AI's emerging dual role in Fintech driving personalization and operational efficiency

Lifecycle marketing is under pressure to deliver smarter results, faster, and cheaper. With automation being key to scaling personalized, precision-targeted experiences, AI is clearly a must-have for staying competitive, efficient, and customer-focused. But what is the most effective way to use it for CX in a Fintech setting?

Aboli Gangreddiwar, Director of Lifecycle Marketing at Fintech Credible, has over a decade of experience driving user growth and engagement in B2B and B2C Fintech and marketplace environments. Her work for companies including Wells Fargo has centered on high-impact campaigns across multiple channels.

Operational edge: Fintech marketers are at the forefront of navigating the dual challenges of achieving operational speed in a transactional market, while also focusing on precise customer targeting. Gangreddiwar sees AI as a powerful tool for both, but suggests a pragmatic approach. "AI can help a lot with hyper-personalization in marketing—that's going to be a big use case," she notes. "At the same time, the lowest hanging fruit is going to be on the operational efficiency side."

Autonomous efficiency: In her role at Credible, Gangreddiwar's focus on operational efficiency stems from the intense competition in Fintech lending. "It's a very competitive market," she explains. "Lending products are very transactional in nature, so operational efficiency is really key." The concept of agentic AI, where AI systems operate more autonomously, is gaining traction, with AI agents already automating marketing tasks like customer segmentation and data analysis. Many see 2025 as the year these agents "join the workforce."

Strategic targeting: Beyond delivering at speed, AI can also help refine marketing strategies. Fintechs have become adept at using machine learning to target customers based on the abundance of consumer credit data they hold. For Fintechs with multiple product offerings spanning from banking to lending, the challenge is clear, Gangreddiwar says: "How do you figure out which is the best product to market to a user?"

Build vs. buy: She points out that the rise of third-party AI tools is a welcome development for marketers with lean data science teams. Platforms such as Hightouch use AI agents to deliver 1:1 customer experiences by determining optimal messaging and timing, effectively making data science more accessible. The central question for marketers, Gangreddiwar notes, becomes: "Do I build these agents, do I wait for my marketing automation tool to include them, or do I buy them?"

Unified messaging quest: AI agents also offer a solution for marketers struggling to achieve omnichannel consistency. Disjointed systems and siloed departmental ownership often mean "your in-app messaging will be different from your email messaging," Gangreddiwar says. While B2B companies may have dedicated product marketing functions to champion a unified customer voice, in B2C, "oftentimes that responsibility falls on the lifecycle marketing team." She anticipates the arrival of what she calls PMM (product marketing manager) AI agents, tasked with the goal of unifying messaging, enforcing brand voice, and generating consistent content.