Michael Beckley
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.
The data fabric underpins all process automation. End-to-end process automation uses a variety of technologies to do two primary things: move work through the organization efficiently and make that work transparent and auditable at every step of the process. The technologies in play are workflow, robotic process automation (RPA), artificial intelligence/machine learning (AI/ML), business rules, and process mining for continuous improvement. With this automation approach, every aspect of onboarding can be accelerated, tracked, and made visible.
It is vital that these automation technologies all work seamlessly together in a single business process. Using unified automation technologies underpinned by a data fabric on a low-code platform dramatically simplifies what’s required to achieve total process visibility and true continuous process improvement.
AI in the mix
New AI tools like ChatGPT can only really achieve value for the business if they are effectively incorporated into new digital workflows. Otherwise, companies will be doing one thing quickly and other places will still be slowed down by inaccessible data or legacy systems that have constrained workflows. What startups and companies need is a platform that helps visualize, change and improve the entire end-to-end process.
Orchestrating AI into the onboarding process can help employees make quicker, more informed decisions. Through AI algorithms, an FI can create a new, self-service portal for their clients in minutes. They can create a new website where customers can browse and sign up for different financial products.
Then, through intelligent document processing (IDP), which is made up of AI algorithms, banks and institutions can use native AI to classify, understand and extract data from emails and documents quickly and securely. This makes the onboarding process more accurate, effective and efficient, requiring fewer manual touches.
Where does ChatGPT play a role here? ChatGPT can help build that onboarding workflow and help with creating standard communications more effectively with parameters and variables from the differences of each product and customer. ChatGPT can generate a lot of that communication, but it must be audited and controlled by a human to ensure that there are no mistakes or inaccuracies. It is about putting AI intelligently into a human workflow where the human is still in control.
What’s next for client onboarding?
AI now automates a lot of the ways businesses and consumers interact with each other. If you think about some of the most complicated financial transactions e.g., creating trusts, estate planning, M&A, these are huge parts of the economy. Due to their importance, these transactions are taxed with a tremendous amount of regulation, manual data entry and errors that frustrate our lives considerably and cost consumers every year.
AI will help restore the much-needed simplicity for companies by handling the mundane, bureaucracy and repetitive tasks with a depth and efficiency that has been previously difficult to achieve without expensive investments in digital transformation.
To implement process automation for client onboarding today, here are two important steps to get you started.
- Understand your onboarding process and workflow by visualizing in a process automation platform. This will allow companies to identify bottlenecks and prioritize where automation technologies can be used to have the most impact on growing the business quickly.
- Unify your end-to-end process on a common data fabric, which enables secure and easy access to data and delivers a 360-degree view of an organization.
These important steps form a foundation for making sure that your AI is accurate. Any automation – bots, AI, human workflows, business tools – needs accurate information to function properly and predictably to deliver the results that startups and enterprises want.
Start connecting your data
For a long time, businesses have tried to flex all business and customer data in one place. Now that we work with APIs that connect us to different systems, business today happens across networks and platforms that no single institution controls.
My final advice:
- Start modeling your end-to-end business process on top of a data fabric using a low-code platform.
- Understand your business, client onboarding, and where RPA, AI and other automation technologies can be most effective in helping you grow sustainably.
- Keep your automation focus on empowering employees and keep the human element strong and prominent within your processes.
It is much more important to stop collecting all the data and start connecting it instead. When we are in a more connected world, we deliver better results and experiences for our teams and customers.
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