when the economy is narrow, financial institutions face several mutually reinforcing challenges. The temptation of bad deeds by customers increases. This creates increased regulatory scrutiny, with the risk of massive fines for non-compliance.
The need to cut costs jeopardizes 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 perfect scenario for the burgeoning industry of agile new fintech providers. It isn’t, unless those fintechs can learn some lessons from established companies about customer onboarding. Those lessons ultimately come down to the marriage 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 at which the organization must determine precisely who the customer is and the true intent of its business. Quick and accurate customer onboarding is always important, but in an economic downturn, it becomes doubly important: Investors quickly lose patience with startups that can’t deliver growth and margin at the same time regulators crack down on growth. risk across the financial sector.
Effective onboarding is the Achilles heel of fintech. A data structure that unifies information without moving it from record systems is the answer.
Effective onboarding is the Achilles heel of fintech. Look at INTELLIGENT, fined $360,000 by its Abu Dhabi regulator. Or the UK Financial Conduct Authority fine GT bench £7.8 million for AML failures. EITHER, SolarisGerman Bank-as-a-Service (BaaS) provider has imposed a restriction not to onboard any future customers without government approval.
The inability of fintechs to properly manage the data and processes required for accurate onboarding may explain much of the decrease in investment in 2022.
Data structure and process automation improve onboarding
Onboarding starts with verified data, things like a name, address, tax ID, details of the proposed business, where the money is coming from, and where it is going. The problem is that financial institutions are large and complicated organizations with myriad IT systems and applications containing siled data sets. These legacy systems across various products, customer types, and compliance programs don’t integrate well.
That means there is an incomplete view of the matter at hand, and trying to complete that view usually means manually 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.
TO data fabric — a technology that unifies all company 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 that data can be managed in a simple, low-code environment. The data is protected at the row level, which means that 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.
With a data structure approach, you can combine business data in entirely new ways. This means that you not only have a 360 degree view of the customer, their identity, history, products, but you can also gain new insights by viewing your company data holistically.
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