When the economy However, financial institutions face several mutually reinforcing challenges. The temptation for customers to take bad action increases. This increases regulatory scrutiny and the potential for hefty fines for non-compliance.
The urge to reduce costs jeopardizes continued investment in innovative financial products and services, at a time when customer expectations for simplicity, effectiveness and a great experience are higher than ever.
On the surface, this looks like a slam dunk for the nascent industry of nimble fintech providers. Not so — unless these fintechs can take some lessons from the established players about customer onboarding. These lessons ultimately boil down to a combination of process automation and data structures.
Why focus on onboarding?
The onboarding experience is a customer’s first impression of an organization and sets the tone for the relationship. This is also the point at which organizations must determine exactly who their customers are and the true intent of their business. Fast and accurate customer onboarding is always important, but it becomes even more so in a downturn—investors quickly lose patience with startups unable to grow and thrive while regulators crack down on risk across the financial sector. profit.
Effective onboarding is fintech’s Achilles’ heel. The answer is to unify the data structure of the information without moving the information out of the system of record.
Effective onboarding is fintech’s Achilles’ heel.look at Sensible, was fined $360,000 by its Abu Dhabi regulator.Alternatively, the FCA fine GT Bank £7.8m for AML failures. or, Solaristhe German banking-as-a-service (BaaS) provider is restricted from onboarding any future customers without government approval.
The inability of fintech companies to properly manage the data and processes required for accurate onboarding could be a major reason for this Investment falls in 2022.
Data Fabric and Process Automation Improve Onboarding
Onboarding begins with verified data such as name, address, tax file number, details of the proposed business, where and where funds are going. The problem is that financial institutions are large and complex organizations with countless IT systems and applications holding siled data sets. These legacy systems, which span a variety of products, customer types, and compliance programs, don’t integrate well.
This means that the view of the problem at hand is incomplete, and completing that view often means manual cutting and pasting between systems and spreadsheets. The mere chance for human error is enough to strike terror into the heart of any bank manager.
A data structure — A technology that unifies all enterprise data—without moving it from systems of record—is the answer. Data fabrics create a virtual data layer where mutable enterprise data and the relationships between those data can be managed in a simple low-code environment. Data is protected at the row level, meaning only the people who are supposed to see it can see it, and only when they are supposed to see it. Data may be on-premises, in a cloud service, or in a multi-cloud environment.
Using a data structure approach, you can combine business data in entirely new ways. This means not only can you get a 360-degree view of your customers, who they are, their history, your products, but you can also glean new insights by looking at your enterprise data holistically.