Neo4j Addresses Key Challenges in Finance

In today’s regulatory environment, financial services firms are beginning to experience the impact of graph databases across a number of functions ranging from fighting financial crimes, preventing and responding to cyber threats and ensuring compliance.

Meanwhile, as the continuous digitization of processes requires financial services firms to evolve their customer engagement strategies to meet rising customer expectations, graph databases are helping financial services firms gain competitive advantage from digitization to drive new sales, reduce costs and build closer relationships with customers.

This white paper, “Addressing Key Challenges in Financial Services with Neo4j,” illustrates how financial services organizations are using graph databases, specifically Neo4j, to effectively solve these problems.

Download the White Paper

Fast Track

  • Financial Fraud Detection with Graph Data Science

    Learn how graph data science enhances your analytics and machine learning pipelines to detect fraud rings, reduce fraudulent transactions and safeguard revenue streams.

    Read the white paper
  • Neo4j powers the Insurance Industry with High Performance at Die Bayerische

    The German insurer uses Neo4j to create a high-performance sales system with 24/7 instant access to contract information.

    Read the case study
  • How Lending Club uses Neo4j to manage over 130 microservices

    Lending Club relies on Neo4j to in its mission to shake up the financial services industry.

    Read the blog post
  • Fraud Detection: Discovering Connections with Graph Databases

    Learn how powerfully and effectively graph databases uncover first-party bank fraud, insurance fraud, ecommerce fraud and other fraud scenarios.

    Read the white paper

Neo4j Financial Services Customers

“The transition to Neo4j technology has at least doubled the level of service for identification of the actual owner of businesses, from an average calculation time of 12 seconds to 67 milliseconds (-99 %) in cases that require tracking of up to 15 ownership links. This allowed us to extend its use and improve the precision of the algorithm at the same time.”

—Stefano Gatti, Innovation & Data Sources Manager, Cerved

Financial Services Case Studies

  • Play Video
    Real-Time Data Lineage at UBS — Wren Chan and Sidharth Goyal, UBS
  • Play Video
    Microservice and Module Management with Neo4j — John Lavin, Vanguard
  • Play Video
    Preventing Fraud with Neo4j - 5 min Exec Overview