Today, small as well as large businesses, have implemented customer identification APIs to prevent their system from online malevolent activities. These APIs ensure AML background checks to mitigate the risks of money laundering activities from happening in the order. These activities can breach the security and result in harsh fines and reputational damage to the organization.
The banking industry and other financial institutions must comply with Know Your Customer (KYC) and Anti-money Laundering (AML) procedures. The regulations should be countered in the system to fulfil the requirements of local regulators.
Automating AML Process
A few years back, financial institutions were supposed to verify identity manually against AML paper records. The customers got verified through documents, and it was the responsibility of the onboarding agent to check the documents and keep a file of records. This overhead of managing paper records and auditing AML processes was time-taking and prone to mistakes done by the staff in maintaining audit records.
But now, manual checking has been replaced by automated online customer identification. In this digitalized world, automated trial processes that undergo all the checks and keep a digital record against each onboarding customer. The market of global automation has estimated a 60.5% increase in the Compound Annual Growth Rate (CAGR) from the year 2014 to 2020 with the revenue generated of $5 billion by 2020 which was $0.18 billion in 2013.
This immense value estimated depicts the need for and utilization of automated processes in the digital world. AML compliance is ensured to keep system sound from money launderers. Also, they are checked against the Politically Exposed People (PEP) record to keep banned identities from performing any transaction over the network. This electronic record is updated in the database every time on identifying any bad actor.
Auditing of Huge Amount of Dataset
Identifying and verifying an identity against a massive amount of data is challenging to keep track of. Furthermore, to streamline the auditing process of AML records, it is necessary to take advantage of technological advancements and introduce automated auditing trial for resilient AML compliance.
The global online services collect a vast amount of dataset on their databases that belong to the personal information and sensitive data of people. This data is collected, maintained and processed efficiently. Using artificial intelligence (AI), the system is automated when it comes to checking the identity against AML electronic record before registering a customer on an online portal.
For this, different algorithms are implemented that ensure privacy and performance at the same time. Also, it is necessary to take care of user experience for onboarding customers. The verification time should not delay the tasks of the customer and provide a robust system to proceed to the user request.
Customer Due Diligence (CDD)
This process includes checks for the potential risks associated with onboarding customers. Also, the validation of existing information in several repositories is part of CDD. The data is collected and verified against the electronic AML records which ensure the authenticity of the customer identity. In this way, a KYC documentation is formed that includes all the data and customer status regarding AML screening.
Data are auto-generated with the application of Artificial Intelligence and machine learning techniques. These help data management and processing efficiently.
Automated Transaction Monitoring
AML screening is an essential use-case of automated AML auditing. These new customers, transactions and money transfer across different countries are monitored by keeping an eye on the activities of the customers. y
Alerts are managed in order to monitor the transaction activity. This system is automated. As it is a complicated process to track every transaction individually, and so automating transaction monitoring can help in validating the transaction across boards.
In automated transaction monitoring, during the data collection process and transaction process, suspicious transaction produces an alert in the system. This alert is verified by the associates, and it is discarded if it is true.
Machine Learning and Transaction Monitoring
Transaction procedures and patterns are different for each business. Also, business rules are different, which becomes a hurdle in monitoring the transactions. To helps with these; machine learning comes to the rescue.
Machine learning is used to analyze the large dataset and detect anomalies in the transaction process irrespective of the business rules. The data is retrieved efficiently by utilizing machine learning and so can be processed in a better way. In this way, AML checks are implemented, and customers are verified based on their activities.
But, along with this, there is a problem in integrating machine learning models. Many financial institutions and businesses do not trust the decision-making of machine learning models. This depends on the business type and rules. Also, the accuracy of machine learning relies on the form of data that is retrieved.