If you feel like businesses are speeding up, it’s not just you. There are many macroeconomic trends impacting the historic patterns that we see in the market today — an accelerated pace of financial services digitization, industry transformation due to COVID-19, a rise in the prevalence of digital assets such as cryptocurrencies, the growth of mobile money at double the forecasted rate, and more. This pressure creates a need for financial services providers to verify their customer identities, faster.
Match rates enter the picture as a crucial metric for compliance teams to monitor and optimize.
A make-or-break technical process
For those who deal with identity daily, match rates tend to become an obsession. Operations for match rates vary between quick automated verifications and slow manual processes. If a customer, an individual or business, does not pass a verification during the KYC (Know Your Customer) process, there may be a need to trigger even more follow-up processes.
Fast, efficient, and accurate match rates are the ideal. When companies can quickly onboard customers, growth becomes more frictionless. That’s why some organizations rely on layers of APIs and technical methods to verify identities — every identity verification solution is different, and it’s business-critical to avoid the potential for error.
Excellent match rates lead to quick and seamless customer onboarding while poor match rates lead to frustrated customers, higher transaction costs, more strain on support and lost revenue.
Identity verification is a scalable process, meaning that if your operations are not seamless, there’s a potential for something to break. Excellent match rates lead to quick and seamless customer onboarding while poor match rates lead to frustrated customers, higher transaction costs, more strain on support and lost revenue.
Calculation methodologies, explained
Match rate as a metric
An identity match is when a person’s identity information meets the requirements for a positive match. The identity match rate considers the whole group of people being verified and is the percentage of submitted identities that does meet the requirements for a positive match.
To understand match rates, it’s helpful to de-aggregate the group-level metric to the individual level process.
If a person is a positive match for the identity information they provide, they can be considered verified and can go on to become a customer. If a person doesn’t pass, there’s a need for further investigation, which requires additional human capital. Multiply the above scenario by thousands, or even millions, of customers and the importance of match rates becomes apparent.
Problems that occur at the individual level for identity verification have the potential to scale up.
Conditions for success
Behind the scenes, identity verification technologies are a blend of art, science, and continuous improvements. Here are a few conditions for success, that every business has the ability to optimize.
Having good data
The first step to superior match rates is having data that is accurate and extensive. Data that is out-of-date or doesn’t represent data that people will submit is challenging to match against. Questions about how the data is acquired, managed, updated and cleansed need careful consideration.
The other main factor here is how much of the population, or target audience, does the data cover? The more coverage, the higher the potential match rate is. Note, a data set might have extensive coverage, but not be accurate, or vice versa. Understanding the strengths and weaknesses of various data sets is crucial to running a robust verification program.
Getting good inputs
No matter how good your data is, each individual decides how to share information with you. And, with each person being unique, there are numerous ways that people will fill different fields. If the provided input doesn’t match the expected format, a no match can occur.
Consider the phone number field; which is the right format to provide that data?
The phone number is one of the most straightforward pieces of data. The formats of names and addresses can vary substantially, significantly affecting match rates.
Meeting verification criteria
Going back to our definition of match rates, the phrase “meet the requirements” is another primary consideration. What are the use cases, or the legal or regulatory requirements, if any? What is the risk management approach? What is the cost consideration?
Every jurisdiction, industry, company, service and customer are different, so identity requirements vary.
Perhaps a simple verification is sufficient and a match for a name and an address, or a name and a phone number, will suffice. Or, maybe the risk level is higher, and multiple data points and ID documents need checking. The match rates in these two scenarios will vary widely, so understanding what data needs checking and how it affects match rate are vital considerations.
Match rate expertise
Ensuring your identity verification program succeeds comes down to understanding the interplay between all the different match rate factors and how they ultimately affect your verification rates.
It’s about understanding the data and optimizing results by combining data sets and data sources. It’s about knowing how people input data into forms and how to extract that information to get the best match results. It’s examining the different scenarios and discerning the most appropriate approach considering the needs, costs and desired outcomes.
The best identity verification program requires access to the best data and the means to handle that data to get the best match rates. But more so, it is about having the knowledge and experience in multiple markets and use cases to deliver match rates that work best for you and your specific organizational needs.
Learn more about the processes, marketplace and solutions of the current identity verification landscape and make informed purchasing decisions for your business.