the overall risk level of the Customer Document (Turner & Page Book Shop Ltd). Figure 1: Example of a Roll-Down Score The "Potential Identity Manipulation" Score adds up the Individual Entity Scores. The two Scores are aggregated to show the Document-level risk tied to the connected Entities. Example of a Roll-Up Score An example of a roll-up score is one that aggregates information from multiple Documents related to an Entity. For example, an Account Entity may be scored by aggregating over the Customer Documents associated with it. The goal of rolling up is to create a more comprehensive Account Entity Score. It does this by using all the available Document-level data. Another example would be a Business Entity linked to several Watchlist Documents. To determine if this Business Entity has Sanctions, we check each associated Watchlist Document. If a Watchlist Document contains relevant sanctions information, a Score is triggered. If no relevant information is found, the Score returns nothing and moves to the next Watchlist Document. After reviewing all Watchlist Documents, we use an aggregation function. This function creates an overall Entity Score using results from each Document. Figure 2: Example of a Roll-Up Score Where and How to Configure [TBC]38Views0likes1Comment
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