4.1.19
TigerGraph, the only scalable graph database for the enterprise, today introduced its latest release, TigerGraph 2.4. The new technology combines graph pattern matching with real-time deep link analytics — a unique mix ideal for fraud and money laundering detection, security analytics, personalized recommendation engines, artificial intelligence and machine learning. The new release makes it easier than ever for enterprises to use deep computational analytics to gain insights from data.
Pattern matching has been around for a long time, but business insights from the technique have been constrained by two problems: difficulty in scaling the computational requirements for large datasets and an inability to do deep link analytics, which requires going more than three hops or levels deep into the dataset.
For example, determining ultimate beneficiary ownership in banking and financial services means traversing from each subsidiary to its parent business unit all the way up to the corporate headquarters, looking up the key stakeholders for each organization and adding up the ownership portions for each stakeholder across the corporate structure. With every hop, the size of data in the search expands exponentially, requiring massively parallel computation to traverse the data.
Each new hop opens up a new world of information, but competing graph databases have only been able to scratch the surface because of their inability to handle these increasingly complex computations. AI and ML developers, for example, have long sought deeper analysis of interconnected data. The deeper the insights, the better the patterns and corresponding features, which leads to more accurate outcomes for business initiatives.
“Unlike other graph databases on the market that delve two to three level deep into the connected data, TigerGraph’s pattern analytics is tuned to be efficient and tractable with the ability to go 10 or more levels deep into the interconnected entities and calculate risk or similarity scores based on multi-dimensional criteria in real-time,”
said Dr. Yu Xu, CEO and founder, TigerGraph. “Efficient graph analytics is more than just a great massively parallel processing engine; it’s understanding what users want to know and focusing on that, and pruning away the rest. The communicative and intuitive power of graphs is the ability to understand a complex set of relationships as one holistic pattern: a path, a set of branches, a loop. TigerGraph’s pattern matching enhancement to TigerGraph’s GSQL query language makes it easier to do that.”
Standard pattern matching solutions have a defined starting point such as a specific customer account or payment and a well-defined pattern with a fixed number of hops such as traversal from a customer account to all the payments originating from the account to recipients of those payments etc. Discovering fraud or money laundering loops is complex, as it does not have a defined starting point as the payment may originate from any customer account and it also does not have defined number of hops as fraudsters or money launderers often use 10+ layers of synthetic accounts to hide their activities.
With its massively parallel processing (MPP) engine, TigerGraph 2.4 addresses both the standard as well as complex pattern matching for datasets of all sizes.
TigerGraph’s GSQL pattern-matching support lets users express multi-hop queries in a compact, easy-to-read format. By expressing the multi-hop patterns in one line, the transparency of patterns used in analytics and feature engineering for machine learning is improved.
Furthermore, TigerGraph’s Massively Parallel Processing (MPP) graph engine guarantees scalable and efficient performance on any size graph analytics by combining the newly added pattern matching query syntax with the unique GSQL feature called accumulators [2]. Accumulators allow data scientists and developers to define multi-dimensional criteria for computing a score or ranking to express how well the two patterns match.
Examples of how the accumulators work with the new pattern matching queries in GSQL include:
* NEXT GENERATION RECOMMENDATION ENGINE – Traditional recommendation engines look at products purchased by a customer, find other “similar” customers who have purchased these items and consider other products or items purchased by these customers as recommendations for the customer in question. Accumulators can be used to define more comprehensive and discerning criteria for selecting “similar”
customers, based on the demographics of the customers, the recency of the shared or common purchased items with the customer in question, the total spend in the shared items category, and a similarity or likeness score based on the characteristics of the purchased items such as “light up”, “colorful” or “superhero Batman”. All of these factors are combined with the customer’s immediate purchase intent based on the recent browsing and search history to compute the likelihood or suitability score for each recommendation.
* FRAUD AND MONEY LAUNDERING DETECTION – Fraud detection looks for transactional patterns similar to those of known cases of fraud or money laundering. Accumulators combined with the pattern matching in TigerGraph allows data scientists to define multi-dimensional criteria for fraud or money laundering detection. As new payments come in every second, accumulators recalculate a new fraud or money laundering risk score for each payment as well as for each account sending or receiving the payment based on the multi-dimensional scoring criteria such as size, frequency and percentage of payments with other accounts suspected of being involved in fraud or money laundering. This is combined with pattern matching as many as 10 levels deep in the payment and customer account graph to flag potentially fraudulent transactions and accounts that have crossed the threshold of acceptable risk and need to be investigated by the fraud or anti-money laundering analysts.
* POWERING AI AND MACHINE LEARNING WITH REAL-TIME DEEP LINK PATTERN ANALYTICS – Explainable AI demands traceability of every decision – whether it’s a recommendation of a particular product or service to a customer or flagging an account for being involved in fraud or money laundering. The accumulators “show the math” involved in arriving at every decision, thereby allowing companies and government to roll out the explainable AI solution to the customer-facing employees as well as to the end consumers. Graph-based features are computed for each pattern and these are fed into the machine learning solution to improve the accuracy for multiple use cases including recommendation engine, fraud and money laundering detection, customer 360 and cyber security.
In addition, TigerGraph announced that AWS users can use their S3 data natively in GraphStudio [3], significantly improving the efficiency of the AWS cloud business user. GraphStudio has been praised for how easy it is to map data stored in local files into the graph schema, using a drag-and-drop GUI. The same ease-of-use is now available for AWS users who have data in S3 files. Native S3 Import from GraphStudio offers better synergy with popular cloud data store and easy to use data import, making it simpler to run TigerGraph on AWS.
