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NumPy VS Riskified

Compare NumPy VS Riskified and see what are their differences

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Omni-Channel SaaS Solution Suite for Retail, Wholesale & eCommerce businesses featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Riskified logo Riskified

eCommerce fraud prevention solution and chargeback protection guarantee for online merchants. Find out how we can help your company boost revenue from online sales using our machine-learning powered eCommerce fraud protection software.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Riskified Landing page
    Landing page //
    2023-10-20

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Release Date
-
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Riskified

Website
riskified.com
Pricing URL
Official Riskified Pricing
$ Details
-
Release Date
2012 January
Startup details
Country
United States
State
New York
City
New York
Founder(s)
Assaf Feldman
Employees
500 - 999
Edit details

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Riskified features and specs

  • Chargeback Guarantee
    Riskified offers a chargeback guarantee on approved transactions, meaning if a fraudulent transaction is approved by their system, Riskified will cover the cost of the chargeback, providing a financial safety net for merchants.
  • Increased Approval Rates
    Merchants often see increased approval rates because Riskified's advanced algorithms and machine learning models are tailored to accurately identify genuine customers, allowing more legitimate transactions to be approved.
  • Global Solution
    Riskified supports a wide range of payment methods and currencies, making it suitable for merchants with a global presence and varying customer demographics.
  • Seamless Integration
    The platform offers seamless integration with major e-commerce platforms and payment gateways, reducing the time and effort required for merchants to set up and begin protecting transactions.
  • Advanced Analytics
    Riskified provides merchants with detailed analytics and reporting tools, helping them understand transaction patterns, assess risk, and optimize their operations.

Possible disadvantages of Riskified

  • Cost
    The service can be relatively expensive for smaller businesses, especially those with thin margins, as the pricing model typically involves a fee per transaction or a percentage of the transaction value.
  • Complexity
    For businesses without a dedicated team for fraud prevention, understanding and leveraging all the features and data that Riskified provides can be complex and time-consuming.
  • Dependence on External Provider
    Relying on Riskified for fraud prevention places a critical aspect of the business's operations in the hands of an external provider. Any downtime or service issues with Riskified could directly impact transaction processing.
  • False Positives
    While Riskified aims to minimize false positives, there is always a risk that legitimate transactions may be wrongly declined, which can lead to customer dissatisfaction and potential loss of sales.
  • Customization Limits
    Some merchants may find that the level of customization available in Riskified's fraud prevention algorithms and workflows does not fully meet their unique business needs or preferences.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of Riskified

Overall verdict

  • Riskified is generally considered a good solution for businesses looking to enhance their fraud detection and prevention capabilities. It is especially recommended for online retailers who wish to strike a balance between reducing fraud and maintaining a seamless customer experience.

Why this product is good

  • Riskified is a well-regarded eCommerce fraud prevention platform that leverages machine learning and big data to identify fraudulent transactions and boost conversion rates. It offers comprehensive solutions for chargeback protection, payment optimization, and account security. Many businesses appreciate its ease of integration, detailed analytics, and the ability to increase approval rates while minimizing fraud-related losses.

Recommended for

  • E-commerce companies
  • Online marketplaces
  • Retail businesses with significant online presence
  • Merchants dealing with high volumes of transactions
  • Businesses seeking advanced analytics for fraud insights

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Riskified videos

+ Add

Riskified Educational Webinar: Automating The Fraud Review Process (Summer Boot Camp - 2nd Webinar)

More videos:

  • Review - Riskified Educational Webinar: Optimal Manual Review (Summer Boot Camp - 3rd Webinar)
  • Review - Riskified : Nanoleaf case study

Category Popularity

0-100% (relative to NumPy and Riskified)

NumPy

Riskified

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
eCommerce
0 0%
eCommerce
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Fraud Prevention
0 0%
Fraud Prevention
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Riskified

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Riskified Reviews

We have no reviews of Riskified yet.
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Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 122 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (122)

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Riskified mentions (0)

We have not tracked any mentions of Riskified yet. Tracking of Riskified recommendations started around Mar 2021.

What are some alternatives?

When comparing NumPy and Riskified, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Signifyd - Signifyd is a SaaS-based, enterprise-grade fraud technology solution for e-commerce stores.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Kount - eCommerce fraud detection & prevention

OpenCV - OpenCV is the world's biggest computer vision library

Sift - Digital Trust & Safety enables your business to grow, innovate, introduce new products, features, and business models โ€“ without increased risk.

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