Quest 8 of 15
Collections and recovery decision support
AI can help prioritise collections work, but it must not replace affordability assessment, respectful treatment, or accountable recovery decisions.
Start here
This module builds on earlier parts, but every important idea is explained in context. AI can help prioritise collections work, but it must not replace affordability assessment, respectful treatment, or accountable recovery decisions. As you read, connect each concept to the worked example and ask what a person must still decide.
Big idea
AI can help prioritise collections work, but it must not replace affordability assessment, respectful treatment, or accountable recovery decisions.
Learn one idea at a time
Read, explore, then mark each idea when you can explain it.
Idea 1 of 8
Use AI risk scoring exclusively to organise and prioritise collections queues, rather than for automated harassment or threats.
Live interactive diagrams
Tap nodes, stages, or cards to explore — these diagrams match this module’s ideas.
Collections AI: help or harm?
- Prioritise outreach with hardship flags
- Auto-threaten customers from a script
- Suggest options within policy bands
- Hide that AI ranked the queue
Tap Left or Right for each example, then "Why?" for the AI explanation.
Choose a deep dive
Open the topics you want to explore. The detail stays folded until you need it.
Deep dive 1Prioritise contact, not punishment
A queue can help agents identify missed promises to pay or incomplete documents. It should not automatically increase pressure or trigger legal action.
Deep dive 2Bias and vulnerable customers
Historic repayment data can reflect unemployment, informal income, geography, or past exclusion. Monitor who receives adverse treatment and investigate unequal patterns.
Deep dive 3Worked example: Collections and recovery decision support
A score flags a borrower in Ghana as high risk after several late payments, while the case notes mention a recent flood affecting their business. A responsible response is: Pause collection pressure and ask an authorised agent to review hardship options and affordability. Correct: the flag is a prompt for careful review, not an automatic sanction. Use this case to separate what the technology contributes from what people contribute. The team should compare the intended outcome with a baseline where applicable, record important assumptions, and keep a clear route to correct or stop the process.
Deep dive 4Local check for Collections and recovery decision support
Ask whether the examples, data, and assumptions fit your school, company, or community. Generic demos often miss local names, laws, connectivity, and languages.
Deep dive 5Collections is still a people decision
Prioritisation models suggest who to contact first; hardship and fairness rules stay with staff.
One-minute challenge
Connect this lesson to real life
Name one situation where this idea could help, and one thing a person should still check.
Explore a real-world example
Use the arrows to connect the idea to a visible situation.
Photo example
Human-led collections workflow
A model suggests a review order; agents assess evidence and hardship; supervisors audit outcomes and complaints.
Key terms
Tap a term to flip and read the definition.
Optional further learningFree textbooks and trusted online resources
These sources informed the course structure. Use them to revisit a concept or study it in more depth.
Ready check
Tick each idea only when you could explain it without looking back.
Ready for practice? List the evidence an agent needs before agreeing a repayment plan.
Extra context (audience, logistics, curriculum notes)
Built for: Banking and finance practitioners — no coding required.
Formats: Learn scroll · Practice interactions · Quiz · Optional chat lab
Module 8 — Collections and recovery decision support
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