Senior Software Engineer

This Week in Neo4j: GraphAcademy takeover. Developer Experience Engineer at Neo4j July 10, 2026 Hey, Adam (from GraphAcademy) here, filling in for Alex this week. This week, in This Week in Neo4j, I'm here to let you know that Neo4j Inc has launched a brand new, refreshed version of GraphAcademy. For those of you who aren't aware, it's Neo4j's home for hands-on learning. Real Cypher, real graphs, completely free hands-on learning. You learn by building, either at your own pace or live in a workshop with other people. Over the past few months, Neo4j Inc has been busy rebuilding the site from the ground up. Neo4j Inc is bringing E.L.A.I.N.E, its AI learning assistant, to the front and center, to help you find your next course and guide you through your learning. If you live in your IDE, Neo4j Inc has also released a GraphAcademy MCP server, so you don't need to leave your editor to learn Neo4j. Neo4j Inc has also been busy writing for its new blog feature. But that's not what this email is about. This one's about what's new to do. Share your experiences and influence the future of Neo4j products: Join the Neo4j User Research panel! It's a chance to connect directly w,ith product development teams, get paid compensation, hear about what Neo4j Inc is working on and more! Happy Graphing, Adam Cowley Coming up! * Livestream: Network Intrusion Detection with Neo4j and Snowflake on July 22 * Meetup: Meet Neo4j Inc in Pune, IN on July 18 & Melbourne, AU on July 23 * All Neo4j Events: Webinars and More FEATURED COMMUNITY MEMBER: Nivedita Thapa. Nivedita built Semantic Model Inspector, an open-source tool that evaluates how ready enterprise semantic models are for LLM-powered analyst tools like Snowflake Cortex Analyst. Her work sits at the intersection of semantic modeling, knowledge graphs, and AI-powered analytics. Connect with her on LinkedIn. She has one of the first confirmed sessions at NODES 2026 "Are Your Semantic Models AI-Ready? What Knowledge Graphs Teach Us About Building Context for LLMs", where she will take a public semantic model, encode the same domain in Neo4j as a typed knowledge graph, and expose that graph context to an LLM before SQL generation. You will see the graph schema, the Cypher patterns that capture what warehouse-native semantic layers miss, and side-by-side code examples of LLM query generation with and without graph-backed context. FOCUSSED LEARNING: Try a lab now. Some things don't need a full course. You just need to learn one thing, fast. That's what Labs are designed for - short, single-module, hands-on lessons that get you in and out with one specific skill. One subject. No setup. Less than an hour. A few to start with: * Working with Dates and Durations in Cypher - learn how temporal types, including dates and times, can be used in Neo4j to filter, compare, and calculate with precision. * Graph type Schema Enforcement - define a consistent schema that acts as your ontology at the database level. Structure without giving up flexibility. * Full-Text Search in Neo4j - Create and query full-text indexes for case-insensitive search. Try one now. You'll be skilled and ready in time for your morning standup. Not sure what to learn next? There's more than one way to find out. Courses are now organized by: * Topic - GraphRAG, Cypher, MCP, and more. * Persona - developer, data scientist, DevOps, context engineer, graph data scientist. * Learning path - a sequence of courses aimed at a concrete goal. One example of a learning path is Neo4j certification: a defined route that ends with something real, a certified Neo4j developer credential. Got something specific in mind? Tell the onboarding assistant what you're trying to build, right from the homepage search box, and it'll point you to the courses or path that get you there. Choose your own adventure. Self-paced learning gets you far, but nothing beats building something live, with other people, in the room (or on the call) with you. And as more of its workshops get built around MCP, that hands-on format matters even more: you're learning how to work with Neo4j inside the tools you already use. Case in point: the Agentic GraphRAG Mini Hack in Bengaluru, run by Zaid Zaim. Thirty minutes of teaching, two hours of MCP-assisted coding, seventeen projects built, four winners. Would you like to deliver one of these yourself? If you know Neo4j and want to teach it - in your workplace, at a meetup, wherever - Neo4j Inc want to help you do it. Deliver through the GraphAcademy platform, and you get instructor notes, a built-in slide view, and help promote the event. Neo4j Inc'll even run a train-the-trainer session with you first, so you walk in confident. Show up, log in, present. That's the takeover. New site, new Labs, clearer paths to your next course, and more workshops than ever. Neo4j Inc put a lot of time and thought into this platform, from the design down to how each course teaches you something. So go take a look, and tell Neo4j Inc what you think. What's working, what's not, what would make it better. Neo4j Inc is genuinely excited to hear it. And if you're ready to teach Neo4j yourself, get in touch. Neo4j Inc'll help you get there. See you at a workshop soon! EcocomityChain.AI built a material genealogy graph in Neo4j that traces a modeled vehicle from the finished product down to raw ore. Eight levels deep, 34,713 nodes, 554 suppliers across every tier. The payoff is the kind of question a flat supplier list can't answer. Their graph spots when the same nickel ore sits under 19 separate assembly chains, quietly turning one mine into a single point of failure for an entire vehicle program. It also surfaces where your real recovery time hides, often six levels below your Tier-1 supplier. They wrote it up as three worked scenarios against a live graph, so it reads like a build log you can actually follow. If you're wrestling with deep, connected data of your own, it's a sharp example of what graph thinking makes visible. Continuous learning. * GraphAcademy: Join the "Cup", complete Courses and win prizes * Learn on Your Schedule: Go deeper into graph intelligence on Neo4j's On-Demand webinar library * Workshops: Join its virtual classrooms workshops from Fundamentals to GenAI * New Webinar: Enterprise AI is missing a key ingredient: The knowledge layer - Americas, Europe, Middle East & Africa, Asia Pacific

Unraveling the power of knowledge graphs in the fight against financial fraud. June 30, 2026 As the financial services industry accelerates its digital transformation, one technology is rapidly emerging as a game-changer: knowledge graphs. By connecting vast amounts of enterprise data into intelligent relationship-based networks, knowledge graphs are helping banks, NBFCs, fintech companies, and enterprises combat fraud, strengthen compliance, and build more reliable AI systems. At the 2nd World Fintech Summit 2026, Pawan Mall, Senior Solution Engineer at Neo4j, shared valuable insights into how graph databases and knowledge graphs are revolutionizing fraud detection, anti-money laundering (AML), customer intelligence, and enterprise AI across the BFSI ecosystem. The rise of connected data. Traditional relational databases have long served as the backbone of enterprise applications. However, as financial institutions manage increasingly complex customer relationships, transaction networks, and digital identities, conventional databases often struggle to reveal meaningful connections hidden across multiple tables. Knowledge graphs solve this challenge by organizing data around relationships rather than isolated records. Instead of treating customers, accounts, devices, transactions, and identities as separate entities, graph databases connect them into an interconnected network that mirrors real-world relationships. This connected approach enables organizations to uncover patterns and insights that would otherwise remain hidden, providing a much deeper understanding of business operations and customer behavior. Why knowledge graphs matter in BFSI. Financial institutions generate enormous volumes of interconnected data every day. Every payment, loan application, account opening, credit assessment, or digital interaction creates relationships that can reveal valuable intelligence. According to Pawan Mall, graph databases such as Neo4j enable banks and financial institutions to transform fragmented datasets into unified relationship-driven models. This significantly enhances their ability to detect fraud, assess risk, and improve decision-making. Unlike traditional databases that require multiple complex joins and lengthy queries, graph databases can traverse millions of relationships in real time, making them particularly effective for identifying suspicious activity and complex financial crime networks. Strengthening fraud detection and AML. One of the most impactful applications of knowledge graphs is in fraud detection and anti-money laundering (AML). Modern financial crime has evolved beyond isolated fraudulent transactions. Criminal networks now operate through interconnected entities involving mule accounts, fake identities, shell companies, compromised devices, and layered transaction chains. Graph technology allows investigators to visualize and analyze these intricate relationships with remarkable speed. By connecting customers, accounts, devices, locations, transaction histories, and behavioral patterns, financial institutions can quickly identify: * Fraud rings operating across multiple accounts * Money laundering networks * Mule account ecosystems * Identity fraud * Suspicious transaction chains * Hidden relationships between seemingly unrelated entities Instead of investigating transactions individually, investigators gain a comprehensive view of the entire fraud ecosystem, enabling faster detection and more accurate risk assessment. Enabling Customer 360 and relationship intelligence. Beyond fraud prevention, knowledge graphs play a significant role in improving customer intelligence. Banks frequently maintain customer information across multiple disconnected systems, making it difficult to develop a unified understanding of customer relationships. Graph databases create a comprehensive Customer 360 view by linking data from various touchpoints, including: * Customer profiles * Loan portfolios * Investment accounts * Insurance products * Digital interactions * Device information * Relationship hierarchies This holistic view allows organizations to deliver more personalized services, improve customer experience, strengthen cross-selling opportunities, and make better lending decisions. Powering the next generation of enterprise AI. Artificial Intelligence is transforming financial services, but its effectiveness depends entirely on the quality and context of enterprise data. Knowledge graphs provide what many experts describe as the "enterprise brain" - a semantic layer that gives AI systems contextual understanding rather than simply storing isolated information. Pawan Mall explained how knowledge graphs serve as a persistent memory layer for AI, enabling intelligent systems to understand relationships between customers, products, regulations, business processes, and organizational knowledge. This foundation supports a wide range of enterprise AI applications, including: * Intelligent virtual assistants * AI-powered customer support * Agentic AI systems * Enterprise search * Context-aware recommendations * Automated decision-making * Knowledge discovery As AI systems continue learning from connected enterprise data, they become increasingly capable of delivering relevant, explainable, and trustworthy insights. Reducing AI Hallucinations with GraphRAG. One of the biggest concerns surrounding Generative AI is hallucination - the generation of incorrect or misleading information that appears credible. To address this challenge, organizations are increasingly adopting GraphRAG (Graph Retrieval-Augmented Generation) architectures. GraphRAG combines Large Language Models (LLMs) with structured knowledge graphs, allowing AI systems to retrieve verified enterprise information before generating responses. Rather than relying solely on statistical language patterns, AI can ground its answers in connected, validated organizational knowledge. This approach offers several advantages: * Higher response accuracy * Improved contextual understanding * Better explainability * Reduced misinformation * Greater trust in enterprise AI applications For regulated industries such as banking and financial services, these improvements are particularly critical, where inaccurate AI outputs can have significant compliance and financial implications. The growing importance of graph intelligence. Knowledge graphs are rapidly becoming a strategic asset for organizations embracing digital transformation. Global enterprises - including many Fortune 100 companies - already leverage graph databases to solve complex business challenges involving fraud detection, cybersecurity, supply chain optimization, recommendation engines, and enterprise knowledge management. For the BFSI sector, graph intelligence is proving invaluable in: * Financial crime detection * Risk intelligence * Compliance monitoring * Customer relationship management * AI-powered analytics * Data governance * Intelligent automation As fraud techniques become increasingly sophisticated and AI adoption accelerates, the ability to connect and interpret enterprise data will become a defining competitive advantage. Looking ahead. Knowledge graphs represent far more than another database technology. They provide the foundation for a new generation of intelligent financial services built on connected data, contextual reasoning, and explainable AI. By bringing together graph databases, enterprise knowledge layers, Agentic AI, and GraphRAG architectures, organizations can significantly improve fraud detection, strengthen compliance, enhance customer understanding, and build AI systems that are both more accurate and more trustworthy. The session by Pawan Mall at the 2nd World Fintech Summit 2026 offered a compelling vision of how connected data is reshaping the future of BFSI. As financial institutions continue their digital transformation journeys, knowledge graphs are poised to become a critical enabler of secure, intelligent, and data-driven innovation. Organizations that invest in graph intelligence today will be better equipped to uncover hidden risks, make faster decisions, and harness the full potential of enterprise AI in the years ahead. "Exciting news! Elets technomedia is now on WhatsApp Channels Subscribe today by clicking the link and stay updated with the latest insights!" Click here! 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Neo4j buys company to challenge Palantir, wants to see less lock-in. In News by MKSE.com Editorial Martin EdenströmJune 26, 2026 Graph database company Neo4j has completed the acquisition of GraphAware, a software company with a strict focus on intelligence analysis for various agencies. The acquisition will strengthen Neo4j's offering in AI-driven analysis of connected data. The investment is seen as a direct response to Palantir's progress. The investment will also be a step towards locking in different models and for "open standards" in the future, says Neo4j's CPO Sudhir Hasbe... This is only a preview of the entire article. To read and access all the data, a one-time payment is required or you or your company becomes a supporting member of MKSE.com. Payment is made at the end of the month and you can read all the content several days before being charged. The amount is up to you, starting at 49 SEK per month. Feel free to give the amount you think the tens of thousands of articles on MKSE.com are worth (190 SEK, 1900 SEK). The money goes to cloud operations and licenses so that MKSE.com stays alive. One-click payment on your mobile via e.g. ApplePay. Via Paypal or debit card. Many thanks for your understanding and support! Are you already a member, but logged out? Tough cookie times now. Make sure you are logged in to your Patreon account and click the Log In link next to it. Or register directly on MKSE.com via the Unlock link. To view this content, you must be a member of