Hi everyone,
I’m currently working on a project with a collection agency that buys overdue credit databases from banks and other financial institutions. They manage hundreds of thousands of credits but are understaffed, with only 50 agents dedicated to making calls. Our goal is to maximize collected revenue by effectively prioritizing which credits to pursue.
Here’s the current setup:
- I have access to a table containing registered payments from the last few years, each with its unique ID.
- I also have access to the original databases provided by the banks.
What I’d like to achieve:
- Use AI to identify which credits are “similar” to those successfully collected in the past.
- Apply AI to identify and qualify credits that are likely to be collected based on historical data.
I’m an advanced Power BI developer but a newbie in AI and not a coder. My vision is to provide a list to call agents that maximizes their potential for collection.
I’m seeking advice and ideas on:
- How to get started with integrating AI into this process.
- Best practices for using historical data to predict future collections.
- Any tools, resources, or methodologies that could help in achieving this goal within Power BI.
Any recommendations or insights would be greatly appreciated!
Thanks in advance for your help.
Best regards,
Juan Aguirre