Truist

Truist, a top-10 US bank formed through the merger of BB&T and SunTrust, entered the market with a mandate to reimagine banking through personalization. With over 7 million digitally active customers logging in an average of 18 times per month, the bank faced a scale challenge common in retail banking: how to convert high-frequency digital touchpoints into meaningful, individualized guidance rather than generic notifications. Without a systematic AI-driven approach, the sheer volume of customer interactions made it impossible to deliver timely, relevant financial insights across all channels — leaving significant engagement and financial well-being value on the table.

Truist partnered with Personetics to deploy the Cognitive Banking platform, which applies machine learning and predictive analytics to customers' transaction data in real time. The system continuously analyzes spending patterns, account activity, and behavioral signals to generate personalized financial insights — surfacing alerts, spending observations, and proactive guidance automatically. Rather than relying on static rule sets, the platform uses ML models to determine which insight to deliver, through which channel, and at precisely the right moment in the customer's financial journey. Personetics' platform integrated across Truist's full digital stack, enabling insight delivery within the mobile app, online banking, and other client touchpoints without requiring customers to actively search for guidance.

Truist has delivered over 1 billion personalized insights in total since deploying the Cognitive Banking platform, with more than 500 million insights delivered in the past year alone — signaling accelerating adoption and platform maturity. The insights achieve a 16% click-through rate, a strong signal of customer relevance in a channel where most financial notifications go unread. The bank's mobile app maintains a 4.5-star rating, reflecting both the quality of the experience and customer trust in the platform's security. Qualitatively, the deployment demonstrates that AI-driven personalization can operate at enterprise scale without sacrificing experience quality.

• High login frequency (18x/month average) multiplies the ROI of personalized insights — the more customers engage digitally, the more value a well-tuned AI insight engine generates.
• A 16% click-through rate on proactive insights is achievable when ML models are trained on actual transaction behavior rather than demographic segments alone.
• Embedding personalization across the entire digital client journey — rather than isolating it to a single feature — is what enables insight delivery to meet customers in context.
• For retail banks, financial well-being messaging that is timely and specific outperforms generic product promotion in both engagement and brand trust.
• Enterprise-scale deployments benefit from vendor platforms purpose-built for financial data, reducing compliance risk and time-to-insight compared to building in-house.