Case Study 4: Automating Consumer Duty Evidence and Management Information
What a smaller financial-services firm could achieve through connected outcome monitoring
The opportunity
Many financial-services firms hold valuable customer-outcome information across several systems.
This may include:
Client review records.
Complaints.
Service-response times.
Fee information.
Vulnerability records.
Cancellation reasons.
Customer surveys.
Communication testing.
Call or meeting notes.
Product and service records.
The challenge is not always a lack of data.
The challenge is bringing the information together consistently enough to understand whether customers are receiving appropriate outcomes.
When information is spread across CRM systems, spreadsheets, complaints logs, finance systems and separate operational records, compliance teams can spend significant amounts of time collecting, reconciling and interpreting data before management can use it.
Automating Consumer Duty evidence and management information could help a smaller financial-services firm reduce reporting effort, improve visibility of customer outcomes and create a more structured approach to identifying potential issues.
What could be improved?
A manual Consumer Duty reporting process may involve:
Compliance requesting information from several departments.
Employees exporting reports from different systems.
Data being supplied in inconsistent formats.
Spreadsheets being reconciled manually.
Review-completion data being checked separately.
Complaint themes being analysed one by one.
Service-response times being collated manually.
Fee and value information being extracted from finance systems.
Vulnerability information being reviewed separately.
Management commentary being written manually.
Actions being recorded in meeting minutes.
Evidence being stored across several folders.
Previous reporting periods being compared manually.
Senior management requesting additional clarification.
Remedial actions being tracked through separate spreadsheets or emails.
Common challenges include:
Reporting being heavily retrospective.
Different teams using different definitions.
Customer-outcome information being held in several places.
Excessive dependence on spreadsheets.
Smaller customer groups being hidden within overall averages.
Complaint themes being difficult to identify quickly.
Limited visibility of emerging issues.
Actions not being centrally tracked.
Difficulty retrieving supporting evidence later.
Compliance teams spending more time compiling than analysing.
Management receiving large reports without clear prioritisation.
Difficulty demonstrating what action was taken in response to identified issues.
Limited ability to monitor whether corrective actions actually improved outcomes.
What could automation look like?
1. Outcome data assessment
Before automating Consumer Duty monitoring, the firm’s existing sources of customer-outcome information could be assessed.
Relevant information could be collected from:
CRM systems.
Complaints records.
Client-review data.
Fee and finance systems.
Customer-support platforms.
Vulnerability records.
Cancellation and refund records.
Client surveys.
Communication-testing results.
Call summaries.
Adviser notes.
Product and service records.
This would help identify which information is already available, where data quality issues exist and which measures can be reliably automated.
2. Outcome-monitoring framework
The firm could first define the questions its management information needs to answer.
These could include:
Are customers receiving the services for which they pay?
Are products and services reaching the intended target market?
Are customers receiving fair value?
Do customers understand important communications?
Can customers access support without unreasonable barriers?
Are vulnerable customers experiencing different outcomes?
Are complaint levels increasing?
Are certain products or services generating repeated issues?
Are cancellations concentrated in a particular customer group?
Are agreed remedial actions improving the identified problem?
The technology could then be designed around these questions rather than simply collecting more data.
3. Central outcome record
A central reporting environment could bring together relevant measures from different systems.
Information could include:
Review completion.
Service delivery.
Fees charged.
Customer-support response times.
Complaint volumes.
Complaint themes.
Complaint resolution times.
Cancellations.
Refunds.
Vulnerability adjustments.
Repeated client contacts.
Communication-related queries.
Customer survey responses.
Remedial actions.
Outstanding management actions.
This could give compliance and management one consistent view of customer outcomes.
4. Data-source mapping
Each measure could be linked to a defined source and owner.
For example:
4.1. Products and services
Potential information could include:
Product records.
Service-delivery data.
Target-market information.
Review completion.
Cancellation reasons.
4.2. Price and value
Potential information could include:
Fees charged.
Service usage.
Ongoing-service delivery.
Refunds.
Customer cancellations.
4.3. Consumer understanding
Potential information could include:
Communication testing.
Client queries.
Errors.
Repeat questions.
Survey responses.
4.4. Consumer support
Potential information could include:
Response times.
Complaint volumes.
Resolution times.
Abandoned contacts.
Repeat contacts.
4.5. Vulnerability
Potential information could include:
Vulnerability indicators.
Support adjustments.
Service outcomes.
Response times.
Complaints.
This could create clearer ownership and accountability for data quality.
5. Automated data collection
Approved connections between systems could collect agreed management information automatically.
This could include:
Reviews completed and overdue.
Services delivered.
Fees charged.
Response times.
Complaint volumes.
Complaint categories.
Complaint resolution times.
Repeated client contacts.
Cancellations.
Refunds.
Vulnerability adjustments.
Missing records.
Customer-group outcomes.
Remedial actions.
Instead of employees manually exporting and combining reports, the information could feed into a repeatable reporting process.
6. AI-assisted qualitative analysis
AI could help identify themes within approved text-based information.
This could include:
Complaint descriptions.
Client survey comments.
Call summaries.
Cancellation explanations.
Adviser notes.
Support records.
Feedback emails.
The system could group similar issues and identify recurring themes.
For example, it might highlight repeated references to:
Confusing communications.
Delays.
Difficulty contacting the firm.
Unexpected fees.
Problems accessing support.
Product misunderstandings.
Repeated administrative errors.
The AI output would remain subject to human validation.
It would support analysis rather than make a conduct or compliance decision.
7. Customer-group comparisons
The dashboard could allow management to compare outcomes across relevant customer groups.
These could include:
Vulnerable and non-vulnerable customers.
Digital and non-digital customers.
New and long-standing clients.
Different service packages.
Different products.
Different age groups.
Different acquisition channels.
Different adviser or service teams.
This could help identify situations where an acceptable overall average hides a weaker outcome for a smaller customer group.
8. Exception alerts
The firm could define thresholds that trigger investigation.
Examples could include:
Review completion falling below target.
Complaint-response times increasing.
Complaint volumes rising.
A particular customer group experiencing higher cancellation levels.
Clients paying for services with no evidence of delivery.
Vulnerable customers waiting longer for support.
Repeated client queries suggesting misunderstanding.
A sudden increase in refunds.
A product generating an unusual level of dissatisfaction.
Remedial actions remaining overdue.
The system could automatically create an investigation task.
It would not make an automated conduct decision.
9. Automated evidence library
Each reporting period could retain a structured record of:
Source data.
Data definitions.
Validation records.
Dashboard outputs.
Management commentary.
Decisions made.
Actions agreed.
Responsible owners.
Target dates.
Completion evidence.
Changes made to products, services or communications.
Follow-up results.
This could help demonstrate not only what the firm observed, but what it did in response.
10. Automated action management
When an issue is identified, the workflow could:
Create an action.
Assign an owner.
Set a target date.
Notify the responsible employee.
Escalate overdue actions.
Record supporting evidence.
Track completion.
Link the action back to the original customer-outcome measure.
This could provide a clearer connection between management information and the action taken.
11. Management report preparation
The system could produce an initial management pack containing:
Executive summary.
Outcome indicators.
Period-on-period changes.
Customer-group comparisons.
Threshold breaches.
Complaint themes.
Completed actions.
Overdue actions.
Areas requiring management judgement.
Compliance and senior management would then validate and approve the final report.
12. Consumer Duty dashboard
Management could view:
Reviews completed and overdue.
Services delivered.
Clients with no recorded service activity.
Average fees by service group.
Complaint volumes.
Complaint uphold rates.
Complaint-resolution times.
Customer-support response times.
Cancellation rates.
Refund levels.
Communication-related queries.
Vulnerable-customer outcomes.
Customer-group comparisons.
Remedial actions outstanding.
Threshold breaches.
Issues requiring investigation.
This could provide a clearer and more current view of customer outcomes across the firm.
What could be achieved?
Assume that a Consumer Duty reporting cycle currently requires:
Compliance manager: 80 hours.
Department heads: 55 hours.
Data and finance employees: 35 hours.
Senior management review: 20 hours.
1. Without a structured automated process
Total manual effort:
80 + 55 + 35 + 20 = 190 hours
2. With a structured automated process
If automated data collection, repeatable reporting and structured action tracking reduce the total employee requirement to approximately:
65 hours
3. Potential annual capacity released
190 hours − 65 hours = 125 hours
At an illustrative blended employment cost of £45 per hour:
125 hours × £45 = £5,625 of direct reporting capacity
This does not necessarily mean £5,625 of direct cash savings.
The value could instead be realised through:
More time for compliance analysis.
Earlier investigation of potential issues.
Reduced spreadsheet management.
Better management decision-making.
More consistent evidence.
Improved action tracking.
More time for customer-outcome testing.
Faster identification of emerging risks.
Improved governance oversight.
Potential operational value
The strongest benefit may extend beyond the direct value of the hours released.
If compliance employees spend less time collecting and reconciling information, that capacity could be redirected towards:
Investigating unusual outcomes.
Reviewing vulnerable-customer experiences.
Testing whether communications are understood.
Challenging product and service performance.
Reviewing fair-value evidence.
Monitoring remedial actions.
Supporting management decisions.
Improving internal processes.
Conducting deeper customer-outcome analysis.
The value of the workflow may therefore come from both greater efficiency and better visibility of potential customer harm.
Before and after
1. Outcome monitoring
Before: Customer-outcome information is spread across CRM records, spreadsheets, complaints logs, fee data and separate operational systems.
After: Relevant outcome data is brought together into a connected monitoring framework.
2. Data collection
Before: Compliance and operational teams manually export and compile information from different systems.
After: Approved system connections automatically collect the required management information.
3. Reporting
Before: Reports are prepared manually and may take several days or weeks to compile.
After: A repeatable reporting workflow produces a structured management pack for review.
4. Complaint analysis
Before: Complaint themes are reviewed manually, one case at a time.
After: AI can help group recurring themes and highlight patterns for human review.
5. Customer-group comparisons
Before: Overall averages may make it difficult to identify weaker outcomes for particular customer groups.
After: Dashboards can compare outcomes across relevant customer segments and highlight differences.
6. Exception identification
Before: Potential issues may only be identified during periodic manual reviews.
After: Defined thresholds can trigger alerts when an outcome measure moves outside an agreed range.
7. Action tracking
Before: Remedial actions may be recorded in meeting notes, emails or separate spreadsheets.
After: Actions can be centrally assigned, monitored and tracked through to completion.
8. Evidence management
Before: Supporting evidence is stored across multiple folders and systems, making it difficult to retrieve.
After: Source data, decisions, actions and completion evidence can be retained within a structured evidence library.
9. Management visibility
Before: Senior management receives periodic reports with limited real-time visibility of emerging issues.
After: A central dashboard provides a clearer and more current view of customer outcomes, exceptions and outstanding actions.
10. Compliance workload
Before: Compliance teams spend significant time compiling and reconciling information.
After: Automation reduces manual reporting effort, allowing more time for analysis, challenge and investigation.
11. Reporting capacity
Before: A reporting cycle could require approximately 190 hours of combined staff effort.
After: Automated collection and repeatable reporting could reduce this to approximately 65 hours.
12. Potential capacity released
Before: High levels of staff time are absorbed by manual data collection and reporting.
After: Approximately 125 hours of annual capacity could potentially be released for higher-value compliance and management activity.
Controls that should remain in place
A controlled Consumer Duty monitoring workflow could include:
Documented definitions for each measure.
Named data owners.
Validation before reporting.
Minimum sample sizes for customer-group comparisons.
Role-based system access.
Restricted access to sensitive customer information.
Human review of AI-generated themes.
Periodic testing of AI classifications.
Retention of source evidence.
Clear separation between data, indicators and management judgement.
Governance approval of reporting thresholds.
Escalation procedures for potential customer harm.
Review of inaccurate or incomplete data.
Audit trails for management actions.
Human approval of final reporting.
Regular review of workflow logic.
Automation should support Consumer Duty monitoring and management information.
It should not replace compliance judgement, management responsibility or required governance oversight.
The potential business impact
Automating Consumer Duty evidence and management information could help a smaller financial-services firm move from manually compiling reports to managing a connected outcomes-monitoring process.
Compliance teams could spend less time exporting data, reconciling spreadsheets and manually identifying themes.
Management could receive clearer information more frequently.
Potential customer issues could be identified earlier.
Actions could be assigned and monitored more consistently.
Supporting evidence could be easier to retrieve.
The result could be:
Reduced reporting effort.
Better visibility of customer outcomes.
Earlier identification of potential issues.
More consistent management information.
Reduced spreadsheet dependence.
Stronger action tracking.
Improved evidence retention.
Better customer-group comparisons.
More time for compliance challenge and analysis.
More responsive management oversight.
A more structured approach to Consumer Duty monitoring.
Is your Consumer Duty reporting still dependent on spreadsheets?
Neuranet helps financial-services firms assess their existing customer-outcome data, define meaningful measures and create automated Consumer Duty monitoring, reporting and evidence workflows around their current systems.reporting workflows.