Better Outcomes by Better Outcomz · Vulnerability
Help specialist teams find customers who may need additional support.
Indicators of potential vulnerability are easy to miss in a single conversation and impossible to find by sampling. Specialist teams spend their time searching for cases rather than reviewing them, and inconsistent handling between advisers goes unseen.
What Better Outcomes does here
Help specialist teams find customers who may need additional support.
Better Outcomes can identify indicators of potential vulnerability across large volumes of interactions and combine those signals with wider customer journey evidence.
This helps specialist teams focus human review on the customers and interactions where additional support may be required.
- 01
Indicators, not diagnoses
Better Outcomes surfaces indicators of potential vulnerability in what the customer said and what happened around it. People decide what they mean and what support is appropriate.
- 02
Journey context
Signals from the interaction are combined with CRM and journey evidence - earlier contacts, recorded adjustments, follow-ups - to show whether the need was recognised and acted on.
- 03
Targeted review population
Specialist teams receive a prioritised set of customers and interactions to review, with the evidence that prompted the referral.
- 04
Handling consistency
Section-level pass rates show where adjustments, referrals and signposting are handled inconsistently between teams and channels.
Walkthrough
One vulnerability journey, step by step.
A debt-advice specialist support team wants to find customers whose circumstances have changed since their plan was set up. Details are illustrative; the screens are real.
Step 1 of 4
Indicators are surfaced across every connected interaction
Better Outcomes evaluates every call, chat and email against the team's vulnerability criteria - disclosure recognised, adjustment considered, signposting offered - and shows the journey stages where handling is weakest across the whole population, not a sample.
- Pass rate by journey stage for vulnerability handling
- Weak stages flagged for specialist attention
Step 2 of 4
Each indicator is shown with its evidence
A specialist opens a flagged interaction. The customer mentioned a recent bereavement while discussing a missed payment; the AI reasoning sets out why this is an indicator of potential vulnerability, beside the verbatim words, with a confidence level. Nothing has been decided about the customer.
- Indicator, reasoning and source evidence side by side
- Specialist confirms, rejects or investigates
Step 3 of 4
The journey shows whether the need was acted on
The CRM record holds no adjustment and the follow-up call a week later did not mention support. Root-cause assessment groups this with similar cases where disclosures were acknowledged but not recorded - a potential process gap rather than a single adviser's error.
- Support agreed versus support delivered
- Potential systemic gap surfaced for review
Step 4 of 4
Specialist time goes to the customers who need it
The team works from a prioritised review population with the evidence attached, records each decision and the support arranged, and tracks over time whether disclosures are being recognised and recorded consistently.
- Targeted review population instead of sampling
- Handling consistency tracked over time
In practice
What this looks like on a real journey.
- Example 01
A disclosure that was never recorded
The customer mentions a bereavement on a call; the CRM shows no adjustment. Better Outcomes surfaces the gap for a specialist to review.
- Example 02
Support agreed but not delivered
Journey evidence shows the adjustment agreed on one call and whether subsequent interactions honoured it.
- Example 03
Referral routes under-used
Missed signposting to specialist or third-party support is identified across the population, not in the handful of calls a sample happened to include.
Illustrative scenarios · Findings are surfaced for human review, not decided automatically
The question this answers
“Was additional support identified and considered?”
What your team gains
- Identify vulnerability indicators at scale
- Surface potentially unrecognised support needs
- Highlight inconsistent vulnerability handling
- Review whether appropriate adjustments were considered
- Identify missed referrals and signposting opportunities
- Give specialist teams a more targeted review population
Real Better Outcomes product screens · Shown with test data
AI assurance and explainability
AI you can evidence, not just trust.
The AI does not simply generate a score. It produces an assessment that a reviewer can check against the evidence used to reach it - and change if the evidence does not support it.
Your data
Customer data is not used to train public or general-purpose AI models. Products are hosted on UK infrastructure with access segregated by organisation, team and user.
Security and governance/ 01
Configurable outcome frameworks
Your outcome definitions, scorecards and rules - applied consistently across every connected interaction.
/ 02
Evidence behind every assessment
Each finding links to the interaction, CRM record and journey events it was drawn from.
/ 03
Explainable reasoning
The criteria applied and the reasoning followed are shown beside the evidence, not hidden in a score.
/ 04
Human-in-the-loop review
Reviewers confirm, reject, escalate or override - with authority to change the outcome and a recorded rationale.
/ 05
Confidence thresholds
Where applicable, low-confidence findings route to human review before any action is taken.
/ 06
Audit history
Findings, decisions, overrides and model versions are written to an append-only audit history.
/ 07
Configurable escalation
Route confirmed findings to remediation, coaching or compliance queues according to your governance model.
/ 08
Role-based access
Named permissions by organisation, team and role, with MFA and lifecycle management.
/ 09
Reporting and governance
Trend, calibration and override reporting for quality, operations, risk and Board oversight.
/ 10
Data controls
UK product hosting, tenant segregation and defined data scope agreed per deployment.
Frequently asked
Questions buyers ask us first.
- Does the AI decide whether a customer is vulnerable?
- No. Better Outcomes surfaces indicators of potential vulnerability in what the customer said and what happened around it. Specialist teams review those indicators and decide what they mean and what support is appropriate. No automated decision is made about any customer.
- What happens when an indicator is surfaced?
- The interaction, the indicator, the supporting evidence and the surrounding journey context are routed to a human reviewer. The reviewer records their decision and any action taken, and that record forms part of the audit history.
- How does this help a small specialist team?
- Instead of searching recordings for cases, the team receives a prioritised review population drawn from every connected interaction, with the evidence attached. Time moves from finding cases to supporting customers.
Insights for Vulnerability teams
Thinking worth taking back to your team.
- Customer Outcomes9 min readVulnerability is not a flagChristian Henson argues that firms should treat vulnerability as changing customer context, using data and AI to prompt human attention without turning support into automated profiling.Read the essay
- Quality & Compliance6 min readStop scoring calls. Start scoring outcomes.Christian Henson explains how Better Outcomes helps organisations move beyond sampled calls to assess customer outcomes across the whole journey.Read the essay
Working session
See Better Outcomes on your customer journey.
Bring one representative customer journey and we'll show how Better Outcomes could assess it across interactions, CRM evidence and customer outcomes. No customer data is required for the first session.