Why Banks Need to Rethink Data Strategy for the AI Era
For years, many banks have
viewed their data strategy through the lens of a technology initiative,
focusing on areas such as cloud migrations, data lakes, tech stacks and
platform connections. These investments certainly matter, but the real purpose
of a data strategy should be improving decision making and driving measurable
business outcomes.
Banks that continue to view
data primarily as a technology exercise risk falling behind at a time when
customer expectations are skyrocketing, competition is intensifying and
emerging technology such as AI is changing how institutions operate. Success does
not depend on how much data a bank has but rather how effectively it can turn
data into smarter decisions, better customer experiences and favorable business
outcomes.
Customers are becoming channel
agnostic
Customers are no longer
thinking in terms of channels, a notable shift. It doesn’t matter whether
they’re interacting in digital banking, branches, contact centers or ATMs, they
expect their financial institution to understand them and help them with their
needs. Customers increasingly demand consistency, context and convenience no
matter through which touchpoint they’re banking.
For example, a customer might
use their mobile app to locate cash, visit an ATM to withdraw the money and
then immediately use those funds to make a credit card payment. These aren’t
separate experiences for the customer, but parts of a holistic financial
journey.
Delivering this level of
contextual awareness requires connecting data across channels, systems and
partners in real time. Banks are being challenged with how to make all of their
data work together to provide a more holistic view of the customer
relationship.
This is where advancements in
AI play a powerful role. Physical banking touchpoints are evolving from
standalone transaction devices into intelligent, interactive endpoints capable
of learning from customer preferences and behaviors.
For example, imagine that an
ATM is able to recognize that a customer typically chooses $50 denominations,
frequently checks available balances before making withdrawals and prefers an
experience consistent with their primary financial institution. These seemingly
minor details can create a personalized, relevant customer experience, driving retention
and loyalty.
Lead with the business
objective
There is a common
misconception that more data automatically leads to more value, but this is not
always the case. Without a clearly outlined business objective, additional data
often just adds complexity. In fact, AI can exacerbate issues associated with
poor data quality, making outcomes faster but less reliable.
Instead, banks should always
start by identifying the business problem they’re trying to solve. Whether the
goal is growing deposits, increasing self-service adoption, reducing fraud or
improving customer satisfaction, the data strategy should directly align with
and support measurable business goals. Evaluating data initiatives based on
whether they can create scalable, quantifiable value will help banks more
effectively prioritize.
Governance as an enabler
One of the most misunderstood
aspects of modern data strategy is data governance. Governance is too often
viewed as a set of restrictions that limit access to information. In reality,
effective governance should make trusted data more accessible and useful.
The most successful banks are
establishing centralized governance principles while decentralizing execution. A
central governance team defines standards, policies and controls, while each
business unit maintains responsibility for data quality and accountability for
outcomes, creating trust without bottlenecks.
Trust is especially important
as AI becomes more deeply embedded into operations and customers increasingly seek
transparency about how their data is being used and the reasoning behind decisions.
Strong governance provides the foundation needed to answer these questions with
confidence.
The future is real-time
decisioning
The most notable opportunity
facing banks when it comes to their data strategy may be moving beyond
historical analysis toward real-time operational intelligence. While data has
historically been used to answer questions about what happened in the past,
banks are starting to use data to decide what should happen next.
This is a powerful use case
for modern AI: transforming data from a retrospective reporting tool into a
decisioning engine that generates alerts, recommendations and actions. This
shift can have notable impacts across the organization, from fraud prevention
and service availability to customer engagement and self-service interactions.
The most successful banks will
no longer view data as an after-the-fact reporting function and begin treating
it as a bridge between business strategy and execution.
As AI continues to advance and
customer expectations rise, the competitive differentiator will not be who has
the most data. It will be who leverages the most effective data strategy to make
better decisions, create seamless, intelligent and personalized experiences
across every digital and physical touchpoint, and meet the most critical
business objectives.
About Author:
Maha Sivara is the Chief Data Officer
for NCR Atleos, a leader in expanding
self-service financial access for financial institutions, retailers and
consumers.