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CMS AI, Algorithms, Automation & Data

The strongest primary source is DWP’s Algorithmic Transparency Record for CMS Predictive Analytics Compliance.

About this guide

The strongest primary source is DWP’s Algorithmic Transparency Record for CMS Predictive Analytics Compliance.

Confirmed official information

What is publicly confirmed?

The strongest primary source is DWP’s Algorithmic Transparency Record for CMS Predictive Analytics Compliance.

Machine learning

Predicts possible payment breakdown

Historic operational and transactional information is used to predict whether a compliant paying parent may become financially non-compliant.

Official evidence Open DWP algorithm record
Caseworker support

Used for prioritisation

DWP describes the tool as supporting operational prioritisation and proactive action intended to keep cases compliant.

Official evidence Read intended use
Important distinction

Not all automation is AI

HMRC income transfers, annual reviews, online reporting and benefit deductions can involve digital or rules-based automation without being machine learning.

Accuracy safeguard

This page does not claim that every CMS calculation, annual review, collection decision or enforcement action is made by AI. The confirmed machine-learning use described here concerns predictive compliance and operational prioritisation.

Official timeline

How CMS digital processing developed

2012 – The new statutory scheme began Government announced the new Child Maintenance Service and highlighted a direct interface with HMRC and annual reviews as modernising features.
2013 onward – Annual-review guidance DWP published guidance explaining the yearly review of maintenance calculations. The current page states that the amount is reviewed for the following 12 months.
Official source Annual Review guidance
2014 – Information-use factsheet published DWP published a factsheet explaining how CMS collects and uses customer information.
Official source How CMS uses information
August 2020 – Some flat-rate deductions automated The National Audit Office reported that DWP automated flat-rate deductions from Carer’s Allowance and Universal Credit where the paying parent had no earned income. It also reported that other deductions were still processed manually.
Official public-audit source NAO Child Maintenance report
2025 – Predictive compliance record published DWP published an Algorithmic Transparency Record confirming its machine-learning use for predictive compliance.
Official source DWP algorithm record
Predictive process

How the confirmed machine-learning tool works

1

Historic CMS information

Operational and transactional data is prepared for analysis.

2

Pattern analysis

The machine-learning model identifies patterns linked to later non-compliance.

3

Prediction

A compliant case may be assessed as being at risk of future payment breakdown.

4

Prioritisation

The result supports operational prioritisation and possible proactive treatment.

5

Caseworker action

A caseworker remains involved in reviewing the case and deciding what action to take.

Primary evidence for this process

The detailed technical description, purposes, data, operation, responsibility and safeguards should be checked against DWP’s live transparency record because it may be updated.

Read the complete official record
Data transparency

Information used across CMS processes

HMRC gross-income information

GOV.UK says CMS finds the paying parent’s yearly gross income from information supplied by HMRC.

Official evidence How maintenance is worked out

Benefits and circumstances

CMS also checks benefit information and considers circumstances relevant to the statutory calculation.

Official evidence Step-by-step calculation guide

Online information supplied by customers

The online account can be used to report changed circumstances, payment problems, extra payments, expenses, income, fraud or incorrect information.

Official evidence CMS online-account functions

Predictive operational information

DWP’s algorithm record describes the operational and transactional information used by the predictive compliance tool.

Official evidence Read data section
Responsibility

Who runs and oversees the service?

Responsible department

Department for Work and Pensions

CMS is a DWP service. DWP is responsible for the public record describing the predictive compliance tool.

Official evidence CMS service page
Algorithm record

Named teams and suppliers

The current transparency record should be used for the published organisational owner, operational responsibility, supplier involvement and technical arrangements.

Independent audit

National Audit Office scrutiny

The NAO investigated CMS performance, collection, enforcement, automation and service transformation.

Public-audit evidence NAO investigation
Direct evidence links added

AI, ordinary automation and digital services

Every row below now contains a direct official source. On mobile, each row becomes a full-width evidence card rather than a cut-off table.

Process
What official sources say
Technology type
Official evidence
Process HMRC income interface
What official sources say CMS obtains yearly gross-income information from HMRC for the statutory calculation. The 2012 launch announcement also described a direct HMRC interface.
Technology type Government data transfer and rules-based processing. This alone is not evidence of machine learning.
Process Annual reviews
What official sources say CMS reviews the maintenance amount every 12 months and checks the paying parent’s circumstances to decide whether the amount should change.
Technology type Statutory review supported by data and calculation rules. Some steps may be automated, but this is not necessarily AI.
Process Benefit data and deductions
What official sources say DWP uses benefit information in relevant calculations. The NAO reported that some flat-rate deductions were automated in August 2020.
Technology type Data sharing, eligibility rules and payment automation.
Official evidence
Process Online CMS account
What official sources say Customers can report changes, report missing or extra payments, submit relevant income or expenses, view payment plans and report fraud or incorrect information.
Technology type Digital self-service. This is not, by itself, artificial intelligence.
Official evidence Official account functions
Process Predictive compliance
What official sources say Historic operational and transactional information is used to predict whether a compliant paying parent may become financially non-compliant.
Technology type Confirmed machine learning used to support prioritisation and proactive casework.
Official evidence DWP algorithm record
Process Collection and enforcement systems
What official sources say The NAO examined CMS collection, enforcement, manual work, technology limitations and transformation plans.
Technology type A mixture of digital case management, rules, manual work and automated processes. The source does not establish that all enforcement is AI-driven.
Official evidence NAO Child Maintenance report
Accountability

Risks and safeguards that require scrutiny

Incorrect underlying records

Any predictive system depends on the quality of its input data. Incorrect income, payments, communications or case status could potentially affect an output.

Relevant official record Data and risk information

Human reliance on a score

A human being may remain the formal decision-maker while still being influenced by an algorithmic recommendation or prioritisation.

Government guidance UK Government AI Playbook

Fairness and bias

Public transparency should show how error rates and outcomes are monitored across different groups and circumstances.

Official government review Review of algorithmic bias

Public notice and explanation

People should be able to understand what information is processed, why it is used and how to challenge inaccurate personal information.

Official DWP information DWP Personal Information Charter
Customer routes

How to ask for records or challenge a decision

Request your personal information

DWP’s official guidance explains how to make a subject access request for personal information held about you.

DWP subject access request

Challenge a payment-amount decision

GOV.UK says a decision about the payment amount can be challenged by mandatory reconsideration, normally within 30 days of the decision letter.

Complaints and appeals

Contact CMS

Use the current official contact page for telephone, Relay UK, Welsh-language and accessibility options.

Official CMS contact details

Request non-personal transparency information

DWP publishes a dedicated route for Freedom of Information requests. FOI is for recorded public information, not your own personal case records.

Make a DWP FOI request

Focused subject access request wording

Please provide all personal information held about me relating to any predictive analytics, machine-learning, algorithmic scoring, compliance prediction or risk classification applied to my Child Maintenance Service case.

Please include, where held:

  • each score, category, prediction and date generated;
  • the information and explanation shown to caseworkers;
  • any recommended or suggested action;
  • records showing who viewed or acted upon the output;
  • caseworker comments, acceptance, rejection or correction;
  • resulting calls, messages, work items or case actions;
  • audit records relating to the use of the output; and
  • the applicable retention period and privacy information.
Further transparency needed

Questions DWP should answer publicly

When was the first real CMS case scored?

Request the first pilot date, first production date and first date on which an output was shown to a caseworker.

What complete data fields and engineered features are used?

Request a full feature dictionary, including definitions, transformations, exclusions and version history.

How many false positives and false negatives occur?

Request monthly confusion-matrix figures and model performance broken down by model version and relevant equality groups.

How often do caseworkers disagree with the output?

Request figures for accepted, rejected, overridden and corrected predictions, together with resulting actions.

Are affected customers told that a predictive score was used?

Request the exact customer notices, scripts, letters and online privacy wording used for predictive compliance processing.

What contracts and costs support the tool?

Request procurement records, statements of work, supplier costs, hosting costs, assurance work and ongoing support costs.

Evidence library

Primary official sources

DWP CMS Predictive Analytics Compliance Primary official algorithm record for the confirmed machine-learning tool. Open official record
Government algorithmic transparency register Central GOV.UK register for published public-sector algorithm records. Open register
How child maintenance is worked out Current GOV.UK overview of HMRC income use and statutory calculation steps. Open calculation overview
Step-by-step calculation guide Detailed DWP explanation of income and the calculation process. Open detailed guide
National Audit Office investigation Independent public-audit report covering performance, automation and enforcement. Open NAO report
CMS online account Official list of changes, payments and information customers can report online. View account functions
DWP Personal Information Charter How DWP says it processes and protects personal information. Open information charter
DWP Freedom of Information requests Official route for requesting recorded non-personal information. Open FOI guidance
UK Government AI Playbook Government guidance on responsible, safe and accountable AI use. Open AI Playbook

Accuracy and legal notice

This is an independent public-interest information page created for Right First Time UK. It is not operated or endorsed by DWP, CMS, HMRC or the National Audit Office.

Official pages can change. Visitors should use the direct evidence buttons to check the latest wording. Analysis and transparency questions are clearly separated from confirmed official statements.

This page provides general public information and is not legal, financial or data-protection advice.