Customer Service

Vulnerable customer identification and routing

Detect signals of vulnerability in customer conversations — bereavement, illness, financial difficulty, distress — and route the interaction to a trained agent with the appropriate handling process.

Decision supportPattern-matchingCustomer experienceAccuracy

30–60%

increase in vulnerability signals correctly actioned

Opportunity assessment

Business Impact
2

Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.

Feasibility
3

Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.

Data Readiness
3

Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.

Risk Exposure
2

High risk. Significant external exposure or regulatory implications. Requires robust oversight and clear accountability.

Change Complexity
2

High people impact. Daily workflows change materially for a number of people. Training, communication, and active change management needed.

Tooling required

Standard LLMSpecialist AI tool

Things to consider

  • The FCA's Consumer Duty has made identifying and responding to vulnerability an explicit expectation for regulated firms, and the same expectation is increasingly applied by consumer bodies to non-regulated ones (FCA).

  • Bias towards over-detection. A false positive means a customer gets a more careful conversation; a false negative means someone in genuine difficulty is handled by script.

  • Never automate the response itself. The model's only job is to route to a trained human, and the handling process must exist and be resourced before you turn detection on.

  • This is special category data in practice. Document the lawful basis, minimise what is stored, and set a short retention period on the flags.

  • Experiment starter: Run detection in shadow mode on one channel for six weeks and have a trained team review the flagged interactions. Count how many were genuine and, more importantly, how many the live process had missed.

AI Transformation Playbook

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