The next time you visit your local bar, consider this: How do you differentiate between a barstool and a table? Both have four legs, flat tops, and you can prop a drink, or yourself on either one. However, putting a cup on a barstool doesn’t make it a table. Context is the key indication that helps us understand which item is which.
Similarly, financial institutions and other businesses looking to successfully onboard new clients must become proficient in rapidly categorizing different types of customers and figuring out how to engage them. In today’s digital age, direct and highly automated engagement channels are becoming more common, making it crucial for organizations to understand that data without context could be quite detrimental to a successful client experience.
Without context,a simple equation for categorizing things could become problematic. Once one understands that the definition is derived from intent or purpose, the picture becomes more than just a list of parameters.
Is verification hurtful or helpful?
Verification is a process that allows a business to make decisions based on information provided by traditional data sources, such as credit bureaus. Verifying a client helps an organization know which data is needed to approve or reject a client. However, it does not leave any wiggle room for exercising discretion based on specific circumstances. Even if an error occurred throughout the verification process, such as the inability to validate an address, there is no room for complex decision making.
Verification is a binary function that categorizes information into “yes” and “no” responses, making it too easy for a user to “fail” a check that, if structured slightly differently, would allow them to onboard as a new client.
However, when additional context is provided, the same client who may have been rejected through the verification process alone can be successfully on-boarded based on a deeper and better informed look at their personal information.
For example, if a lender were to be presented with a potential client for a loan who cannot be validated for proof of address, this client would be flagged as risky without any context.
However, this same borrower profile changes if we include some contextual information such as age. If the individual is a 25-year-old, it is highly likely they are a renter and don’t own a home, which would therefore explain the lack of address validation. The age may further indicate that traditional data sources could prove to be less reliable than others such as social media information. This is especially true for companies dealing with businesses that exist solely in the digital space, for which traditional data is sparse. The entire business is conducted online leaving much need for alternative data sources such as online payment processors and e-commerce platforms.
Another example is that of credit history. If an applicant for a mortgage were to move to the United States from a country that doesn’t have a requirement for individuals’ credit scores, the applicant would be flagged and probably not receive a mortgage. Once context is provided as to the applicant’s nationality, the financial organization reviewing the application will have a better understanding of whether the applicant is truly eligible.
Using Tech to Better Know Your Customer
Context is what distinguishes between verifying a customer and knowing a customer, and the right technology is what makes it possible to add this context for a large number of decisions. Technology gives an organization the ability to attribute weight to different data points allowing the data to better inform the decision.
By using additional context and technology, verification processes can become KYC processes. KYC strives to minimize uncertainty, the riskiest and potentially costliest operational environment for organizations of any size. The processes are driven by why do I need this information and what is the best way to attain it instead of just do I have a specific piece of information.
Through machine learning processes, technology like Robotic Processing Automation (RPA) can bridge the gap between a potential loss and a gain for a company. RPA systems automate the task by watching the user perform the task instead of a programmer creating a list of automated actions to complete a task. The result is providing a company with greater flexibility in automating actions that a user would otherwise have to perform.
Similarly, by using application programming interfaces (API), companies can access many different data sources in real-time to ensure they have a well rounded picture of the risk factors associated with new clients.
Further, OCR and biometric imaging provide an organization with the ability to use physical data points by digitizing data from documents such as IDs, and further standardize the way KYC processes are carried out.
Check out your KYC options
The KYC process provides information not only regarding the business’ decision of whether to accept the client, but also provides context for what further data sources should be utilized for identity verification. Through the use of various data suppliers and technological resources, KYC processes can turn from creating loss to profit generating. To find out more about how to replace your costly and outdated customer verification process into state-of-the art automated KYC processes, check out this vendor search filter and compare Scanovate to the rest of the competition.