ComplyCube offers one of the most advanced and complete platforms in the identity verification and KYC space, helping small, large, and prominent organizations effortlessly meet their AML obligations worldwide.
ComplyCube's mission is to grow trust in the global digital economy by empowering businesses of all sizes to implement slick and resilient verification journeys that increase customer conversions, prevent fraud, and reduce onboarding costs – all without adding unnecessary friction to genuine users.
The all-in-one KYC verification platform is built upon cutting-edge AI, trusted sources, and expert human reviewers, allowing us to offer an extensive and coherent array of checks, including AML & PEP Screening, Document Authentication, Biometric Verification, Multi-bureau Checks, Address Verification, and much more.
Why ComplyCube?
❇️ Trusted by startups and big names alike, including AXA, Lycamobile, and Citi.
❇️ 98% Client onboarding rate, helping you convert more customers and grow your business.
❇️ One-stop solution for everything you need to meet your AML and KYC compliance obligations.
❇️ Global coverage of 220+ countries, 10,000+ document types, and over 3,000 data points from trusted sources and partners worldwide.
❇️ A large set of features and checks, including PEP and Sanctions Screening, Adverse Media Checks, ID Document Verification, Biometric Checks, Liveness Detection, Government Database Checks, Address Verification, and more.
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ComplyCube makes it easy to verify identities and stay compliant with regulations. It helps businesses onboard customers smoothly while following the rules. With simple tools to check identities and manage risks, ComplyCube is a great choice for any company needing to keep things legal and straightforward.
This was one of the most pleasant SaaS integrations I've ever experienced. Simple documentation, quick engagement from Sales all the way to Support. We wanted to launch our product in two countries, then scale to 16 within 4 months. ComplyCube was extremely supportive and provided us with KYC strategy and a platform that's flexible and useful for our business needs. Very pleased!
We've tried several SaaS platforms in the identity verification space, but we were left frustrated with complicated integration steps and not particularly unhelpful support and sales.
ComplyCube (and shoutout to Vic and Lucas) were brilliant from the get-go! The API documentation is rich and easy to follow. Integrations took us a couple of hours and our clients are breezing through the onboarding process keep it up guys!
Based on our record, Pandas seems to be more popular. It has been mentiond 197 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.
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 1 day ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / about 2 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 4 months ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential. - Source: dev.to / 5 months ago
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