Onboarding and automation: What fintechs can learn from big banks

When the economy is tight, financial institutions are faced with 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 imperils continued investment in innovative financial products and services, while at the same time customers have higher expectations than ever for easy, effective, and great experiences.

On paper, this looks like a slam-dunk scenario for the burgeoning industry of new nimble fintech providers. It’s not – unless those fintechs can learn some lessons from established firms about customer onboarding. Those lessons ultimately come down to the marriage of process automation and a data fabric.

Why focus on onboarding?

The onboarding experience is the customer’s first impression of the organization and sets the tone for the relationship. It’s also the point at which the organization must accurately determine 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 rapidly lose patience for startups that can’t deliver growth and margin at the same time as regulators crack down on risk across the financial sector.

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

The inability of fintechs to properly manage the data and processes required for accurate onboarding may 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 of the proposed business, where the money is coming from, and where it’s going. The problem is that financial institutions are big, complicated organizations with myriad IT systems and applications holding siloed sets of data. These legacy systems across various products, customer types, and compliance programs don’t integrate well.

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

A data fabric — a technology that unifies all enterprise data – without moving it from systems of record — is the answer. The data fabric creates a virtual data layer where mutable enterprise data, and the relationships between those data, can be managed in a simple low-code environment. The data is secured at row level, meaning only the people who should see it can see it, and only when they should see it. The data may be on-premise, in a cloud service, or in multi-cloud environments.

With a data fabric approach, you can combine business data in entirely new ways. This means you not only have a 360-degree view of the customer, their identity, history, product(s), but you can also glean new insights from seeing your enterprise data holistically.

Onboarding and automation: What fintechs can learn from big banks by Walter Thompson originally published on TechCrunch