Accelerator · Billing dispute intelligence

RESOLVE
Billing Dispute Intelligence

One correlated timeline for every disputed charge, top-up and payment — from usage and rating to the bill, the payment channel and any adjustment — so care and billing teams can answer “why was I charged?” in minutes, not after a chain of hand-offs.

See the live dashboard
6
Agents in the squad
8
Correlation keys in the demo data
1
Timeline per disputed charge
Full
Audit trail per adjustment
Dispute timeline
Illustrative case
Charge disputed by subscriber
Data session ratedUsage · OSS/CDR
Charge on the billBilling
Top-up via mobile walletPayment channel
Complaint loggedCare / CRM
Root cause found · adjustment proposed for approval
Example steps for illustration.
Chapter 4The proof Use case 5 of 6 Value-chain stageCustomer care and billing

Turn disputes into loyalty

The pressureBill shock and slow dispute handling are leading churn triggers in a saturated market.

Why it matters to an operator executiveA disputed charge is a moment of truth. One correlated timeline lets care answer “why was I charged?” on the first call — protecting the relationship, controlling credits and fixing the root cause.

Where it sits in the telecom value chain
  1. Network
  2. Product & pricing
  3. Sales & channels
  4. Customer care
  5. Billing, RA & fraud
  6. Wholesale & enterprise
  7. Regulatory

Highlighted: the stages this use case changes. See how our services map to it →

Business lens

From a business outcome to measured value

We start from the KPI an executive owns, not from the technology — then work back to the decisions, data and agents that move it.

Our delivery methodology
  1. 1 · Outcome & KPI First-contact resolution Handling time · FCR · repeat disputes · dispute-driven churn
  2. 2 · Value-chain domain Care & billing Prepaid top-ups and postpaid bills
  3. 3 · Decisions Valid charge? Credit, adjust or explain; which root cause to fix
  4. 4 · Data One timeline Usage and rating, invoice lines, top-ups, payments, adjustments, tickets
  5. 5 · AI & agents Evidence assembled Event correlation and explanations; agents build timelines and draft resolutions
  6. 6 · Governance & adoption Credits in policy Limits by role; named approver above them; audit per adjustment
  7. 7 · Measured value Loyalty protected Faster answers, fewer repeats; baselined in discovery

How we’d deliver it

Indicative durations · sized with you in discovery
  1. 1 2–3 wks
    Discover & value case
    Main deliverable

    Dispute baseline and value case

  2. 2 2–3 wks
    Design
    Main deliverable

    Correlation keys, resolution policy, care journey

  3. 3 30–45 days
    Build & integrate
    Main deliverable

    Timeline and agent squad for one dispute category

  4. 4 2–4 wks
    Deploy & adopt
    Main deliverable

    Care and billing teams on the approval queue

  5. 5 Ongoing
    Run & scale
    Main deliverable

    More categories; root-cause fixes; managed operations

Same five phases on every engagement; the pilot (phase 3) runs on your data with your team. How the methodology works →

The problem

Billing disputes are a data problem before they are a care problem

A disputed charge touches usage records, rating, billing, the payment channel and the care system. Agents in the contact centre rarely see all of it.

Evidence spread across systems

Usage, rating, invoice, top-up and payment events sit in different BSS, OSS and payment platforms with different keys.

Slow, manual investigation

Care escalates to billing, billing asks IT for records; the subscriber waits and the case ages against its SLA.

Root causes repeat

Without linking disputes back to rating rules, bundles or payment failures, the same error produces the next wave of complaints.

Our approach: correlate every lifecycle event on the governed Data Fabric, let agents assemble the timeline and propose a resolution, and keep a named owner approving adjustments.
How it works

Correlate, explain, resolve, learn

RESOLVE links the lifecycle of each charge and payment into one timeline, then routes the case with the evidence attached.

Usage & rating
Bill
Payment / top-up
Complaint
Root cause
Resolution

The agent squad

Six agents do the evidence gathering and drafting; care and billing owners approve credits and adjustments.

Timeline Builder Agent
Event correlator
Correlates usage, billing, payment and care events using account, MSISDN, reference IDs and timestamps to build the full timeline.
Pattern Matcher Agent
Transaction linker
Links related events across systems with fuzzy and compound-key matching when identifiers do not line up.
Root Cause Agent
Analysis engine
Identifies the likely cause — rating rule, bundle configuration, duplicate charge or failed payment — from the evidence.
Resolution Agent
Case manager
Drafts the resolution and any adjustment based on dispute type, history and policy, for a person to approve.
Compliance Agent
Regulatory tracker
Tracks SLAs and complaint-handling obligations and keeps an audit trail for every decision.
Notification Agent
Stakeholder alerter
Keeps the subscriber and internal teams updated through the case, with smart routing.
Screens · live on demo data

Lifecycle correlation dashboard

How many lifecycle events are captured per transaction, which keys link them and which systems they come from — the correlation layer every dispute timeline is built on.

Figures load live from the demo platform. The demo data set models the charge, top-up and payment lifecycle (charge request, rating, settlement, posting, reconciliation and disputes); case-level care records are connected in client deployments.

Lifecycle events captured
—
Events per transaction
—
Correlation keys
—
Source systems
—
Lifecycle events by type
Correlation method
Events by source system
Illustrative demo briefWritten from this page’s synthetic demo data · not client results
What the demo data shows

Every charge-and-payment event is captured, from charge request through rating, settlement, posting and reconciliation. Disputes are raised on a minority of transactions, and fewer than half of those have a recorded response — the gap the customer feels. Correlation needs several keys, none dominant, so single-key look-ups miss evidence.

Where we would act first
  1. Build each timeline on compound keys rather than one reference number.
  2. Prioritise disputes with no response yet, against their SLA.
  3. Cluster root causes by tariff, bundle and payment channel so they are fixed at source.
What a pilot would prove

One dispute category resolved on first contact more often, with every credit inside policy and its evidence attached.

Who uses it

Who uses RESOLVE

RESOLVE gives care, billing and assurance teams the same evidence at the same time.

Head of customer care

Contact centre

  • A full timeline on screen during the call
  • Fewer escalations to billing and IT
  • SLA tracking for every open dispute

Billing operations

Billing team

  • Root cause per disputed charge, with evidence
  • Proposed adjustments ready for approval
  • Recurring errors traced back to rating and bundles

Revenue assurance

Assurance team

  • Disputes linked to leakage and overbilling cases
  • Audit trail for every credit issued
  • Trends by product, channel and cause

Regulatory & compliance

Compliance team

  • Complaint-handling timelines evidenced
  • Consistent decisions under policy
  • Reports ready for regulator requests
Why RESOLVE

One timeline instead of a chain of hand-offs

How correlated dispute intelligence compares with the way many billing disputes are handled today.

CapabilityRESOLVEManual investigationCRM notesEmail chains
Evidence gathering Automatic across systems Record requests to each team Partial, care-side only Forwarded screenshots
Root cause Proposed with evidence Analyst judgement Not captured Not captured
Adjustment control Policy plus named approver Manual approval Free-text notes Unclear ownership
Audit trail Every step logged Partial Partial Scattered
Learning loop Causes fed back to rating and bundles None None None
On the Data Fabric Framework

Platform architecture

RESOLVE reads usage, billing, payment and care data through the Data Fabric and writes decisions back to the care and billing systems. About the framework →

Telecom data sources
CDR / usage
Rating
Billing
Payments & top-ups
CRM / care
Correlation layer
Event linking
Compound keys
Time alignment
Lineage
RESOLVE agents
Timeline
Root cause
Resolution
Compliance
Outcomes
Approval queue
Adjustments
Notifications
Cause analytics
Value

What operators get from correlated disputes

Capabilities, not promises: we baseline your dispute volumes and handling times in discovery before sizing the benefit.

Faster answers

Care sees the whole charge history in one place instead of waiting on billing and IT.

Controlled credits

Adjustments follow policy and a named approver, with the evidence attached.

Fewer repeat disputes

Root causes are traced to rating rules, bundles and payment failures and fixed at source.

Evidence for regulators

Complaint timelines and decisions are logged end to end.

Getting started

From pilot to production

We usually start with one dispute category — for example data-bundle charges or failed top-ups — and the systems it touches.

Step 1

Discovery

Baseline dispute volumes, categories and handling times; map the systems and keys each category touches.

Weeks 1-2
Step 2

Pilot

Correlate lifecycle events for one category, stand up the agent squad and the approval queue for care and billing.

30-45 days
Step 3

Scale & run

Add categories and channels; feed root causes back to rating and product teams; optional managed service.

Ongoing
Chapter 4 · The proof · 5 of 6

Next: make AI production-grade

AI is moving into network, care and fraud decisions — and boards and regulators expect it to be governed.

RESOLVE capability tour
AI
AI Analyst

I'm the DaasLabs AI Analyst for the telecom demo platform. I can help with:

  • Revenue assurance & CDR reconciliation
  • Fraud: SIM swap, SIM box, IRSF and Wangiri
  • Churn, customer and network analytics
  • Executive briefings across the accelerators

Answers are generated from the demo data.