Recent Discussions
Migration Guidance to Parsers 4.x | Release announcement
We are happy to share that migration guidance to Parsers 4 is now available on the Quantexa Community. Why & How to Plan a Parsers 4 Migration | Quantexa Community This guide provides support with planning and estimation of the migration to the latest versions of Parsers, and introduces available migration options, as well as provides detailed recommendations on how to make the upgrade smooth and straightforward. Step-by-step Guide to Migrate to Parsers 4.3 | Quantexa Community Step-by-step Guide to Migrate to Parsers 4.2.4 | Quantexa Community As a reminder, Parsers 4 is a requirement for Quantexa 2.8+. More information about the releases can be found here: Welcome to Parsers 4.3 | Release Announcement | Quantexa Community Welcome to Quantexa 2.8 | 2.8.0 Release Announcement | Quantexa Community
How Decision Systems 2.8.3 integrates with Data Viewer and Explorer
We’re excited to announce that Data Viewer and Explorer are now fully integrated into Decision Systems with version 2.8.3. Why This Matters Data Viewer helps investigators and analysts explore and filter data efficiently during investigations. Data Viewer uses the Explorer API to query data sources and present them in a structured, interactive format. This means: Investigators and analysts can automatically replicate score logic, saving time, minimizing errors, and delivering scale. Score logic is more easily represented, for intuitive filtering of relevant transactions. Complex scores are more easily investigated. How Data Viewer and Explorer work with Scores When an investigator selects a score, Data Viewer and Explorer can display all transactions that triggered that score. Transactions can be shown via queries or transaction IDs. Using queries allows investigators to see the queries in Explorer for deeper investigations beyond the original score. Benefits of integration with Decision Systems Faster implementation: Saves ~0.5 days per score—no longer need to set up Data Viewer and Explorer manually. Reduced maintenance: Queries are automatically sent to Data Viewer and Explorer—no need to manually replicate score logic or update UI queries after tuning. Accuracy and confidence: Automated integration ensures correct transactions are displayed to investigators in Data Viewer and Explorer. Improved user experience: Queries can be explored and amended in Explorer; Data Viewer now supports all scores. Explainer videos Watch these short videos to learn more: 01 Data Viewer and Explorer See how the integration appears in the UI and learn how to configure Data Viewer and Explorer. 02 Data View Integration Setup Understand the data ingestion requirements for using Data Viewer and Explorer effectively.Decision Systems for Trade Finance - GA Announcement
We’re excited to announce that Decision Systems Trade Finance has now moved to General Availability as of 2.8. This is a significant milestone that lays the foundations for Decision Systems within Quantexa’s Decision Intelligence Platform. What Are Decision Systems? Decision Systems are Quantexa’s configuration-driven scoring solutions, built on Assess. They are designed for rapid configuration of use case specific decisioning models —enabling faster deployment, consistent performance, and easier adoption across supported use cases. Decision Systems empowers users to: Leverage the full power of Assess without writing code. Move quickly from resolved Entities and Graphs to actionable insights. Use out-of-the-box scores and scoring models based on best practices and domain expertise. Focus on decision execution, reducing time spent on data wrangling and complex data engineering. Supported Use Cases For our most proven solutions in Financial Crime monitoring and compliance, we provide baseline coverage across the entire decision lifecycle, from data, to insight, to decisions with Decision Systems. Supported use cases in 2.8 Trade Finance – General Availability Capital Markets – Early Access Retail Banking – Early Access If you're working on a FinCrime use case not listed, Decision Systems may still be applicable depending on maturity. To determine fit—even for Trade Finance—please speak to your Quantexa Solution Architect to get started. Highlights Although Trade Finance is being announced these highlights are applicable to all supported use cases. Simpler User Experience Conditional Configuration You can now dynamically generate only the necessary configuration to deploy your scoring solution, reducing manual effort, improving clarity, and enabling scalable support for new use cases and product capabilities. Simplified Scorecard, Severity, and Scorecard Group Configuration Streamlined configuration ensures that scores and triggers contribute logically and transparently to final decisions—reducing configuration time and improving investigator efficiency. Simplify Score Roll-Up to Customer, Counterparty, and Relationship You can easily configure the aggregation of transaction, entity, and network risk to customers, counterparties, and relationships without duplicating setup thus, reducing required knowledge, setup time, and improving consistency. Centralized Definitions and Validations on Entity, Relationship and Network Scores You can benefit from centralized configuration, validation, and auto-generation of inputs which ensures consistency and reduces time spent on setup and debugging. Optimized Outcomes Segmentation You can now configure segmentation across scores and scorecards with a simplified, centralised setup which tailors scoring to the subject being alerted, all without modifying your ETL. Score Coverage New customer and relationship transaction scores. Including round amounts, unusual transaction types, dormancy, and hub-and-spoke patterns. This greatly expands coverage and enables detection of a wider range of insights. Trade Finance General Availability Trade Finance refers to the provision of finance and services by Financial Institutions (FIs) for the movement of goods and services, domestically or cross-border. Trade-Based Money Laundering (TBML) is a broad term that includes illicit transactions both within and outside formal Trade Finance operations. This use case focuses specifically on TBML risks represented by transactional documents within Trade Finance. The following graph illustrates a typical Document–Entity model for the Trade Finance use case: For full details of the release, please see Decision Systems on the documentation site. Release notes Migration guide Trade Finance Explainer Videos Our series of explainer videos guides you through deploying a Trade Finance FinCrime scoring solution using Decision Systems. These videos are not a substitute for following the documentation during a deployment. Please ensure you refer to our documentation site for the complete deployment tutorial. 00. Introduction We outline the key outcomes of Decision Systems and summarize the key configuration steps. The following videos will describe those steps in more detail. 01. Configuring Your Solution To get started, we show how to select your use case and define scoring subjects, such as customers and their relationships. 02. Performing Data Mapping Next, we show how to map outputs from data ingestion to the selected use case. The mapping process ensures that the original meaning of the data is preserved and remains consistent across your organization. No changes to the source data are required. 03. Configuring Your Scores We walk through the process of configuring a score using available templates. We show how to define severity levels and UI descriptions. As an example, we use the score Customer with Social Link to Watchlist. 04. Configuring Your Scorecards A scorecard is a key part of a scoring system, translating the complex data into a structured, interpretable score that drives consistent, data-informed decisions. This video shows how to build scorecards by selecting scores, assigning weightings, and grouping related scores. 05. Configuring Your Alerting A critical part of the scoring process is communicating key insights to users and stakeholders. We outline how to configure alerting logic to ensure alerts are meaningful and contain new insights. We explore four key alerting modules: When the overall score exceeds a threshold When a scenario triggers but hasn’t been recently alerted When a scenario’s contribution to the score increases When new information is detected in a scenario 06. Adding an Additional Score For the occasions when you need to adapt your existing scoring solution to incorporate additional scores, this video demonstrates how to add the score Relationship Trading in High-Risk Jurisdiction. 07. Configuring Your Segmentation We show how to define and apply your preferred segmentation to your scores and scorecards. In this case, we exemplify how to account for differences between large corporate and SME customers. 08. Investigating Your Alerts In the final video, we will take you through a full Trade Finance FinCrime Solution in the UI. You will be able to see alert, scorecard and scores.
We are pleased to announce the release of Quantexa 2.8.0
Our latest update is a foundational release that will enable existing and future capabilities in our Decision Intelligence Platform and the solutions we offer. 🔒Log in to see the full release announcement in our member-exclusive area. Follow Release Announcements 🔔+ to get notifications of new content.
Introducing 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.
New and Improved Search Functionality
We’re excited to announce that our new and improved Search capability has now moved to General Availability as of 2.7.7. (this is referred to as Search 2 within our technical documentation). Let’s face it – the current Search experience came out with the first versions of the Quantexa Platform and needed a facelift. Although it did the job, and quite a good one at that, we received feedback from Quantexa users that gave us ideas on how to overhaul the functionality to both keep the UX consistent and to make it a lot more intuitive. Throughout this journey, we also found synergies in the Search setup process by coupling it up with our Data Fusion tool, and making Search a lot faster to set up. By the way, now you can explore our roadmap and give feedback on our features and functionality in our Product Roadmap & Ideas Portal! Be a part of our product development! How we brought Entities front and center From user feedback, we realized that the way users interact with the Search functionality can be improved by bringing more power to search using what Quantexa already knows about entities thereby optimizing the Search query construction. To enable this the Query Building aspects of the Search UI have been redesigned to reframe the search experience around the Entities available in the Platform. This gives the user the power to tell the system what they are looking for (which Entity Type) and then the system offers the most appropriate Data Sources and Search Fields to find the Individual, Business or other Entity the user wishes to search for. Before, the search results could sometimes be not exactly what -the user expected due to incomplete or sub-optimal search configuration. This brings us to our next update – new configuration through Data Fusion! How we simplified Search configuration If you are a Quantexa user, you might be familiar with the Search configuration process which is separate from entity resolution and tuning. What we figured is, if our users are overwhelmingly looking for entities in their searches, why not streamline the search setup up by ‘inheriting’ the settings from Data Fusion, where all the magic happens with entities. To be specific, you no longer manually have to define the search fields and groups yourself as we derive them from Fusion config. So you not only save efforts in setting up a tool, but you also get far better results from reusing the configuration that’s been tuned and tested. We have also improved some existing features: We have updated how our Results Filters work once the user has executed their query. It is now possible to filter using more than one item in the Filter list in the results screen. Additionally, we have improved the Table View of results. How we made Search faster If you are using new Search with our Entity Store then you are able to perform very fast direct Entity Searches even across massive amounts of data. The Entity Store keeps a pre-built version of your resolved entities available at hand, but also updates as soon as there are any changes or new data is introduced, so you get the speed without compromising on the content. How we made de-bugging faster Lastly, it is worth noting that this new version of Search has both re-used existing components within the Platform and is much simpler to support and de-bug than the old version of Search. Together this reduces the overall support burden on Quantexa’s R&D department – you might be thinking this is more of Quantexa benefit, but actually it means we are able to handle support requests faster and free up engineers to work on other enhancements to the Platform. What has been removed? At a fundamental level the new version of Search (known as Search 2 in our technical documentation) has feature parity with the old version of Search (Search 1) plus the new features already explained. However, there are several feature removals that are worth noting: As the Fusion configuration will now drive the search field mappings, there is no need to generate Search Fields anymore, thus we removed the feature altogether. We have removed the support for field boosting as the feature was deemed not useful by our users Facets have been replaced with Aggregations and can be easily migrated from the old Search to the new Search. We have removed the type ahead on Search Fields in the Search Bar as the query-building experience has been re-designed and this feature is no longer necessary. Deprecation and Removal of the old version of Search The old version of Search (Search 1) is deprecated at 2.8.0. We expect to remove the old version of Search from the Platform at the earliest at 2.9.0. All new customers are required to adopt the new version of Search from 2.7.7. onwards. View the New and Improved Search Demo Head over to Product Demos and launch the New Search Demo (login required) for an interactive walk-through of the new features included: Product Demos - Quantexa Community Take a self-guided tour through our interactive demos to see how the Quantexa Platform solves real-world challenges and unlocks powerful insights. How do I provide Feedback? We know this is not the end of the improvements necessary to our search capabilities, so expect to make further investments in the medium term. It would be valuable to hear feedback about the new Search results screen and the query editing experience. Please provide feedback directly to the Search initiative using the Product Roadmap & Ideas Portal. Future roadmap For the next 12 months we will not be making major changes to introduce new capabilities to our Search functionality. Our focus will be ensuring that as our existing customers upgrade and adopt this new version of Search and that the experience of migrating from old Search to new Search is as simple, fast and pain-free as possible. I encourage you to get started with the new Search functionality and let us know how you find it! Read the full release notes and Search 2 Migration Guide on the Documentation site.
2.7 Quantexa Upgrade Guide
Quantexa 2.7 Upgrade Guide The 2.7 Quantexa Upgrade introduces several important updates, and this guide provides additional guidance to support the transition. The upgrade includes: Core Product Changes: Migration to Data Lake, integrated ETL validation tools, updated Graph Script utilities, and more. Data Packs Migration: Steps for transitioning to Parsers 3 and 4, with guidance to help you decide which version to use. Feature Updates: Removal of Quantexa Incubators and changes to tools like the Transaction Viewer, with guidance available for transitioning to Data Viewer. Log in to access the full guide and learn how these changes may impact your implementation: 2.7 Quantexa Upgrade Guide - Quantexa Community Quick Upgrade Overview The 2.7 Quantexa Upgrade consists of three main parts: Core Product Changes Removal of Quantexa Incubators Data Packs Migration Most of the Core Product changes are automated migrations and minor adjustments* which can be tested in a local environment. Migration to Delta Lake is going to be the…FinCrime Detection Pack 0.4 is now available
We are excited to announce version 0.4 of our FinCrime Detection Pack is now available in Early Access. This release introduces new features that will increase flexibility, improve score coverage and optimise score configuration to better capture desired behaviours, characteristics and events. This builds on the functionality released in version 0.3 of the FinCrime Detection Pack. Feature Highlights: You can now apply score logic on a targeted subset of transactions to help you uncover more specific underlying patterns that might otherwise be overlooked. You can now configure transaction score parameters based on the segment a score subject belongs to. This allows you to account for differences and ensure that you are capturing the behaviours and events you’re interested in. You are now enabled to select from a range of appropriate Event Windows to ensure that a score is targeting the desired behaviour or event. Target new risks with additional Score Types You can read more about the release in Detection Pack 0.4 Release (login required) For full details of the release, including compatible Quantexa Platform versions and minor enhancements, please see the Quantexa Documentation site. Release notes Migration guideWelcome to Parsers 4.2 | Release Announcement
Alongside the release of QP2.7, we are happy to share the release of 4.2.0 of Standard Parsers. This release extends the cleansing options you can define purely in config - configurable simple generic cleansers. We have introduced new config-based cleansers that allow you to perform replacements in strings, remove and keep parts of a string based on pre-defined options, change the case of a string for different languages and to extract specific parts of input strings, all without writing any Scala. These back-end config improvements extend to the front-end. The users of QP 2.7 will have access to the extended configurability described above within the UI - more details are available in the release notes and documentation for QP2.7. The release also brings some key bug fixes and a small improvement to business parsing that should catch more edge cases of business names with odd punctuation distributions. For more information on the release features, please see the 4.2.0 release notes and for general info on Parsers, see the documentation.Welcome to Quantexa 2.7 | 2.7.0 Release Announcement
We are pleased to announce the release of Quantexa 2.7.0. This release includes: Graph Scripting QSL (Early Access) Highlights Simplified Interface: Configuration-Based Expansions: Define Batch Graph Scripts using the Quantexa Scripting Language (QSL) with a simplified interface, reducing the need for custom Scala code. Enhanced Network Precision: Path-based Expansions: Use path-based Expansions instead of perimeter-based Expansions, resulting in tighter and more focused Networks. Efficient Data Processing: Data Pre-Filtering: Options to pre-filter data help minimize the volume of data that needs to be processed, enhancing overall efficiency. Improvements to the Entity Store Cost Efficiency and Performance: Reduced Elastic Utilization: New indexing format reduces Elastic resource usage by up to 40%, leading to significant cost savings. Smaller Index Size: Optimized Entity structure decreases index size by 60%, allowing projects to run on smaller Elasticsearch clusters. Operational Enhancements: Exclusion of Unchanged Entities: Logs can now exclude unchanged Entities, streamlining log management. Entity Version Tracking: Introduction of a version field in all Entities to track data updates, necessitating a full reload of Elasticsearch indices upon upgrade. Loading Improvements: Flexible Output Paths: Enhanced configuration accepts various path types for output locations, removing the need for file:/// prefixes. Parallel Load Execution: Specify Entity types for parallel loading, significantly reducing load times. Storage Optimization: Entities exceeding a threshold of records are stored without compounds, reducing Elastic storage requirements. REST API Enhancements: Advanced Query Capabilities: Support for the negation operator ! and wildcard queries * across all string attributes, enabling more complex and flexible searches. Pagination Support: Total hits in search responses help determine the availability of additional data, facilitating efficient data retrieval. For detailed migration steps and configuration settings, refer to the 2.6.x - 2.7.0 Migration Guide and relevant configuration references. New Global UI Configuration Service Highlights Streamlined Configuration Management: Unified Configuration Service: Global UI settings have been moved from the Explorer service to a new Global UI Configuration service, providing a centralized method for managing these settings. Dedicated REST API Endpoints: A new REST API allows programmatic access to global UI settings, enhancing integration and automation for users and Low-Code Configuration parts of the Quantexa UI. Future-Proofing and Intuitiveness: Intuitive Settings Location: This reorganization places global UI settings in a more logical location, facilitating easier management and future enhancements. Foundation for Future Improvements: While there is no immediate functional impact in the 2.7.0 release, this change lays the groundwork for future improvements in platform UI configuration. More information ➡️For more details on the release, see the 2.7.0 Release Notes on Platform Documentation. If you cannot access the Documentation site, please get in touch with your Quantexa point of contact or the Community team at community@quantexa.com. To receive updates on every release, click Subscribe in the top-right corner of the Release Announcements page (login required).
