AI Agent for Credit Lead Recall: Klio Case Study
In 2 months, Klio recalled outstanding loan applications, boosting conversion from 5.80% to 13.55% and halving the cost per appointment. Ready to recover leads without expanding your team?
Thousands of loan applications remain outstanding because no one manages to recall them multiple times. With an AI agent for credit lead recall, Klio automated recalls for **1,176** leads, completing **5,246** attempts in two months. The result: conversion for those who answered rose from **5.80%** to **13.55%**, and the cost per appointment dropped from **€10.97** to **€5.86**, with confirmed appointments passed directly to consultants. ## TL;DR - 5,246 attempts on 1,176 leads: repeated recalls without burdening the team. - Conversion for those who answered: from 5.80% to 13.55% in 2 months. - Cost per appointment: from €10.97 to €5.86 (−46%). - Confirmed appointments transferred to consultants, not call lists. - 7/7 coverage and time-optimized sequences based on response times. ## The Client: Italian Credit Company with a Network of Consultants An Italian company in the credit sector generates thousands of loan inquiries every month. The bottleneck wasn't acquisition, but lead processing: it takes an average of **4.6** attempts to get a response. Previously, recalls were sporadic and manual; the objective was to deliver only confirmed appointments to consultants, without new hires.  ## The Challenge: Achieving Volume with Quality and Consistency Three obstacles hindered the work. - No one answers on the first ring: multiple attempts are needed to reach the contact. - In credit, there's no room for improvisation: no promises on amounts, rates, or conditions in the first call. - High volumes require automation: managing thousands of manual attempts is unsustainable. The project's core focus was recovering unconverted leads, with clear and repeatable qualification rules. ## The Flow: From Interest to Confirmed Appointment The useful outcome is a qualified appointment, with an agreed-upon date and time. The AI agent for credit lead recall follows four consistent steps. ### 1) Recall: For Those Who Requested Information Klio starts with lead data and the relevant branch. Those who don't answer enter a recall sequence: many opportunities unlock after the first attempt. ### 2) Opening: Context and Reason for the Call Identifies the interlocutor, informs about potential recording, and references the previously submitted information request. ### 3) Interest: Is It Still Current? No economic evaluations over the phone: the consultant decides on amounts and conditions. Klio offers a free, no-obligation consultation. ### 4) Appointment: Verified Calendar, Confirmed Date Asks for preferred day and time, checks the calendar, and proposes alternatives. Records only after explicit confirmation. ## The Numbers: Fewer Leads Processed, More Appointments Closed | Metric | First Month | Second Month | |---|---|---| | Leads Processed | 823 | 353 (−57%) | | Call Attempts | 3,854 | 1,392 | | Attempts per Contact | 4.6 | 3.9 | | Response Rate | 52.4% | 60.6% | | Confirmed Appointments | 25 | 29 (+16%) | | Conversion for Responders | 5.80% | 13.55% | | Conversion on Total Leads | 3.04% | 8.22% | | Conversation Minutes per Appointment | 21.94 | 11.73 | | Cost per Confirmed Appointment | €10.97 | €5.86 | In the second month, processed leads decreased by **57%**, but appointments increased by **16%**: the agent focuses time where there's engagement, without "burning" the database.  ## Klio's Voice: One and a Half Minutes to Book The script is essential and transparent: "Am I speaking with [name]? This call may be recorded; do you wish to proceed? Some time ago, you requested information about a loan: are you still interested? Do you have a preferred day and time?" Three elements make it credible: immediate clarification of the call's purpose, no financial promises (referral to consultant), and closure only after explicit confirmation of date and time. This is the difference between an AI voice agent for booking appointments and a dialer reading a script.  ## Impact on Conversion, Costs, and Team - Conversion for responders: from **5.80%** to **13.55%** (+133%). On total leads: from **3.04%** to **8.22%** (+170%). - Costs: **−46%** on cost per appointment (from **€10.97** to **€5.86**), with conversation minute cost of **€0.50**. - Team: **−38%** in total minutes (from **548.5** to **340.2**), more appointments. **7/7** coverage. This makes the adoption of an AI call center for financial companies sustainable, even for cold contacts. ## Why It's Not Just Call Automation - Intelligent sequences: times and days adapt to when people actually respond. - Consistent qualification: same criteria on every call, no variability between operators. - Clean handover: consultants receive an agenda, not a list to call back.  ## Conclusion Klio transforms outstanding inquiries into confirmed appointments, without increasing staff. Fewer wasted attempts, more meaningful conversations, and controlled costs. Want to see the same flow for your leads? Request a demo and test an agent on a real sample. ## Frequently Asked Questions ### How does it differ from a traditional call center? An AI agent handles high volumes with persistent recall sequences and uniform qualification rules. It reduces idle time (waiting, voicemails) and routes only confirmed appointments. In this case, it increased conversion from 5.80% to 13.55% and significantly cut the cost per appointment, while maintaining consistent conversation quality. ### Is it compliant with privacy and GDPR? The agent notifies of potential recording and proceeds only with consent. Data is processed for stated purposes (recall based on information request and appointment booking), with limited and auditable retention. Policies include data minimization, access control, and deletion upon request. Governance is designed for regulated contexts like consumer credit. ### Which KPIs should be monitored to assess impact? The main ones: attempts per contact, response rate, conversion for responders, conversion on total leads, conversation minutes per appointment, and cost per appointment. In the case study: 4.6→3.9 attempts, 52.4%→60.6% response, 5.80%→13.55% conversion, 21.94→11.73 minutes, €10.97→€5.86 cost per appointment. ### Does it integrate with existing CRMs and calendars? Yes: it reads leads from CRM, updates outcomes, and synchronizes with calendars to propose real slots. Deduplication, preference management, and notes for the consultant ensure orderly handovers. The goal is to deliver confirmed appointments, not open tasks, reducing operational friction. ### How quickly are results seen? Optimization is progressive: response patterns emerge in the first month, and in the second month, the agent focuses efforts on the most effective windows. In the case study, appointments increased by 16% while processing 57% fewer leads, with a significant improvement in conversion and costs. ### When is it beneficial to use an AI voice agent for booking appointments? When there are many outstanding inquiries, lists of contacts to re-engage, and teams are occupied with higher-value activities. It's ideal for dormant lead recovery, evening/holiday coverage, and re-engagement campaigns, maintaining message consistency and cost control.