Like many in retail banking today, you may feel as if you’re between a rock and a hard place. To your left, you have the pressures of rapidly advancing technology; to your right, shifting customer expectations. What was acceptable, or even lauded, as industry leading just a few years back can now mark you—in the hearts and minds of an increasing portion of your addressable market—as legacy and irrelevant.
To not only survive, but thrive, banks must evolve beyond traditional data-driven models and adopt a signal-oriented approach—an operational mindset where data signals from AI models, data events, and triggers are seamlessly integrated into business workflows to drive decisions at scale.
What are signals in banking?
In banking, a signal is the actionable insight derived from AI models, data events, and triggers. More data, plus better AI, should equal more and better signals—and therefore better business outcomes. Often, this takes the form of a prediction—and one you can do something about. Forecasting when customers will churn, down to the individual level; identifying fraudulent transactions in real time; or optimizing lending offers based on individual circumstances are all great examples of what you can do when you get signals right.
Unlike raw data, signals are designed to directly inform and enhance business processes, creating value by making decisions faster, smarter, and more precise.
Banks that can integrate signals into their business processes are making the fastest, boldest steps to becoming truly AI-powered organizations. These will be among the first banks to scale decision-making while adapting to dynamic customer needs and regulatory requirements.
Why are signals critical for banks?
1. Customer expectations are skyrocketing
Modern consumers demand hyper-personalized experiences. This is especially true in a low-interest-rate environment, where customers are no longer swayed by price alone—service becomes the key differentiator. Banks that win will offer the best access to the best service at the right time, including thoughtful and proactive support for vulnerable customers.
Siloed data and inconsistent workflows prevent a unified understanding of customer behavior, making it difficult to deliver timely, relevant, and personalized experiences across channels.
Signals enable banks to anticipate individual needs, then offer tailored products and well-timed services. For example, by analyzing a customer’s payment patterns, the bank could trigger preemptive credit offers or fraud alerts— delivering value to their customer at just the right moment.
2. Regulatory compliance is complex (and nonnegotiable)
The regulatory environment for banks is growing more stringent. Signals allow for real-time monitoring and reporting. In short, this means proactive, rather than reactive, compliance. By putting AI to work, banks can flag potential compliance risks early, which in turn means they can avoid costly fines and build trust with regulators.
3. Cost efficiency is a must
Economic pressures, from inflation to rising credit demand, are pushing banks to do more with less. Manual decision-making and redundant processes will hinder scalability and drive up costs.
Signals reduce costs by automating processes like risk assessment, fraud detection, and customer support—freeing up human agents for higher-value tasks.
4. The risk of falling behind
Banks that fail to adopt signal-oriented strategies risk losing their competitive edge to more agile, AI-savvy competitors. This could result in declining customer loyalty, missed revenue opportunities, and operational inefficiencies.
How can you embrace the signal-oriented future of banking?
To become a signal-oriented bank, you should consider these practical steps:
1. Invest in end-to-end signal integration
AI-driven signals offer two distinct benefits: effectiveness (improving decision outcomes) and efficiency (reducing time, cost, and friction). Achieving both depends on real-time data orchestration, scalable AI platforms, and embedding signals seamlessly into workflows.
Prioritize technology that automates the generation and integration of signals into your workflows. This will ensure AI insights flow seamlessly into operational processes, from fraud detection to customer service.
2. Enhance your data readiness
Trusted AI can only be built on trusted data. Work to break down data silos and adopt tools that enable real-time data orchestration—in one central location. A focus on minimizing data movement will also reduce inefficiencies.
3. Upskill your workforce
Train teams to understand AI-driven insights, with a clear mandate to act based on those insights. The most competitive banks in the future will have fostered a data-driven culture where decision-making is rooted in actionable signals.
4. Start small, scale fast
It’s a good idea to begin with targeted AI projects that address high-priority challenges, such as fraud detection or customer retention. Once proven, you can scale these models across other areas of the business.
5. Engage with experts
Partner with experienced technology providers, such as Teradata, to access the tools, expertise, and infrastructure needed for a signal-oriented transformation.
The future of banking belongs to those who can harness the power of signals. By adopting a signal-oriented approach, banks can enhance customer experiences, improve operational efficiency, and stay ahead in a rapidly evolving industry.
The journey starts with understanding your current capabilities, identifying the gaps, and taking decisive steps toward integration and scalability. Teradata's ready to help.