Money laundering has become an undeniably common issue over the past few decades now. As per the International Monetary Fund (IMF), money laundering accounts for 2-5% of the world’s GDP, which is massive. Therefore, both financial and government institutions are continuously searching for better approaches to battle these criminals.

Anti money laundering is a term that covers all these laws, regulations and procedures that the government has taken to prevent the criminals from concealing the origin of illegally earned money and eventually showing it to be legitimate earnings (aka money laundering). AML regulations direct the financial and other institutions to continuously verify the origin of unusually large amounts of cash, cheques and report the same in case of any suspicious activity. 

It’s been a challenge for financial institutions to continuously comply with the increasing number of AML and KYC requirements while not scaring off the prospect clients from opening their bank accounts, and performing heavy transactions. 

Nowadays, customers are accustomed to signing up easily to services provided online like social networks or on-demand streaming from their computers or mobiles with a few clicks. Online services are completely operational and they enable customers to enjoy the services, subsequent to giving some personal details. Though, still in case of banking services, converting a non account holder of a bank into a new one is far from being direct. The main challenge being the requirement of verifying the customers remotely in real time to comply with KYC/ AML regulations and combating the financing of terrorism (CFT) .

With the 4th AMLD from European Union, Online Identity Verification is going to be the starting point for AML compliance in the digital world. Understanding the cost effectiveness and demands of the customers for the availability of these services online, the updated 4th AML directive is an indication that the governments are finally becoming comfortable with digital verification. 

Customer Due Diligence is the first basic step performed by the financial institutions to comply diligently with the AML/KYC regulations. If the scope of risk remains undetermined after performing this, companies are required to do Enhanced Due Diligence(EDD) of their clients. It involves steps to know the customer’s details and business activity. Basically, CDD is a part of KYC. Companies and financial institutions of all sizes are required to verify their customers’ name, address photo proof for which they ask for a government issued id. Moreover, Customer Due Diligence goes hand in hand with online verification which automates the whole process.

Having a solution like AZYO can easily solve all these concerns and it helps companies to meet KYC, AML, GDPR & CCPA compliance. 

AZYO is the market leader in Automated Identity Verification and with the power of Convolutional Deep Neural Networks, Powerful Graphical Processing Units, Advanced Machine Learning Algorithms, AI-Driven Automation, Optical Character Recognition, Blockchain, Facial Recognition & Liveness Detection – AZYO is consistently evolving to verify identities and prevent fraudsters.

AZYO’s Facial Recognition Technology can identify people in pictures, video, or in real-time. It includes both face identification and face verification (also called authentication). Facial verification is concerned with validating a claimed identity based on the image of a face, and either accepting or rejecting the identity claim (one-to-one matching).

AZYO’s REAL OCR converts different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data. AZYO’s OCR is smart, trained and specialized which allows individuals and businesses to convert hard-copy content into digital files while simplifying the verification process.

AZYO’s REAL OCR engine supports a wide range of documents like Passport, Driver’s License, Lease Agreement, Resident Permits, Bank Cheques, Tax Invoices and likewise . Moreover, the images captured by OCR engines are further analyzed with machine learning algorithms to validate the authenticity of the documents.