When the economy tight, financial institutions face several mutually reinforcing challenges. The temptation for bad customer actions is increasing. This creates more regulatory oversight, with the risk of huge fines for non-compliance.
The drive to reduce costs jeopardizes continued investment in innovative financial products and services, while at the same time customers have higher expectations than ever for convenient, effective and great experiences.
On paper, this looks like a slam-dunk scenario for the burgeoning industry of new agile fintech providers. It isn’t, unless those fintechs can learn some lessons from established companies about customer onboarding. Those lessons ultimately boil down to the marriage of process automation and a data factory.
Why focus on onboarding?
The onboarding experience is the customer’s first impression of the organization and sets the tone for the relationship. It is also the point where the organization must accurately determine who the customer is and what the true purpose of their business is. Fast and accurate customer onboarding is always important, but in an economic downturn it becomes doubly so: investors quickly lose patience with startups that cannot deliver growth and margin at the same time as regulators crack down on risks in the financial sector.
Effective onboarding is the Achilles heel of fintech. A data fabric that unifies information without extracting it from registration systems is the answer.
Effective onboarding is the Achilles heel of fintech. Look at WISE, fined $360,000 by the Abu Dhabi regulator. Or the fine from the British Financial Conduct Authority GT Bank £7.8m for AML failures. Or, Solarisslapped the German Bank-as-a-Service (BaaS) provider with a restriction not to onboard prospective customers without government approval.
The inability of fintechs to properly manage the data and processes required for accurate onboarding may be a large part of the culprit investment decline in 2022.
Data fabric and process automation improve onboarding
Onboarding starts with verified details, things like a name, an address, a tax number, details of the proposed company, where the money is coming from and where it is going. The problem is that financial institutions are large, complicated organizations with countless IT systems and applications that contain siled data sets. These legacy systems for different products, customer types, and compliance programs don’t integrate well.
That means there’s an incomplete picture of things, and trying to complete that picture usually involves manual cutting and pasting between systems and spreadsheets. The mere possibility of human error should be enough to terrify any bank executive.
a data fabric – a technology that unites all company data – without removing it from the registration systems – is the answer. The data fabric creates a virtual data layer where volatile business data, and the relationships between that data, can be managed in a simple low-code environment. The data is secured at the row level, meaning only the people who should see it can see it, and only when they should see it. The data can be on-premises, in a cloud service, or in multi-cloud environments.
A data fabric approach allows you to combine business data in entirely new ways. This means that you not only have a 360-degree view of the customer, their identity, history, product(s), but you can also gain new insights by viewing your business data holistically.