Interviewer view · keep this screen to yourself
AI in a Singapore bank's contact centre
You run the case. Read the prompt, answer questions from the notes below, and share data only when the candidate asks for it or gets stuck. Score at the end.
Case timer
00:00
1. Read the prompt aloud
Read it slowly, then pause. Let the candidate ask questions before they structure.
A Singapore bank has 400 contact-centre agents. Each costs SGD 60,000 a year fully loaded and works about 1,600 productive hours a year. The team has listed three AI use cases (see the exhibit). Assume 80 percent of agents will use whichever tool is launched. Which should the bank start with, and what is it worth?
The prompt refers to Exhibit 1. After reading it, say: "Open Exhibit 1 now."
Format note: Interviewer-led: the interviewer shows the exhibit and asks the questions in order.
2. Answers to clarifying questions
Give these answers only if the candidate asks. If they ask something not listed, give a sensible answer or say it does not matter here.
If asked: Is the goal lower cost or better service?
Answer: Both, but the board wants savings visible within a year.
If asked: What rules apply to AI that uses customer data?
Answer: The bank has an approved setup for internal tools; customer-facing AI needs extra review, human checks, and more time.
If asked: How should value be counted?
Answer: Agent hours freed, at fully loaded cost, and only if the bank can reduce hiring.
3. The hypothesis a strong candidate states
Listen for an early, testable guess like this one. It does not need to match word for word.
Tools that help agents are faster and safer to launch than tools that talk to customers. My hypothesis is that after-call summaries give the best mix of value and feasibility, so they should go first.
4. A model structure
Compare the candidate's structure with this one. A different split can be just as good if it is clean and fits the problem.
- Rank the use cases on value and feasibility
- Cost of an agent hour
- Key: Value of each use case, with adoption
- Feasibility and running cost
- What happens to the freed time
Exhibit 1
The prompt uses this exhibit, so the candidate opens it right after you read the prompt ("Show exhibit 1" on their screen).
| Use case | Agent hours affected a year | Share of time saved (%) | Feasibility (1 low to 5 high) |
|---|---|---|---|
| After-call summaries written by AI | 160,000 | 50 | 5 |
| Answer suggestions during calls | 320,000 | 15 | 3 |
| Chatbot that handles simple requests | 128,000 | 40 | 2 |
So-what
After-call summaries have the highest value and the highest feasibility. The chatbot has similar value to answer suggestions but the lowest feasibility, because it talks to customers directly.
5. The working, step by step
Each step shows how a strong candidate works it out. Share a new fact from it only when the candidate asks or is stuck, and let them do the math: the result in the dark box is what they should reach.
Step 1: Cost of an agent hour
What a strong candidate does: Fully loaded cost divided by productive hours.
Cost per agent hour (SGD): 60,000 ÷ 1,600 = 37.5
Step 2: After-call summaries: hours saved
What a strong candidate does: 160,000 hours of after-call work a year, half of which the tool can save, with 80 percent adoption.
Hours saved a year: 160,000 × 0.5 × 0.8 = 64,000
Step 3: After-call summaries: value
What a strong candidate does: Hours saved times cost per hour.
Value (SGD a year): 64,000 × 37.5 = 2,400,000
Step 4: Answer suggestions: value
What a strong candidate does: 320,000 hours of calls, 15 percent faster, 80 percent adoption.
Value (SGD a year): 320,000 × 0.15 × 0.8 × 37.5 = 1,440,000
Step 5: Customer chatbot: value
What a strong candidate does: 128,000 hours of simple requests, 40 percent handled by the chatbot, 80 percent of the planned volume reached.
Value (SGD a year): 128,000 × 0.4 × 0.8 × 37.5 = 1,536,000
Step 6: Net value of summaries
What a strong candidate does: Interviewer: "Licences and model usage for the summary tool cost about SGD 400,000 a year."
Net value (SGD a year): 2,400,000 - 400,000 = 2,000,000
Step 7: What the freed time means
What a strong candidate does: Convert the hours saved into agents. Interviewer: "The bank loses about 60 agents a year through normal staff turnover." So it can capture the saving by not replacing some leavers, without layoffs.
Agent-equivalents freed: 64,000 ÷ 1,600 = 40
The recommendation to listen for
At the end, say: "The CEO walks in. What is your recommendation?"
Start with AI-written after-call summaries. First, they have the highest value: about SGD 2.4 million a year, or SGD 2 million after running costs. Second, they are the most feasible, because an agent checks every summary before saving it. Third, the freed time, about 40 agents' worth, can be captured by not replacing some of the roughly 60 agents who leave each year, so no layoffs are needed. Plan answer suggestions next, and prepare the chatbot in parallel through the bank's review process.
Risks a strong answer names: Adoption may be below 80 percent if summaries need heavy editing; Savings disappear if the bank replaces every leaver anyway; Summaries with wrong details could create complaints.
Next steps: Pilot summaries with 50 agents for six weeks and measure minutes saved per call and edit rates; Agree with the workforce team how many leavers will not be replaced.
Strong versus weak
A strong answer
Sized each use case with adoption, compared value with feasibility, netted off running cost, and said how freed hours become savings.
A weak answer
Recommended the chatbot because it is the most visible, and counted all freed hours as cash saved.
Score the candidate
Score each criterion from 1 to 5. A 2 or a 4 sits between the descriptions.
Total
0 out of 25
Score all five criteria to see the band and the feedback template.