Azure
Microsoft's cloud computing platform and service, offering a wide range of cloud services, including those for computing, analytics, storage, and networking. Users can choose and scale these services to build, deploy, and manage applications through Microsoft-managed data centres.
Audit and Risk Assurance Committee
Committee responsible for reviewing and recommending disclosures on risk matters in the annual financial statements and in the annual report, also to report on financial risks including fraud and IT risks.
Cloud Solution Provider (CSP)
A company that offers cloud-based infrastructure, platforms, and applications to businesses – for instance Azure services.
Compliance
Adherence to laws, regulations, and guidelines to prevent fraud and ensure ethical conduct.
Data analysis
The process of inspecting, cleansing, transforming, and modelling data to discover useful information and inform conclusions.
Data outliers
Data points that significantly differ from other observations in a dataset and may indicate unusual patterns or anomalies that could suggest fraudulent activity. Identifying these outliers is crucial for effective fraud detection and prevention, as they can highlight irregularities that warrant further investigation.
Data pipelines
The structured flow of data from various sources through a series of processes and transformations until it reaches its final destination for analysis or reporting. This involves steps such as data collection, cleaning, integration, transformation, and loading into data storage systems.
Data Protection Impact Assessment (DPIA)
A process designed to systematically analyse, identify and minimise the data protection risks of a project or plan. It is a key part of accountability obligations under the UK GDPR (General Data Protection Regulation) and helps assess and demonstrate compliance with data protection obligations.
Data scientist
A professional who applies their expertise in data analysis, statistics, and machine learning to combat fraud in the NHS.
Enterprise Fraud Risk Assessment (EFRA)
A comprehensive evaluation process used by organisations to identify, assess, and mitigate risks associated with fraudulent activities. This assessment aims to protect the organisation’s assets, reputation, and financial health by proactively addressing potential fraud risks.
Ethical standards
The moral principles guiding the collection, analysis, and application of data, ensuring that all activities comply with relevant legislation and professional standards.
Financial losses
The monetary loss suffered due to fraudulent activities.
Local Counter Fraud Specialists (LCFS)
An accredited counter fraud professional who delivers both proactive work (e.g., raising fraud awareness, preventing and deterring fraud) and reactive work to hold those who commit fraud to account (e.g. fraud investigations).
Machine learning
A branch of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed.
Memorandum of Understanding (MOU)
Formal agreement between two or more parties, outlining the terms and details of the understanding, including each party's roles and responsibilities. MOUs are typically not legally binding but signal a mutual commitment to working together and provide a framework for future actions.
Microsoft Fabric
A unified data platform designed to streamline and simplify data management, integration, analytics, and collaboration for organisations. Announced by Microsoft in May 2023, Fabric integrates multiple services that Microsoft previously offered in its Azure ecosystem, allowing users to handle the end-to-end lifecycle of their data in a single platform.
NHS data sets
The various collections of health-related information gathered and maintained by the NHS. These data sets are important for managing and improving healthcare services, conducting research, and informing health policy decisions.
Robot Process Automation (RPA)
The use of software robots to automate repetitive and rule-based tasks within business processes. It can streamline operations, improve efficiency, and enhance accuracy in operational tasks related to fraud detection and prevention.
Secure Data Environment
A controlled and secure infrastructure where sensitive data related to fraud investigations and prevention activities is stored, processed, and analysed.
Service Level Agreement (SLA)
A detailed contract that defines the specific services a provider will offer and the standard at which those services will be provided, which is measurable.
Single Network Analysis Platform (SNAP)
Built by the Cabinet Office’s Public Sector Fraud Authority, this platform brings government data together in a single environment and enables public bodies to see how companies and individuals are connected within a network analytics context and use this to surface fraudulent behaviour and illicit activity in any area of government spending.
Static data sets
Fixed or historical data that do not change frequently, such as patient demographics, medical records, and financial data. These data sets provide a stable basis for analysis, helping to identify anomalies and detect patterns of fraud.
Strategic Impact Assessment (SIA)
A process used by organisations to evaluate the potential impacts of strategic decisions or projects on their business objectives and environment. This assessment helps in understanding the long-term effects and guiding better decision-making.
Strategic Intelligence Assessment (SIA)
A comprehensive analysis and evaluation process used by organisations, especially within the realms of security, defence, and business strategy. The purpose of an SIA is to provide decisionmakers with actionable intelligence that informs long-term planning and strategic decisions.
Unified Data Access Layer (UDAL)
A secure system used by NHS England to manage and analyse patient data without revealing personal identities. It integrates various NHS data sources into one platform, ensuring data is accessible, reliable, and useful for improving patient care and operational efficiency. UDAL supports detailed analysis by linking anonymised data sets, helping healthcare providers understand patient interactions and outcomes better while maintaining confidentiality.
Weak match
Where the comparison between different data sources or datasets shows some similarities but not enough to be considered a strong or exact match. Weak matches may indicate potential issues or errors that require further investigation to determine their significance and ensure data integrity.