AI that turns digital leads into credit, and collections into data
CrediQ needed to keep up with growing demand from digital channels. We built online pre-approvals, machine learning models for collections and a mobile collections app.
About CrediQ and the challenge
The client
CrediQ offers credit to consumers in El Salvador, and more and more of its leads were arriving through digital channels.
The challenge
Growing digital demand
Leads from digital channels were growing faster than the team could process them.
Knowing who is ready
Applications needed to be prioritized by purchase intent.
Smarter collections
Collections needed intelligent tools, not more manual follow-up.
Signature feature
Pre-approved,
in minutes.
Digital leads move through an online pre-approval, and collections are guided by machine learning instead of manual follow-up.
- Online pre-approvalApplicants get an answer without visiting a branch.
- Intent-based priorityLeads ranked by how ready they are to buy.
- Smart collectionsMachine learning models and a mobile collections app.
Interface illustration, not real client data.
Our approach
From diagnosis
to transformation.
- 1
Detect
Rising digital demand handled with manual processes.
- 2
Analyze
Customer service flows, application times and collection behaviour across platforms.
- 3
Build
An online pre-approval model, machine learning models for collections, a mobile collections app and predictive data models.
- 4
Transform
Faster applications and a strategy measured with real data on user behaviour.
New services through digital channels, and better collections behind them.
Our takeaway from this project
Deliverables
- Online pre-approvals
- ML collections models
- Mobile collections app
- Predictive analytics
A group project
This project was delivered with TechyWe, TocheeTech’s sister company and engineering partner. The same senior team builds TocheeTech projects, with a Toronto project lead and a Canadian contract.
Next project
Banco Cuscatlán
From doing agile to being agile