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Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
Proprietary software for viewing and editing PDF documents. pdftk: GNU GPL/Proprietary: command-line tools to manipulate, edit and convert documents; supports filling of PDF forms with FDF/XFDF data. PDF-XChange Viewer: Freeware: Freeware PDF reader, tagger, editor (simple editions) and converter (free for non-commercial uses).
CNET Download (originally Download.com) is an Internet download directory website launched in 1996 as a part of CNET. Initially it resided on the domain download.com, and then download.com.com for a while, and is now download.cnet.com. The domain download.com attracted at least 113 million visitors annually by 2008 according to a Compete.com ...
PDF24 Creator is an application software by Geek Software GmbH for the creation of PDF files from any application and for converting files to the PDF format. The application is released under a proprietary freeware license.
Soda PDF is an application used to view, create, and edit Portable Document Format (PDF) files. [3] The software was developed by LULU Software in 2010, based in Montreal, Quebec , Canada . Features
It is widely used in the financial sector, especially by accounting firms, to help detect fraud. In 2022, PricewaterhouseCoopers reported that fraud has impacted 46% of all businesses in the world. [1] The shift from working in person to working from home has brought increased access to data.
Can append output to an existing PDF file. Supports strong password-based PDF security. Allows PDF metadata—including author, title, subject, and keywords—to be set. Create files for PDF version 1.2, 1.3, 1.4, or 1.5; The software uses OpenCandy (which includes spyware) to deliver advertisements.
ML.NET is a free software machine learning library for the C# and F# programming languages. [4] [5] [6] It also supports Python models when used together with NimbusML.The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. [7]