Leading with Consumer Trust in Banking
Reimagining how data privacy and security intersect with the customer experience
Nearly 80 percent of consumers believe their personal data is valuable, and roughly the same number believe the security of their data is extremely important. At the same time, consumers expect banks to provide next-level personalization, convenience, account and payment protection and increasing financial wellness stewardship, all of which is based on their personal data.
At the intersection of these issues is consumer trust. And so, from the bank perspective, the real question becomes: how can we optimize customer interactions in real-time to personalize the customer experience, while upholding safety and ultimately building consumer trust?
Building consumer confidence
In a recent survey of banking customers, 68 percent said the security of their personal information trumps convenience. Sustainable digital competitiveness requires an acknowledgment that trust is front and center. This compels banks to invest in understanding the complete supply and value chain of how consumer data is used within bank systems, processes and decisioning models to support its operations.
This may seem overwhelming, but I introduced three simple ideas in a recent Forbes innovation blog article that offer a practical starting point. They're summarized below.
- Abstract your business operations via a Conceptual Map. This map should encircle the customer, capturing all sources of raw data generated by the customer's activities.
- The data generated is an important reusable enterprise asset, so thinking of it as an internal Product is helpful to promote and manage its use inside the organization.
- Building off this Data Product foundation are an abundance of relationships that can be discovered and captured inside the data. Data scientists call these Data Features and their value stretches beyond analytic models into business decisions, processes and composable business functionality.
Next, let's extend one aspect of these ideas to illustrate the leverage this approach can provide.
Instrumenting for trust
Since all raw data generated by consumers is defined in a library of shareable Data Products, let's consider the role Data Products may play in the trust landscape. Both trust and regulations hinge on the responsible use of consumer data, especially Personally Identifiable Information (PII). Since most Data Products include extensive functionality for annotation and meta-data, you can infuse a lot of information into them. This is where my idea of instrumenting for trust begins. For example, you can indicate what data elements are PII. You can indicate domains of prohibited use such as, this data cannot be used in credit risk decisions but it can be used in marketing decisions. Let's say you design this labelling in a way that you can introduce a new experience in your mobile app for Customer Data Preferences.
Since all raw data generated by consumers is defined in a library of shareable Data Products, let's consider the role Data Products may play in the trust landscape. Both trust and regulations hinge on the responsible use of consumer data, especially Personally Identifiable Information (PII). Since most Data Products include extensive functionality for annotation and meta-data, you can infuse a lot of information into them. This is where my idea of instrumenting for trust begins. For example, you can indicate what data elements are PII. You can indicate domains of prohibited use such as, this data cannot be used in credit risk decisions but it can be used in marketing decisions. Let's say you design this labelling in a way that you can introduce a new experience in your mobile app for Customer Data Preferences.
Customers can toggle on or off a set of data preference options to enable or disable the banks ability to use their data for discretionary purposes. For example, while the customer cannot disable the bank's ability to monitor their payment data for fraudulent activity, they may disable the bank's ability to use it to inform marketing offers.
To do this, you must be able to act on their data preferences across the bank's operation. This is why I refer to the labelling mechanism as instrumenting for trust. Since decisions and models function most accurately with all data at their disposal, the bank's entire decision intelligence apparatus must auto-adjust the moment a customer asserts a new data preference. When the Data Product has been labelled to enable this, models are adjusted in real-time to exclude features that rely on the newly prohibited data, decisions similarly fallback and the customer's wishes are imminently respected.
Market-leading personalization
This approach facilitates a different kind of personalization. I call it personalization to the core. Imagine how nimble you can become in responding to regulatory changes. It isn't just personalization to the core, it's also digital to the core.
Yet, when customers consent to make aspects of their personal data available for your discretionary decisioning needs, it also empowers you to translate that data into more meaningful, tailored experiences. You can react to changing behaviors, keep them motivated to stay on budget and help them efficiently move toward their financial goals.
Taking the first step
Traditional banks are often shackled with old systems, coding and technical debt that make it difficult to move forward. However, the ideas below can help banks move in the right direction.
- Proactively embrace data privacy and security principles that best respect and align with the most rigorous regulation(s) in jurisdictions where you do business. Principles of the legislation should be paired with your own vision for customer privacy empowerment and instrumented into the meta-data attributes of your enterprise data products.
- Enable your digital teams to collaborate and co-create. This involves understanding the dynamics of the digital team, dissolving barriers between silos and easing collaboration between the diverse personas that power innovation. But, don't try to boil the ocean. In fact, incubation is a best practice. It allows your new digital way of working to evolve and take root.
- Avoid becoming ensnared in a risky, expensive legacy system modernization or replacement, if possible. Instead, it may be more beneficial to contain the legacy system in a tight scope, wrap it in an API layer and engage the system in your vision of a more composable business. With these techniques, you're trying to pry open a new space an airgap between the data generated by your customers and your existing systems. Within this airgap, you can produce shareable and reusable digital assets that your digital teams can use to collaborate across functions, and imagine and invent new experiences, offers and value propositions. This is where applied intelligence platforms come into play, providing capabilities that foster a network effect of unrestrained digital value.
In an atmosphere where consumer data protection is exponentially more complex and bank customers expect more meaningful experiences, consider a collaborative, cross-functional approach that addresses both challenges at once. Pry open an airgap, fill it with enterprise intelligence and foster uninhibited collaboration, co-creation and transparency. You'll take control of your digital destiny and deliver more personalized, secure customer experiences that spark tangible advances in consumer trust and financial well-being.
About Author:
Bill Waid is Chief Product and Technology Officer for FICO. In this role, he drives development of FICO Platform, which provides the ideal decisioning foundation companies need to successfully achieve digital transformation. Bill previously was general manager for Decision Management Systems at FICO, helping firms adopt analytics, decisioning and optimization solutions that identify and take action on unique, predictive insights, in real-time. Bill joined FICO in 2002 to lead the formation of the decision management business, now one of FICO's largest and fast-growing offerings, serving thousands of customers in a wide range of industries across the globe. Bill began his career as an engineer for Neuron Data and Blaze Software. He holds a civil engineering degree from Lehigh University.
About Author:
Bill Waid is Chief Product and Technology Officer for FICO. In this role, he drives development of FICO Platform, which provides the ideal decisioning foundation companies need to successfully achieve digital transformation. Bill previously was general manager for Decision Management Systems at FICO, helping firms adopt analytics, decisioning and optimization solutions that identify and take action on unique, predictive insights, in real-time. Bill joined FICO in 2002 to lead the formation of the decision management business, now one of FICO's largest and fast-growing offerings, serving thousands of customers in a wide range of industries across the globe. Bill began his career as an engineer for Neuron Data and Blaze Software. He holds a civil engineering degree from Lehigh University.