KYC. Know your customer. Easy right? But what does knowing your customer actually mean? Clearly, it’s not about befriending their family, taking them out for a steak dinner, or showing up at their wedding. But, a lot more goes into KYC than just asking them to upload a selfie with an identity card.
As the pace at which the fintech and cryptocurrency sectors continue to grow, so does the need for fighting financial crime and preventing money laundering.
Companies have the legal or moral obligation to ensure that their customers are not involved in shady dealings. Are the customers they’re onboarding good or bad? Are they sanctioned? Are they watch-listed? Do they have any political levels of exposure? All these questions involve an incomprehensibly large amount of data.
Traditional KYC/AML practices involve extremely lengthy manual processes that are not only inefficient, expensive, and prone to human error, but also extremely time-consuming. Imagine in the world of crypto trading, being told that your application would be reviewed and approved in two weeks, or more? It simply wouldn’t be viable.
So how do businesses who want to scale quickly, onboard as many customers as possible without having to sift through millions of people and their individual paper trails?
KYC Using Automation
Companies have been making use of Big Data for some time now and the process of KYC/AML is getting faster and more efficient. Complyadvantage, for example, relies on machine learning and AI to deliver essential information to their clients (banks, governments, cryptocurrency exchanges) in real time. This means that they can onboard customers quickly.
Through machine learning, they can identify trends and patterns amongst customers instantly. Just by analyzing a corporate or government website to identify the context around individuals, the company can build reliable profiles on them.
Different Levels of Risk
For a crypto exchange, it’s probably enough to know that you aren’t dealing with a drug trafficker or sexual offender. But advanced tools can go far beyond traditional research to create the entire picture. Say, for example, a customer is not sanctioned and has no criminal record but they did appear in the press for misconduct, perhaps for a minor crime or disturbance.
Football fans dancing on a table is of little preoccupation for a company holding an ICO. But for a political candidate or businesses working with a high level of risk, this kind of prior information is critical.
It’s also vital, then, that the level of risk is categorized, so that companies like exchanges and banks aren’t leaving money on the table unnecessarily. Level 1 may be indicative of high risk, say, while level 4 is low.
Perhaps your company policy allows you to work with risk on a 3-4 level, or a 3-2. This type of detail in the data allows companies to scale and onboard as quickly as possible–without falsely flagging, wrongly barring customers and sending them to the competition.
As regulation steps up around the world, exchanges, wallets, and other blockchain companies can scale quickly by using compact intelligent tools that they can rely on.
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