July 2023 Summaries

In the era of customer-centric businesses, providing an exceptional omnichannel experience is crucial to success. As online retail sales have increased, connecting data across various silos has become essential for a true omnichannel approach. TigerGraph, a powerful graph database platform, helps large customer-facing businesses create a connected customer platform by effectively leveraging data and improving customer interactions. Graph databases offer advantages such as flexibility and entity resolution, enabling connections between data from different sources. Successful utilization of TigerGraph has been demonstrated by various enterprises, leading to increased customer engagement and improved advertising performance.

TigerGraph is a graph database platform designed to store and manage large-scale datasets, enabling fast query processing and unlocking the full potential of connected data for various applications. It has been recognized by leading industry analysts such as Gartner and offers unmatched analytics intelligence. Key features include high performance, strong scalability, continuous enhancement on Graph AI & ML, and an ambitious vision for customers. Companies like Intuit, Xandr, and Amgen have successfully utilized TigerGraph to improve their operations and gain deeper insights into their data.

TigerGraph's Graph Rules Engine is a revolutionary approach to rule-based operations that addresses the limitations of traditional rules engines, such as complexity, slow adaptability, and high maintenance costs. By leveraging graph databases, businesses can efficiently represent their products, customers, and operations, leading to enhanced manageability, rapid adaptability, and cost-effectiveness. Two Forbes top 20 enterprises have already benefited from this innovative solution, achieving significant savings and improved operational efficiency.

A leading investment bank has adopted TigerGraph, a graph database and analytics platform, to improve its real-time fraud detection capabilities significantly. The financial institution's credit card business needed a scalable real-time fraud detection engine with response times of under 0.5 seconds. By leveraging TigerGraph's unique approach and customizing its algorithms, the bank was able to detect patterns indicative of suspicious activity in real-time, effectively categorizing credit card applications. The implementation of TigerGraph as a distributed cluster on the institution's premises ensured data privacy and control while providing high availability and performance. This innovative solution has helped the financial institution protect its customers from potential fraud and maintain trust in the digital age.

UAWelcome is an innovative project that uses graph technology to revolutionize refugee support in the US. Founded by compassionate individuals, UAWelcome addresses the challenges of coordinating resources for refugees with a graph-based platform that connects them with volunteers offering specific services. The platform leverages TigerGraph's database and algorithms to match refugees with suitable hosts or volunteers efficiently. Overcoming various challenges, UAWelcome has empowered over a thousand individuals to provide assistance to those in need. Its success demonstrates the potential of graphs for good and serves as a blueprint for addressing other humanitarian issues using technology responsibly and ethically.

Graph AI is an emerging technology that has significant potential in various industries, including marketing. By 2025, Gartner predicts that 80% of data and analytics innovations will involve graph technologies. Many Fortune 500 companies have already adopted graph-based solutions, leveraging their ability to facilitate rapid decision-making and unlock valuable insights. Graph AI has driven profitability across various sectors such as fraud detection, network optimization, pharmaceutical drug discovery, risk monitoring, recommendation engines, cybersecurity, and more. In marketing, graph databases offer several advantages, including relationship mapping, personalization and targeting, customer journey analysis, influencer marketing and advocacy, customer segmentation, campaign optimization, and customer retention and cross-selling. By leveraging the power of interconnected data, graph databases provide a comprehensive view of customers, enable personalization, optimize campaigns, and drive customer-centric marketing strategies. As this technology continues to evolve, it will shape the future of marketing and unlock new possibilities for businesses across various industries.

Bank fraud is a significant issue affecting financial institutions worldwide, with organized criminal groups being the primary perpetrators. Fraud differs from anti-money laundering (AML) as it involves criminals misrepresenting their identity to steal money, while AML focuses on monitoring and reporting money movement to prevent illicit funds. Bank fraud can be categorized into two main types: loan fraud and credit card fraud. Detecting fraud is crucial during the application process and at the time of the fraud itself. Fraud detection teams focus on identifying suspicious accounts and activity through automated means, often utilizing machine learning algorithms. TigerGraph's advanced methods for consolidating information, assessing account connections, and monitoring behavioral changes contribute to effective bank fraud detection and prevention.

Anti-Money Laundering (AML) practices are crucial in the financial sector for preventing illicit activities. Graph databases play a significant role in implementing these practices by analyzing relationships between commercial and consumer accounts, as well as their associated transactions. By using graph databases, financial institutions can identify suspicious activities indicative of money laundering more effectively. The steps involved in combating money laundering include entity resolution, generating alerts based on violated rules, calculating risk scores for account groups, and creating alert entities for further investigation by fraud analysts. Graph databases offer advantages such as easier entity resolution, avoidance of ad hoc joins, scalability, performance, advanced aggregation support, and mutability, making them a valuable tool in the fight against money laundering.