AB Handshake Reviews

Plaid is a global fintech infrastructure platform that enables businesses and consumers to securely connect to financial data and services. Through APIs that connect to more than 12,000 financial institutions across 20+ countries, Plaid operates the world’s largest open banking data network. By removing the complexity of direct bank integrations, Plaid enables developers, financial institutions, and enterprises to build secure, user-friendly, and conversion-optimized financial products. Plaid powers payments, lending, identity verification, fraud prevention, and personal finance applications. Today, more than 100 million people worldwide, including half of U.S. adults, have relied on Plaid through their connections to over 7,000 apps and services. Plaid supports a wide range of industries and financial workflows, including: Banks & credit unions, Payouts streamline disbursements, and Pay by Bank supports direct consumer-to-business payments for recurring bills, subscriptions, and eCommerce, Mortgages & lending, Government & public sector programs, Property management, Gaming, Crypto & Digital Assets, Business finances, and Wealth Management. Customers include Venmo, Robinhood, Rocket, Carvana, Affirm, Invitation Homes, and many more. Plaid is built with enterprise-grade encryption, continuous monitoring, and regulatory compliance, including PSD2 in Europe and FCA requirements in the UK. Plaid does not hold funds but enables secure data transfer and payment initiation. Plaid provides trusted infrastructure across payments, identity verification, fraud prevention, account connectivity, and open banking. With scale, security, and mobile-first design, Plaid helps businesses create seamless and secure financial experiences for millions of consumers worldwide.

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.