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WIP - 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]WIP - Batch & Dynamic Scoring
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' 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? Please specify the most suitable section of the Community Library for publishing. 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 HemingwayScoring Concepts: Scenarios & Risk Factors
This guide explains how to differentiate between Scenarios and Risk Factors. At Quantexa, we aim to give our clients meaningful Alerts. We focus on those that pose the highest risk to investigators. This article targets Business Analysts, Business End Users, and Subject Matter Experts (SMEs). It focuses on individuals engaged in risk assessment, decision-making, or developing Scores. When readers grasp these distinctions, they can design Scores and Scorecards better. This helps us show only the most relevant risk profiles to investigators. Read the full article for: Definition of Scenarios and Risk Factors Best Practices for Setting Scenarios vs Risk Factors How Scenarios and Risk Factors affect Alerting and Re-Alerting Using Detection Packs 4. Scoring Concepts: Scenarios & Risk Factors - Quantexa Community This article explains how to differentiate between Scenarios and Risk Factors. At Quantexa, we aim to give our clients meaningful Alerts. We focus on those that pose the highest risk to investigators. This article targets Business Analysts, Business End Users, and Subject Matter Experts (SMEs). It focuses on individuals…
📖 New Article: Decision Making Beyond Local Graph Context
When investigating an Entity in the Quantexa Graph, how do you ensure you're not missing critical context? Expanding through the Graph can reveal important connections, but going too far can lead to information overload. This new article by explores how Multi-hop Graph Analysis helps investigators uncover hidden relationships by analyzing the Graph in its entirety—beyond just proximity. Using Quantexa’s Knowledge Graph framework, Matt explores an example of how this approach identified 65 new Entities linked to financial crime that weren’t flagged in public investigations. 🔎 Learn how Multi-hop Graph Analysis enhances investigations, from AML to risk management and beyond. 📖 Read the full article: Decision making beyond local Graph context - Quantexa Community Background When investigating an Entity in the Quantexa Graph, it is common for an investigator to start from the Entity in question, and expand through the Graph along related documents and Entities to find relevant linked information. This is standard practice in many contexts, for example in various risk applications,… You can also watch the webinar recording to hear from Matt directly: How Knowledge Graphs Help Identify Bad Financial Actors and Commercial Opportunities at Scale In this session, you’ll learn how multi hop graph techniques explore proximity and paths to illuminate relationships and communities of interest.
Data Streaming Design Principles to Enrich Information in Kafka Input Messages
This article provides practical solution designs to enrich information in incoming messages and align them with Quantexa's Streaming Tier (Document Model) definitions, leveraging lookup principles to ensure seamless integration and efficient operation. The Quantexa Streaming Tier is a critical component for enabling near-real-time data ingestion and processing. Ensuring that input messages adhere to the required schema and include necessary data is essential for maintaining system performance, reliability, and data integrity. Log in to read the full guide on Data Streaming Design Principles to Enrich Information in Kafka Input Messages, including: Conforming Data Using a Mapper Application Data streaming lookup design architecture Lookup considerationsDesigning a Kafka Solution to Meet Functional and Non-Functional Requirements Beyond Quantexa
Designing a Quantexa streaming solution that addresses both functional and non-functional requirements can be challenging, especially when specific use cases require capabilities beyond the OOB functionalities of Quantexa platform. While some extensions may be necessary to meet these requirements, it is important to avoid unnecessary customizations and follow Quantexa’s best practices to ensure the solution remains: Future-proof: Ready for platform upgrades and changes. Aligned with industry standards: Compliant with recognized guidelines and practices. Optimally performing: Delivering high efficiency and responsiveness. Scalable: Easily adaptable to growing data and user demands. Seamlessly integrated: Enhancing operational efficiency within the Quantexa ecosystem. This article explores how to design Kafka streaming solutions that address unique requirements while staying aligned with Quantexa's best practices, minimizing customizations, and leveraging the platform’s capabilities effectively. Read the full article (login required): Designing a Kafka Solution to Meet Functional and Non-Functional Requirements Beyond QuantexaExplore the Latest Articles in the Community Library 📚
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