A customer’s onboarding journey is often the most interaction they will have with their bank. Onboarding activities provide the first meaningful insights into what being a customer will be like, and they can make or break the long-term prospects of the relationship.
Unfortunately, the onboarding journey at many banks is rife with inefficient processes and disjointed experiences, which can lead to customer loss before the relationship even begins. Banks are also grappling with growing risk and fraud pressures and the need to deliver differentiated customer experiences while managing significant increases in operating costs and ineffective data management strategies and solutions.
As innovators chip away at banks’ stronghold on the market, it’s more important than ever to streamline and automate the onboarding journey and subsequent customer management to ensure a solid and fruitful relationship.
Connecting the dots
In 2024, banks will be working to connect the dots of data across the organization to reduce fraud, manage risk, and deliver a seamless customer journey and exceptional customer experiences. To do this, they’re employing an enterprise decisioning strategic model that helps break down internal data silos and glean data-driven insights that help them manage risk more effectively, enhance customer experiences, and increase operational efficiency.
Risk of legacy systems
As the world has gone digital, banks have encountered a new type of fraudster, adept at using AI and other technologies to compromise organizations and consumers. Banks with legacy systems are far less likely to detect the types of fraud these new bad guys are pushing – such as synthetic identity scams and even updating established scams like check fraud.
The bad guys know who’s still operating legacy systems, and they know how to circumnavigate them to avoid detection. This wouldn't be an issue if these banks had connected and holistic views of their data.
Beyond fraud, legacy systems also threaten risk management, as they don’t allow banks to move with agility or make changes to decisioning strategies. Adjusting policy rules or deploying new analytics is a lengthy process in these cases.
Yet banks operate in a rapidly changing world, where interest rates are high and credit risk tightens profit margins. There are numerous interconnected risks in our digital world, and lagging doesn’t bode well for banks that still depend on legacy systems. The inability to respond with agility to changing market dynamics as banks work toward embedded finance, digital and contextual customer journeys, and real-time decisions could spell disaster as risks are amplified quickly.
Doing more with data
As banks apply and scale their enterprise decisioning strategies, they can fully leverage the data they already have available. Bringing together all data points into a unified platform allows banks to make more effective, accurate and efficient decisions while reducing costs and breaking down the number of steps required to make decisions.
Each stage of a customer lifecycle involves a multitude of touchpoints and numerous decisions. A holistic view of data gives banks a complete, 360-degree view of each customer – and each customer’s risk profile.
AI innovation will continue to explode in 2024, and with it comes an evolving set of challenges for banks across both fraud and risk. As with digital evolution, banks can expect the steady drumbeat of consumer demand for banks to do more to provide seamless and frictionless interactions.
The implementation of enterprise decisioning will help banks tap into their vast data stores by breaking down silos to gain insights that lead to real-time, data-informed decisions with a consistent approach across the customer lifecycle.
