Tuesday, February 17, 2026
Essential complaint KPIs that regulated firms should track and evidence
Setting out KPIs that give good outcomes in regulated complaint handling
Complaint managers in financial services hold one of the most scrutinised roles in the industry. The FCA has strengthened expectations under DISP, Consumer Duty has raised the bar for fair outcomes, and the Financial Ombudsman Service (Ombudsman)continues to highlight where firms fall short. Meanwhile, customers expect faster responses, clearer communication, and fair resolutions.
For senior leaders, complaint KPIs are more than operational statistics. They’re also indicators of conduct risk, customer outcomes, and whether the firm can evidence control when reviewed.
You already know this reality. Your team sits at the intersection of customer experience and regulatory exposure. Yet many firms still face the same problem. They have data, but it’s often inconsistent and takes so long to collate that there’s no time left to analyse it properly before the next report is due.
With the FCA increasingly focusing on data-driven oversight, firms cannot rely on KPIs that only tell part of the story.
Regulators consistently emphasise that complaint data must be accurate, complete, and capable of evidencing fair outcomes, not just recorded for reporting purposes.
This guide outlines essential KPIs in regulated industries, particularly those supervised by the FCA. It focuses specifically on measurable indicators used for oversight, reporting, and regulatory assurance. For behavioural and process frameworks used by regulated complaint case handlers, see our 5 Cs complaint handling framework.
Why KPIs are essential, not just a buzzword
When the purpose of KPIs is unclear, they can feel like a tick box exercise or a tool for micromanagement. Used properly, they are measurable indicators that show whether complaint handling is meeting regulatory expectations, delivering fair customer outcomes, and supporting the firm’s strategic objectives.
In regulated environments, they also act as early warning signals, highlighting delays, inconsistencies, or emerging risks before they escalate into customer harm or supervisory concern. Effective KPIs must be defined at leadership level, clearly explained to teams, and reviewed regularly to ensure they remain relevant, actionable, and aligned to both regulatory obligations and operational performance.
Operational metrics show how busy your team is. Regulatory assurance metrics show whether customers are being treated fairly and risks are under control.
What’s the importance of KPIs in regulated complaint handling?
In regulated complaint handling, KPIs do more than measure performance. They provide evidence. They show regulators that decisions are consistent, customers are treated fairly, and risks are identified early. Strong KPI oversight turns complaint data into assurance, helping firms demonstrate compliance rather than simply claiming it.
Why KPIs matter more than ever under FCA supervision
The FCA not only expects firms to understand their complaint data and act on it. Supervisors seldom focus on isolated metrics. They assess patterns, trends, and whether firms act on what their data shows. This includes identifying patterns of harm, evidencing fair outcomes for all consumers, addressing root causes, and ensuring customers receive timely support.
KPIs shouldn’t be treated as another set of operational tick boxes: if they don’t add value, they’re just noise and a waste of time.
KPIs are regulatory signals. They confirm whether customers are treated fairly, whether complaints are investigated competently, and whether risk is under control.
The most valuable complaint KPIs are predictive indicators because they reveal emerging conduct, process, or communication risks before they surface in audits, complaints data returns, or supervisory reviews.
Board-level insight: What complaint KPIs tell leadership
Complaint KPIs are conduct indicators, not operational statistics
Trends matter more than single data points because they reveal emerging risk patterns
If reliable complaint data cannot be produced quickly, regulators and supervisory teams often interpret this as a control weakness
The KPI categories regulators expect firms to evidence
Regulators typically assess these indicators together, not in isolation, to understand whether the firm’s complaint handling is controlled, consistent, and fair.
Timeliness indicators regulators review first
Timeliness is usually assessed first because delays are one of the clearest early indicators of customer harm and operational control weakness. Persistent delay trends often indicate upstream process failures rather than individual handler performance.
Time to acknowledge
Measures
Time taken to confirm receipt of a complaint
Why it matters
Acknowledgement sets the tone
Slow responses increase anxiety and follow-ups
Regulators expect prompt engagement
What good looks like
Same day or next day acknowledgement
Time to resolution
Measures
Days from complaint receipt to final response
Why it matters
Delays are a major escalation trigger
Eight weeks is a maximum, not a target
Example: if average resolution time rises from 12 days to 28 days across reporting periods, supervisors typically expect firms to explain why and what action is underway
What good looks like
Average resolution time comfortably below regulatory limits
For insight into operational causes of delay, see: The silent tax: Productivity loss in complaint handling
Ageing and work in progress
Measures
Open cases segmented by age
Why it matters
Older cases carry a higher regulatory risk
Frustration increases with time, raising escalation and reputation risk
Backlogs often indicate operational strain
What good looks like
Cases progress steadily with regular customer updates
Outcome quality indicators used to assess fairness
DISP requires fair, consistent, competent assessments. Consumer Duty adds expectations that firms monitor and evidence good outcomes.
Uphold rate
Measures
Proportion of complaints upheld vs rejected
Why it matters
High rates may indicate service issues
Low rates may indicate defensive decisions
What good looks like
Balanced results with clear reasoning
Regulators assess trends, not snapshots
Repeat complaint rate
Measures
Customers returning with related issues
Why it matters
Indicates root causes remain unresolved
What good looks like
Declining trend supported by corrective action
Vulnerability-related KPIs
Measures
Complaints involving vulnerable customers
Whether enhanced support steps were followed
Outcome differences between customer groups
Why it matters
Regulators expect firms to provide proportional support to vulnerable customers. Firms must evidence that outcomes are consistent across all groups
What good looks like
Clear identification of vulnerability flags
Documented adjustments and tailored communication
No statistically significant adverse outcome bias
Evidence that vulnerability is considered in decision rationale
Supporting guidance: A practical guide for complaint case handlers: dealing with vulnerable customers
Root cause category trends
Measures
Patterns in underlying drivers
Trends in root cause categories
Repeat themes by product or process
Why it matters
Weak root cause analysis is a common supervisory finding. Without structured categorisation, trends remain invisible
What good looks like
Consistent categorisation
Trend visibility by product, channel and timeframe
Documented remediation linked to root causes
Measurable reduction in repeat themes
Strengthen RCA methods here:
Why did this complaint occur? Using the Fishbone Diagram and 5 Whys to strengthen complaint handling
Quality assurance pass rate
Measures
Percentage of cases meeting investigation standards
Decision rationale quality
Evidence completeness
Why it matters
QA identifies weaknesses before they escalate into Ombudsman referrals or regulatory investigations. It’s a key control for consistent, fair outcomes
What good looks like
High pass rates supported by structured QA frameworks
Clear rationale for decision and evidence completeness
No statistically significant adverse outcome bias
Actionable feedback loops into training and process improvement
Documented improvement over time
Clear link between QA findings and root cause prevention
Regulatory risk indicators and escalation signals
Ombudsman referral rate
Measures
Percentage of complaints escalated externally
Referral trend by product or issue type
Why it matters
A rising referral rate may indicate widespread dissatisfaction, poor communication, or investigation weaknesses
What good looks like
Declining referral trends
Clear escalation analysis informing training and process updates
Low referral rates relative to complaint volume
Uphold rates by the Ombudsman
Measures
Percentage of decisions overturned
Uphold rates compared to industry benchmarks
Why it matters
High overturn rates may indicate investigation gaps, poor evidence, or inconsistent interpretation of fairness
What good looks like
Sector-aligned or lower-than-average uphold rates
Structured post-Ombudsman review processes
Decision learning embedded into QA and training
Systemic issue detection rate
Measures
Number of systemic issues identified
Time to remediation
Recurrence after remediation
Why it matters
Missed patterns can create widespread harm. Repeated issues appearing across products or customer groups are treated as a high regulatory priority
Example: when similar complaints arise and are categorised differently, firms can miss emerging systemic risks until regulators identify them first
What good looks like
Proactive detection before external escalation
Cross-product visibility of recurring themes
Timely remediation with documented impact
Measurable drop in recurrence rates after remediation
Customer outcome indicators and satisfaction signals
Customer satisfaction after resolution
Measures
Feedback following case closure
Complaint journey satisfaction
Positive feedback post-resolution
Why it matters
Communication quality influences escalation risk as much as decision accuracy
What good looks like
Clear explanation letters with minimal re-contact
Positive post-resolution sentiment
Correlation between communication clarity and lower escalation rates
Supporting article: Why apologising to customers matters in complaint handling
Outcome testing
Measures
Structured sampling of decisions
Cross-team consistency reviews
Fairness checks across customer segments
Why it matters
Outcome testing helps to evidence Consumer Duty compliance and decision consistency
What good looks like
Formalised outcome testing schedule
Independent review of high-risk decisions
Consistent application of policy across teams
Evidence trails linking decisions to regulatory principles
Documented improvement over time
Operational control and capacity indicators
Case handler capacity and workload
Measures
Distribution of complaints across team members
Average caseload and complexity per handler
Average case age per handler
Why it matters
Overloaded teams make reactive decisions. Capacity imbalance drives delay and inconsistency
What good looks like
Even distribution of caseload according to complexity and experience
Early warning indicators for backlog buildup
Capacity planning based on trend forecasting
Case touches or handovers
Measures
Number of ownership transfers
Average case age at handover
Internal escalations per case
Why it matters
More handovers increase delay, confusion, and risk
What good looks like
Clear ownership from start to finish
Timely, well-reasoned decisions
Good communication with customers while decisions are being made
Transparent and structured processes that reduce rework and delay
How supervisors interpret complaint KPIs
Supervisors seldom focus on a single data point. They look for patterns, signals, and behaviour over time. Complaint metrics are interpreted as indicators of governance, control, and decision quality.
From a supervisory perspective:
Trends matter more than individual data points
A single spike may be noise. Sustained movement signals control weakness or systemic risk.
Delays often signal emerging operational strain or risk
Rising average resolution times can indicate workload pressure, investigation gaps, or poor ownership clarity.
Inconsistent categorisation suggests unreliable management information (MI)
If similar complaints are categorised differently, root cause analysis becomes unreliable and systemic risk may be missed.
Stable metrics with no variation may indicate incomplete data or weak oversight
Perfectly flat trends can suggest under-reporting, inconsistent capture, or lack of challenge.
Evidence of action is as important as the data itself
Supervisors expect to see documented remediation, governance oversight, and measurable impact. Data without response raises questions.
Complaint KPIs are not performance theatre. They are used as indicators of governance maturity, customer outcome oversight, and regulatory culture.
Bringing it all together
Strong complaint handling is not defined by how many KPIs a firm tracks, but by whether those metrics provide clear, timely insight into customer outcomes, operational risk, and decision quality. When KPI data is delayed, fragmented, or unreliable, firms lose the ability to detect problems early or evidence effective control when reviewed.
Many teams understand which metrics matter yet still struggle to produce them quickly, accurately, or consistently. In supervisory reviews, difficulty producing reliable complaint data is often interpreted as a control weakness rather than a reporting problem. This gap between knowing and evidencing is where reporting pressure, supervisory concern, and operational strain tend to emerge.
In many firms, KPI reporting still depends on spreadsheets, manual data gathering, or disconnected systems. That makes reporting slow, increases the risk of error, and limits visibility when leadership or regulators ask questions. Structured workflows, consistent categorisation, and centralised complaint data remove that friction. They make reporting faster, more reliable, and easier to interpret, while giving decision makers confidence that outcomes are being monitored, understood, and improved.
If you are reviewing how your firm tracks and evidences complaint performance or evaluating complaint management software, these practical guides explain what strong oversight looks like and how to achieve it:
FAQ: Complaint KPI reporting
What KPIs does the FCA expect firms to track?
The FCA does not prescribe a fixed list, but it expects firms to monitor metrics that demonstrate complaints are handled promptly, fairly, and consistently, and that risks to customers are identified early. In practice, firms should track indicators covering timeliness, decision quality, root causes, vulnerability outcomes, escalation rates, and systemic trends, and be able to show how those metrics are reviewed, interpreted, and acted upon.
What is a good complaint resolution time?
There is no single correct timeframe. Regulators expect complaints to be resolved as quickly as possible, not merely within deadlines. Strong performance is shown by stable or improving median resolution times supported by evidence that complex cases are prioritised and customers kept informed.
How do you measure complaint quality?
Complaint quality is assessed by reviewing whether investigations are fair, evidence-based, consistent, and clearly explained. This is typically measured through structured QA reviews, consistency checks, rationale testing, and outcome sampling.
How to find the percentage of complaints upheld in favour of the consumer by the FOS?
The FOS publishes complaint data, including sector specific data, once a quarter, and can be accessed through the published Financial Ombudsman data. Firms can use this to measure quality of their complaint handling processes. A consistently higher-than-average rate can indicate potential weaknesses in investigation quality or decision-reasoning, while unusually low rates may also raise questions if outcomes appear defensive or inconsistent.