Onboarding and automation: What fintechs can learn from big banks

When the economy is tight, financial institutions face several mutually reinforcing challenges. The temptation for bad action on the part of customers increases. This creates increased regulatory scrutiny, with the risk of massive fines for non-compliance.

The urge to reduce costs prevents continued investment in innovative financial products and services, while at the same time customers have higher expectations than ever for simple, efficient and good experiences.

On paper, this looks like a slam-dunk scenario for the burgeoning industry of nimble new fintech providers. It isn’t—unless these fintechs can learn some lessons from established firms about customer onboarding. These lessons ultimately come down to the combination of process automation and a data structure.

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 determine exactly who the customer is and the true intent of their business. Fast and accurate customer onboarding is always important, but in an economic downturn it becomes doubly so – investors quickly lose patience for start-ups that cannot deliver growth and margins at the same time as regulators clamp down on risk across the financial sector.

Effective onboarding is fintech’s Achilles heel. Look at WISE, fined $360,000 by Abu Dhabi regulator. Or, UK’s Financial Conduct Authority fines GT Bank £7.8m for AML failings. Or, Solaris, the German Bank-as-a-Service (BaaS) provider, slapped a restriction on not bringing in future clients without government approval.

Fintech’s inability to manage the data and processes required for accurate onboarding could account for much of the decline in investment in 2022.

Data fabric and process automation improve onboarding

Onboarding starts with verified data, things like a name, an address, a tax ID, details about the proposed business, where the money comes from and where it goes. The problem is that financial institutions are large, complex organizations with countless IT systems and applications containing siled sets of data. These legacy systems across different products, customer types and compliance programs integrate poorly.

That means there’s an incomplete view of the case, and trying to complete that view usually means manual cutting and pasting between systems and spreadsheets. The possibility of human error alone should be enough to strike fear into the heart of any bank manager.

A data structure – a technology that unifies all corporate data – without moving it from systems of record – is the answer. The data structure creates a virtual data layer where mutable business data, and the relationships between these data, can be managed in a simple low-code environment. The data is secured at the row level, which means that only those who need to see it can see it, and only when they need to see it. The data can be on-premise, in a cloud service or in multi-cloud environments.

With a data structure approach, you can combine business data in completely new ways. This means that you not only have a 360-degree overview of the customer, their identity, history, product(s), but you can also gain new insights by seeing your company data holistically.