Revamping KYC/AML Standards with Artificial Intelligence
Artificial Intelligence has got covered the financial services industry through its innovative solutions that are transforming Know Your Customer (KYC) and Anti-money Laundering (AML) standards. To combat the risks of fraud, AI systems are managed which help in tracking the malicious transactions and associated present and future risks based on the overall monitoring perspectives. An intelligent decision making knows whats cooking inside the system, hence come up with the observations that seem dangling from the legitimate path.
The identification of high-risks has become easy with AI technology. AI has the ability to perform multitasking that can be replaced with human efforts that are utilized for manual processing. The resources, efforts and valuable time would be employed for some other organizational tasks. AI technology uses hybrid technologies made up of the knowledge of Natural Language Processing (NLP) and Machine Learning (ML). This fusion results in an efficiently automated system, particularly in labor-intensive areas.
The traditional institutions which include financial institutions, banks, insurance companies, etc. which are in dire need of complying with KYC/AML procedures can take advantage of these growing technologies. Understanding the result accuracy level and how efficiently the technologies can perform certain tasks, KYC/AML processes can be automated. AI-based technologies can help in digital identity verification, AML screening and transaction monitoring of identities.
Enhanced Due Diligence
To enhance the due diligence process in an organization, AI can help automate the process of client profile verification. Client risk profile can be verified in mere seconds against several databases and through the process of identity verification. This will ensure the need for regulatory compliance as well as company standards.
The document verification process can be performed through AI technology. AI techniques capture the template of the document, run spoofing and photoshopped test to make sure that the document has not tampered in any way. Secondly, it extracts the data out of specified fields from the document and verifies it. Online document verification can help in the easy verification process. Similarly, biometric authentication, especially face verification can also be performed and through AI, liveness detection can be done. Also to match the unique facial features, AI technology is used.
With efficient verification methods, KYC analysis can fulfill the need for ‘Know Your Customer’ compliance. KYC process has become streamlined with the use of innovative methods that are replacing manual KYC verification processes.
AML Screening and Transaction Monitoring
Automated AML screening involves identity verification against sanction lists which include a list of Politically Exposed Persons (PEPs), databases of criminal records, money launderers, and exposed cybercriminals. Instead of manually checking identities against this list, automated AML screening can help your business comply with the regulatory regimes.
Similarly, for transaction monitoring, AI plays an important role. Not only once at the time of account registration identity verification is also required, but it should also be done on a continuous basis. Transaction monitoring is hard to perform manually, AI techniques can be used to detect suspiciousness in the money flow.
Embedding the knowledge in the AI model can help reduce the risks of dirty money flow. It is difficult to track whether money is coming from an honest source or transferred to an honest node. For this too, the AI model has trained that identity the malevolent actors in the ecosystem and are disrupting the kind flow of money.
To conclude, the AI revolution is remodeling the ways the KYC process was performed before. It is introducing innovative measures through which KYC can be completed efficiently. Through digital identity verification, Customer Due Diligence (CDD) processes have become a piece of cake for KYC analysts.