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What’s New
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.
🎥 Webinar: Why Entity Resolution & Graph Based Analytics is Crucial for Modern Fraud Detection
4:00 PM BST | 5:00PM CEST | 11:00AM EDT Save your seat Join Clark Frogley, Global Head of Fraud Solutions, and Shyam Bhatt, Head of Fraud Solutions at Quantexa for a webinar exploring why Entity Resolution and Graph-Based Analytics is crucial for modern fraud detection, and how to apply these tools. Insurance fraud doesn’t happen in isolation — and detecting it shouldn’t either. Modern insurance fraud demands modern detection. Insurers now need to evolve their fraud strategies and move beyond claim-by-claim analysis to uncover wider schemes that span across claims, policies, and entities, through entity resolution and networks. Join Quantexa's fraud experts for a live demo and in-depth conversation on how advanced entity resolution and network analytics are powering a new era of contextual decision intelligence. During the session, you'll explore how cutting-edge software is enabling insurers to uncover hidden fraud rings, spot anomalies earlier, and scale their defences — all in real-time and without extensive manual investigation. By attending this webinar, we will explore: Learn how moving beyond traditional, siloed claim-by-claim analysis to entity-centric, graph-based approaches can uncover complex fraud schemes that span multiple claims, policies, and actors. Experience a hands-on demonstration showing how cutting-edge software uses contextual decision intelligence to detect hidden fraud rings, spot anomalies earlier, and reduce reliance on manual investigation. Gain insights into the advanced technology stack enabling scalable, real-time fraud detection — including practical applications for fraud analysts, data scientists, and technology leaders.  Save your seat Community resources to explore 👋Join the Insurance Fraud User Group: 📖 From the Community Library: Investigating Network Fraud: 4 Key Areas for Investigators Handling Large Networks for Investigations & Tasks The changing shape of fraud in the UK market - ABI Fraud Statistics Lessons Learned from a Streaming Lending Fraud Project Optimizing Entity Resolution and Graph Expansion🎥 Webinar: Why Entity Resolution & Graph Based Analytics is Crucial for Modern Fraud Detection
4:00 PM BST | 5:00PM CEST | 11:00AM EDT Join Clark Frogley, Global Head of Fraud Solutions, and Shyam Bhatt, Head of Fraud Solutions at Quantexa for a webinar exploring why Entity Resolution and Graph-Based Analytics is crucial for modern fraud detection, and how to apply these tools. Insurance fraud doesn’t happen in isolation — and detecting it shouldn’t either. Modern insurance fraud demands modern detection. Insurers now need to evolve their fraud strategies and move beyond claim-by-claim analysis to uncover wider schemes that span across claims, policies, and entities, through entity resolution and networks. Join Quantexa's fraud experts for a live demo and in-depth conversation on how advanced entity resolution and network analytics are powering a new era of contextual decision intelligence. During the session, you'll explore how cutting-edge software is enabling insurers to uncover hidden fraud rings, spot anomalies earlier, and scale their defences — all in real-time and without extensive manual investigation. By attending this webinar, we will explore: Learn how moving beyond traditional, siloed claim-by-claim analysis to entity-centric, graph-based approaches can uncover complex fraud schemes that span multiple claims, policies, and actors. Experience a hands-on demonstration showing how cutting-edge software uses contextual decision intelligence to detect hidden fraud rings, spot anomalies earlier, and reduce reliance on manual investigation. Gain insights into the advanced technology stack enabling scalable, real-time fraud detection — including practical applications for fraud analysts, data scientists, and technology leaders.  Save your seat Community resources to explore 👋Join the Insurance Fraud User Group: 📖 From the Community Library: Investigating Network Fraud: 4 Key Areas for Investigators Handling Large Networks for Investigations & Tasks The changing shape of fraud in the UK market - ABI Fraud Statistics Lessons Learned from a Streaming Lending Fraud Project Optimizing Entity Resolution and Graph Expansion
