The concept of graph databases traces back to Leonhard Euler. Euler was an 18th century Swiss mathematician who made several important discoveries in mathematics, such as infinitesimal calculus. In solving the “Seven Bridges of Königsberg” problem in 1736, Euler laid the foundations for graph theory. (He also got a fun shout-out in Hidden Figures when […]
The Fundamentals of Natural Language Processing and Natural Language Generation
Natural Language Processing (NLP) is the process of producing meaningful phrases and sentences in the form of natural language. Natural Language Processing precludes Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLU takes the data input and maps it into natural language. NLG conducts information extraction and retrieval, sentiment analysis, and more. In Natural […]
What Is Naïve Bayes Classification and How Is It Used for Enterprise Analysis?
Click to learn more about author Kartik Patel. What Is Naïve Bayes Classification? Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is a supervised classification technique used to classify future objects by assigning class labels to instances/records using conditional probability. In supervised classification, training data is already labeled with […]
The Importance of Data Science and Analytics in the Finance Industry
Click to learn more about author John Russell. It is estimated that by the end of 2020, there were approximately 40 zettabytes (40 trillion gigabytes) of data in the world. If it feels a bit difficult to wrap your head around that number, don’t worry – you are not alone! It is a truly staggering […]
Graph Databases: Updates on Their Growing Popularity
Graph databases became recognized as a database design in 2006, when Tim Bernes-Lee developed the concept of a huge database called the “linked data.” This concept became the basis of graph storage, and could display how organizations, people, and items or entities are associated, or “interconnected” with one another, and the nature of the relationships. […]
Byrider Selects PointPredictive as Machine Learning AI Partner to Prevent Fraud
According to a new press release, “PointPredictive Inc., the San Diego-based machine learning company, announced today that Byrider has selected the company’s risk scoring solutions to help them better segment high- and low-risk applications and dealers to improve profitability, expand loan availability and enhance the lending experience for both consumers and dealers. As part of […]
The Most Innovative Global Financial Services Organizations Bank On TigerGraph
According to a recent press release, “TigerGraph, the only scalable graph database for the enterprise, today announced that the world’s most innovative financial services organizations — from emerging companies to the world’s largest banks — have selected TigerGraph to strengthen their fraud detection and credit risk assessment efforts. In fact, four of the five largest […]
NICE Actimize Revolutionizes Collaborative Fraud Fighting with Decentralized AI Capabilities
A new press release reports, “NICE Actimize, a NICE business and leader in Autonomous Financial Crime Management, is introducing its Federated Learning capability that will provide financial services organizations (FSOs) with higher fraud detection rates across numerous fraud scenarios by leveraging NICE Actimize’s Collective Intelligence network. With this innovative cloud-based approach that uses machine learning […]
Handling Concept Drift in Interventional Machine Learning Systems
Click to learn more about author Peter Baudains. Fraudsters are becoming increasingly ambitious and sophisticated in their financial crime endeavors, and fraudulent activity is becoming harder to differentiate from genuine activity. Losses are being realized in a shorter period, as fraudsters adapt to beat traditional fraud management techniques and strategies. This constant innovation by fraudsters […]
Fraud Detection Using a Neural Autoencoder
Click to learn more about author Rosaria Silipo. The co-authors of this column were Kathrin Melcher and Maarit Widmann. The Fraud Detection Problem Fraud detection belongs to the more general class of problems — the anomaly detection. Anomaly is a generic, not domain-specific, concept. It refers to any exceptional or unexpected event in the data, […]