Company Profile
Company Type
Wealth management company
Team Size
20-100 employees
Industry
Finance
Key Challenge
Struggling with inefficient manual customer retention processes that were slowing growth and increasing operational costs. Their primary concern was audit readiness.
Tools Connected
The Challenge
Manual customer retention was the biggest bottleneck in this wealth management company's operations. Their team of 20-100 employees processed hundreds of customer retention requests weekly, each requiring multiple steps, cross-referencing against finance-specific requirements, and coordination between departments. The average customer retention request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their customer retention output was inconsistent. Different team members followed different procedures, and there was no standardized way to handle edge cases that are common in finance. A recent audit revealed that 12% of completed customer retention records contained errors that required rework — costing the organization an additional $50K annually in correction and remediation efforts. The leadership team recognized that continuing to throw people at the problem wasn't viable and began searching for an AI-powered solution.
The Solution
Arahi AI provided the automation backbone this finance team needed. They deployed a multi-agent workflow that breaks the customer retention process into discrete, automated steps — each handled by a specialized AI agent. The first agent monitors triggers from QuickBooks and Stripe. The second agent analyzes and processes incoming requests using finance-specific business logic. The third agent executes actions across connected tools and notifies team members via Slack.
The beauty of the no-code approach was speed of implementation. The team had their first agent live within 90 minutes, and the full customer retention workflow was operational within a single afternoon. They used Arahi AI's template for finance customer retention as a starting point, customized the business rules to match their specific process, and connected their existing tool stack without writing a single line of code. Within the first week, the agents had processed over 200 customer retention instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key finance customer retention metrics.
Monthly Churn Rate
60% reduction
Before
5.2%
After
2.1%
At-Risk Detection Lead Time
Proactive vs. reactive
Before
After cancellation
After
14+ days before churn
Retention Intervention Success
189% improvement
Before
18%
After
52%
Annual Revenue Saved
$340K impact
Before
$0 (no proactive program)
After
$340K recovered
NPS Score
142% improvement
Before
24
After
58
“We went from spending half our day on customer retention to having it just happen automatically. The AI agents handle the routine work perfectly, and our finance team can focus on the strategic decisions that actually move the needle. I wish we had done this a year ago.”
VP of Operations
Wealth management company
Key Takeaways
The most important lessons from this finance customer retention automation project.
This finance team proved that customer retention automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling customer retention capacity by 10x without adding headcount fundamentally changed the economics of their finance operations.
Consistent AI-powered processing eliminated the quality variance that came with different team members handling customer retention differently.
Real-time visibility into customer retention metrics gave leadership the data they needed to make better strategic decisions.
Implementation Timeline
From zero to production in 90 minutes — here's how they did it.
Step 1: Mapped the existing customer retention workflow
Documented every step of the current manual customer retention process, including decision points, exceptions, and handoffs between team members. Identified which steps could be fully automated versus those needing human oversight.
Step 2: Built the automation in Arahi AI
Used Arahi AI's no-code builder to create the customer retention workflow: connected QuickBooks and Plaid as data sources, configured AI decision logic for finance-specific requirements, and set up automated actions and notifications.
Step 3: Parallel run with manual process
Ran the AI agents alongside the manual process for one week to compare outputs. The AI matched or exceeded human accuracy on 98% of customer retention instances, with the 2% of edge cases automatically flagged for human review.
Setup Time
90 minutes
AI Agents
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating customer retention in finance.
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