Many scientists compare the universe to a network of tiny vibrating strings, smaller than subatomic particles.
String theory suggests that these strings, as they twist and vibrate give rise to everything around us – matter, energy and even forces like gravity. But this idea of interconnected threads isn’t just confined to science; it also has implications for banking.
In a bank’s ecosystem, invisible connections – similar to those strings – can deliver significant business impact as they transform and optimize processes and deliver insights that unlock innovation opportunities.
Smallest particle, biggest problem
While saying start with the smallest particle might sound easy enough, it’s perhaps the most challenging part of this formula – data. Data is the smallest particle and biggest challenge, but represents a “big oil” opportunity for banks moving into 2025. But to be frank, for many banks, it’s also a bit of a mess.
Banks aren’t lacking data, rather they have so much rich customer and non-customer data pouring in as a result of digital transformation and acceleration that it’s now an overwhelming challenge to manage.
The data problem has multiple challenges: quality, management, storage, lineage and governance. To further complicate matters, data is scattered across the organization in databases and various platforms.
These combined challenges make it incredibly difficult for banks to connect, integrate and democratize data and ultimately result in an inability to make fully informed, data-driven, holistic decisions and, further downstream, have negative impacts on customer experience. Add all of this up and what do you get? Rising operational costs, inefficiencies, digital transformation and AI implementations that sputter along versus smooth and efficient implementations. Additionally, banks are missing out on rich insights that can help drive real progress across numerous areas.
To begin tackling this small particle/big opportunity that data presents, banks must refocus on the basics and ensure they have an effective data management and governance framework in place. Doing so will help them advance more rapidly and efficiently and will uncover new opportunities for innovation and differentiation that will resonate with customers and drive revenue opportunities.
Strings between things
As banks work to solve the data silos challenge they must also work to solve the organizational silos. Customer interactions occur across numerous functions of the bank – marketing, onboarding, fraud, risk, ongoing customer management, and collections to name a few.
Making the invisible strings between these areas visible with strong ties across data, advanced analytics, and technical architecture will enable faster and more informed decision-making, better collaboration, and streamlined operations.
This connectivity doesn’t just enable continuous improvement; it empowers banks to harness their teams' collective intelligence. With advanced analytics, AI, and machine learning, banks can become more adaptive and resilient, spotting potential risks while seizing opportunities for innovation. The key to all this? Enterprise customer decisioning.
Viewing the web from above
Banks make thousands – if not many millions – of strategic, operational, and transactional decisions. As the data pours in and the speed of innovation moves at a blistering pace, they need the ability to fast-track the time to decision but doing so requires a broader view of interconnectivity.
As banks work to understand the small particles and connected threads at a micro level, it’s important to take a bird’s eye macro view as well. Enterprise customer decisioning helps banks do just that, it provides a broader view across the entire customer relationship and journey and results in significant benefits in terms of economies of scale and a better way to see and manage interconnected risks.
Here’s a practical example: Competition from fintechs offering frictionless loan applications has pushed leading banks toward straight-through processing for transactions like loan approvals. By connecting and integrating across fraud, risk and customer intelligence banks can reduce the time for loan decisioning from two or three weeks down to two or three days. Doing so changes the customer view and relationship as they have faster cash-in-hand, and the bank benefits from improved process efficiency and a deeper relationship with the customer that is more apt to expand over time.
“There’s a competitive advantage for banks to gain by improving their service to make it more frictionless than their competitors, “ says Carl Eastwood, Global Lead, Fraud and Financial Crimes, SAS, “Banks are working to fund loans within seconds rather than days. So, a customer can be standing on the car lot and paying for their car because they’ve applied for and been approved for a loan in minutes.”
Micro to string to web – Are you ready?
Banks need to answer several questions to evaluate their readiness to move toward an enterprise customer decisioning model.
- What is the current state of our data, data management and governance framework? Is there work to be done, and if so, where and how will it be started?
- Where are we on our cloud journey and are our technology partners ready and able to support that transition?
- Are our internal teams ready to come together and find a common business language so that we can advance integrated decisioning across areas of the business that are not typically integrated?
- Does our current technology provide the micro-to-macro view we need to make better decisions? Are those systems appropriately integrated across parts of the business to enable collaboration? Are those systems connected to enterprise governance and compliance?
- Will an enterprise customer decisioning framework and supporting technology help us better align with stakeholder demands and needs, whether that is strategy set at the board level or meeting and exceeding customer expectations?
When theory becomes reality
If the above evaluation results in many “yes” responses or many “yes, but there’s a lot of work to do” responses, then an enterprise customer decisioning effort may need to be on the bank’s roadmap if it’s not already.
While the string theory analogy we’ve been using here has yet to be proven through a demonstrable experiment, enterprise customer decisioning is a reality with real-world applications and tangible results and benefits.
In today’s crowded market with more competition, more risk, and more regulatory pressure, time is truly of the essence. The speed at which banks make solid, transparent, and defendable decisions and the impact of that speed and experience on the customer journey cannot be understated. But more than that, the connectivity across banks provides deeper and better insights that help drive transformation, innovation, and differentiation in a financial world that’s always evolving.