My son started his job right after he finished college, with his first real paychecks and new responsibilities including finding a place to live, financing a car and thinking about the financial impact of proposing to his girlfriend. During one of our regular calls, he asked me a question about a subject school didn’t prepare him for: “What is the correlation between applying for and using credit and my credit score?”
In that brief moment was a world of potential for financial institutions today. An AI-powered mobile notification could sense a prime opportunity for personalized education delivered at a time of need. Evidence of awareness by a bank or credit union at this pivotal point in my son’s life, a tailored video tutorial or interactive chat could empower this new entrant to the workforce with a very important lesson in financial literacy.
Moments that build potential lifelong relationships occur every day – yet banks and credit unions often miss them amidst the transactional aspects of traditional banking. Generative AI promises to help financial institutions create vastly deeper relationships by pinpointing opportunities, then generating the optimal content and engagement strategy for every unique customer.
Does the value outweigh the risk? The three key considerations for evaluating whether to invest in growing an internal program or exploring an outsourced solution.
This webinar will show how to develop marketing strategies that will generate new checking account volume.
The Dawn of Generative AI Era
Conversational interfaces like ChatGPT hint at AI’s impending impact on customer experience. Beyond rote service queries, these new tools provide expanding capabilities for contextual, intuitive interactions. And with the ability to learn rapidly – each iteration reveals enhancements reflecting consumer usage and trends.
Generative AI represents the vanguard of experience personalization. Human-like language models can create boundless volumes of customized conversations, educational explanations, marketing messages and advisory recommendations tailored to individual financial contexts, profiles, goals and knowledge levels. They bring relevance at speed and scale.
Current successes on this frontier span industries. Netflix employs AI to generate thousands of personalized movie trailers from a single film. Disney uses it to allow performers to converse with visitors in recognizable character voices. Australia’s fourth-largest bank ANZ recently introduced a home loan explainer bot that breaks down the intricacies of mortgages into simpler conversational language based on the customer’s familiarity with the complex product.
For financial marketers seeking resonance, creative AI will soon transcend human capacity to deliver hyper-relevant experiences.
The State of Banking Personalization
Unfortunately, most banks have miles to go before reaching personalized engagement proficiency. Bain research last year found a significant “relevance gap” persists between what customers expect from personalization and what banks deliver. Less than one-third believe their bank understands their needs and preferences thoroughly enough to deliver tailored solutions, indicating most efforts remain superficial thus far. The impact of this lack of understanding is an increasing level of “silent attrition” as customers expand the number of financial institutions they use.
Part of the dilemma is data itself. Financial institutions possess expansive transaction logs and engagement history at the core of contextual personalization. But insights teams report ongoing challenges harnessing siloed information from disparate systems to inform integrated strategies. Engineering legacy infrastructure for analytics heightens complexity.
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Adding second-party data enriches profiles, but raises governance considerations. While external signals create comprehensive pictures of embedded lifestyle preferences on social platforms or browsing habits, ethical questions around consent and transparency emerge. That said, research shows that illustrating empathy and using data for the benefit of customers will open the door for greater use of expanded insights.
Compliance risks also lead risk-averse banks to avoid personalized recommendations in messaging. Broad sloganeering around “better rates” or “earn more” persist despite access to details on actual deposit or loan balances that could inform specific savings advice tailored to a household. Providing individualized financial guidance at scale remains technologically immature.
More than ever, generative AI awakens new possibilities to resolve each obstacle – but only for those bold enough to adopt early.
How AI Boosts Relevance
Applied prudently, here are four ways leading financial institutions leverage generative AI to craft tailored interactions that foster enduring engagement across customer lifecycles:
1. 24/7 Financial Literacy Education
Contextual educational content at scale is the holy grail for banks seeking to build relationships around financial wellbeing. However, manual subject matter creation requires tremendous resource investments.
Enter AI – shared data environments allow models like ChatGPT access into reams of bank-approved information on products, markets, regulations and consumer financial needs. Cloud-based tools can synthesize learning materials personalized to nuanced user profiles at incredible volumes in near real-time.
ANZ’s home loan explainer chatbot uses AI to fill knowledge gaps with on-tap resources – no branch trip required. OCBC Bank similarly employs AI to craft personalized financial tips based on spending behaviors, life stages and past queries.
Educational AI augments human advisors, allowing them to reserve specialized guidance for complex needs. But democratic access to information builds equality and trust. Participants feel empowered while institutions gain loyalty.
2. Behavioral Insights for Proactive Care
Generative AI soon won’t just react to inquiries, but will be proactive as illustrated by US Bank showing leadership by informing, protecting and advising customers that have unique financial situations. Sophisticated analytic engines use spending declines on routine services like daycare or streaming subscriptions to trigger tailored outreach addressing potential financial hardship.
With further data integration, intelligent systems can anticipate consumer stress tied to late mortgage payments. AI then generates a pre-emptive notification checklist with customized assistance on restructuring options, local aid programs and relief sources specific to that family’s unique situation.
Whether struggling customers appreciate aid privately to avoid embarrassment or publicly to catalyze support, financial institutions providing that real-time compassion differentiate relationships through data-enabled empathy and support.
3. Predictive Intelligence for Preemptive Value
Predetermined life milestones like my son’s first job, home ownership or my future retirement require significant but often overlooked financial shifts. Younger consumers especially need guidance navigating first-time complex decisions on savings plans or major debt obligations. Missed opportunities to steer in these pivotal moments lead to relationship abandonment or missed opportunities for relationship expansion.
But behavioral signals identify inflection points. AI can scan shared datasets spanning spending patterns, web browsing, survey responses and more to accurately predict major changes ahead. Generative tools craft communications addressing anticipated needs from branching into parenting and small business accounts months before delivery, to countdowns preparing for a cross-country move.
When revealed preferences yield to AI foresight for timely utility, not hindsight-based offers alone, marketing earns its place in customers’ lives. Algorithms spotlight moments where education, products and budgeting tools provide unique lifetime value.
4. Optimized Multichannel Orchestration
Despite access to robust data, most campaign personalization remains compartmentalized by channel instead of centrally optimized based on cross-channel intelligence. This limits contextual relevance across touchpoints when digital experience barrages consumers daily.
Generative AI now enables intricate, integrated segmentation where desktop web interactions inform the next best conversation for a mobile app. Content can seamlessly evolve across advertisements, ATMs, statements, in-branch tablets and call center scripts based on unified customer data and predictive models. This provides unified conversations across all distribution channels.
Where human coordination of this orchestration complexity breaks down, AI autogeneration brings coherence. The most public-facing teams use these tools today for audience targeting. But their backend application coordinating omnichannel messaging stands to accelerate returns on data for relevance.
Overcoming Existing Barriers
Despite the vast array of benefits, generative AI ushers in as many apprehensions today as anticipations for supporters. This is especially true in financial services, where risk avoidance often stifles innovation and progress. Despite watershed advances, most employees with institutional knowledge on data governance and infrastructure complexity harbor doubts. Similar to questions revolving around deployment of cloud solutions five short years ago, reassurance is required to compel laggard organizations support adoption.
Frustrations certainly pervade early testing phases. Engineering usable data flows requires foundational upgrades, from APIs to reporting protocols enabling model feedback. User studies expose areas of fragility needing reinforcement through further training.
But concerns show signs of subsiding as more organizations report positive results from initial applications. Orienting AI to enhance roles and activities – first call routing, then offline service documentation – rather than wholly replace humans builds internal buy-in. As confidence grows, so do usage domains; USAA already reports high member enthusiasm for its AI assistant Eva fielded across websites and apps to answer thousands of queries from its military community base.
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Still, most digitally mature organizations acknowledge much work remains translating promise into practical strategies at scale. The keys reside in governance, metrics, a willingness to embrace change and patience for progress. But as more success stories emerge, the more skeptics will sign on to drive transformation from within.
The Road Ahead for Generative AI and Banking
How far along the adoption curve will financial institutions reach with generative AI when today’s middle schoolers look for personalized ways to save or understand the power (and risks) of credit? When industry leaders gathering at trade association meeting swap lessons learned, which case studies will they share?
Many see a future state where sentient assistants shepherd consumers their entire financial journey. AI avatars evolve alongside households anticipating decisions and nudging beneficial behaviors daily. They provide guardrails against fraud, relief in emergencies and expertise ensuring sound choices despite boundless freedom.
Yet even the most progressive financial institutions foresee human advisors maintaining irreplaceable roles – their compassion and empathy during turmoil means more than even the most emotionally intelligent algorithms. Instead, the vision taking shape positions AI as amplifiers allowing people to focus energy where it shines brightest. Behind intuitive tools, teams emerge unburdened by rote tasks made obsolete. Their expertise scales.
The time to move forward with generative AI is upon our industry. How will we embrace the opportunity to change the lives and financial future of our customers?