AI user research for financial services
From abandoned applications to low NPS, AI interviews reveal where customers get stuck and why they don't sign up or renew. Compliance can review the guide first.
Common challenges in financial services user research
Satisfaction research is mostly NPS scores, with no clear reasons behind the low ones
Product terms are complex, so surveys can't tell you why customers don't sign up or renew
Strict compliance requirements mean long approval cycles for external research
Customers are guarded in interviews, so sensitive topics rarely get honest answers
What financial services teams research with heari
Why customers abandon account opening and applications
Pinpoint the exact step where customers drop off. Start from the page they exited and uncover the specific barrier—whether it's paperwork, friction in the process, or a lack of trust.
How customers perceive—and what worries them about—wealth and insurance products
Do customers not understand the product, or not trust it? Follow up on how they read the terms, risks, and returns, and where their concerns come from.
Complaints and low-score follow-up
Trace vague low scores back to the exact experience that caused them, and turn complaints and low NPS into actionable fixes.
Mobile banking app experience
See how easy it is to complete everyday tasks like transfers, balance checks, and investment purchases—and where customers get stuck or frustrated.
Relationship manager experience
How often customers want to hear from you, and how. Hear real stories of dealing with relationship managers: what builds trust and what puts people off.
Wealth and investment decisions
How customers choose investment products, and why they hesitate. Trace how they compare channels, weigh returns and risk, and finally decide.
Financial services research example
A typical study you can run right away. The research assistant adapts the plan and interview guide to your business.
Study plan
Sample interview guide
The AI interviewer follows the guide and asks follow-up questionsWhy they applied
Core questionWhat did you plan to use the loan for?
Follow-upWhy this bank
Where they got stuck
Core questionWhich field were you filling out when you stopped?
Follow-upWhat happened at that point
Concerns
Core questionWas there anything that worried you?
Follow-upThe exact wording or clause
Trying again
Core questionWhat would bring you back to finish the application?
Follow-upThe one thing that most needs to change
Data shows what customers did. heari finds out why.
Connect ThinkingAI Agentic Engine to select customers by application and usage behavior. Bring their click paths into interviews, and write findings back as customer tags.
What the behavior data shows
Spent 6 minutes on the proof-of-income upload page, left, and didn't log back in for 3 days.
What the heari interview found
"I didn't have proof of income on me. I'd have to get it from work, so I figured I'd do it later. Then I forgot."
What the behavior data shows
No transactions in the 60 days after opening the account.
What the heari interview found
"I only opened the account for the sign-up bonus. I can't make sense of the risk ratings on the investment products, so I'm afraid to buy anything."
Compliance and data security in financial services
No sensitive data collected
Interviews never collect account numbers, transactions, ID numbers, or other sensitive data.
Compliance review first
Your compliance team can pre-approve the interview guide and set boundaries for follow-up questions.
Full audit trail
Interview records and review history are kept in full and can be exported for archiving.
No investment advice
The AI interviewer only asks and listens. It never gives investment or product advice.
Find out what's driving low scores in a single round of interviews
Bring your satisfaction or churn data, and we'll run a compliant, controlled pilot study with you in heari.
