As payment fraud becomes increasingly complex in the United Kingdom, high-quality data helps companies unravel the risks, patterns, and anomalies that make up payment fraud. While collecting the data itself is important, payment fraud analytics assist companies in understanding and deriving valuable insights from the raw data. That’s what fuels the most effective decisions that mitigate risks, safeguard assets, and promote secure growth.
Let’s explore the key components of payment fraud analytics so your company can invest in the right solution for your specific needs.
Understanding Payment Fraud and Risk Measurement in the UK
Payment fraud involves unauthorised transactions or deceptive practices to obtain financial gain. In the UK, fraud remains a significant threat:
- Authorised Push Payment (APP) Fraud: The Best Practice Standards (BPS) system recorded nearly 175,000 cases of Authorised Push Payment (APP) fraud in 2023, amounting to over £300 million.
- Total Fraud Cases: The Office for National Statistics (ONS) reported a 33% rise in fraud cases in 2024, reaching approximately 4.1 million incidents.
An organisation’s ability to measure risk determines its ability to proactively identify and prevent payment fraud before it happens. Misjudging risk can lead to high false positive rates, inconveniencing legitimate customers, or failing to detect fraudulent activities, resulting in financial losses and reputational damage.
When risk is assessed inaccurately, it can lead to high false positive rates, meaning trusted customers face unnecessary friction. This hurts customer retention and stunts business growth. Inaccurate risk identification can also lead to high rates of fraud, leading to financial losses and reputational damage for companies. Advanced fraud analytics with accurate risk measuring dynamically applies the right amount of friction to prevent fraudulent activities while driving secure growth with your trusted customers.
Key Components of Payment Fraud Analytics
1. Data Collection and Aggregation
Payment fraud analytics begin by collecting data from various sources, such as customer transactions and relevant databases. The solution creates a comprehensive dataset using historical and current data, establishing baselines and detecting unusual patterns or outliers.
Once the payment fraud data is collected, data aggregation consolidates the diverse data points into a holistic view of transactions and patterns.
Through effective aggregation, the analytics solution can distill meaningful insights, uncover hidden relationships, and enhance the accuracy of fraud detection algorithms. This process enables a more meaningful examination of the collected data, contributing to a robust and proactive approach to identifying and preventing payment fraud.
2. Machine Learning and Predictive Modelling
Machine learning algorithms analyse historical data patterns related to payment fraud. By learning from extensive datasets, these algorithms can detect subtle fraud indicators that would go unnoticed during manual reviews. Predictive models use this analysis to forecast future fraud risks, empowering organisations to proactively address potential threats before they happen.
The effectiveness of these algorithms and models relies on the diversity and quality of the data inputs. Sift’s Digital Trust & Safety Platform, for example, uses a global data network that ingests more than one trillion events per year. If fraudsters target one Sift customer, the data is instantly integrated into our machine learning models to identify and prevent the same type of attack from happening to another customer.
3. Real-time Monitoring
Real-time monitoring allows for immediate responses to suspicious activity, minimising vulnerability. Beyond monitoring transactions, data from diverse sources, such as merchants and industries, is ingested.
This multi-source approach enhances risk decisions by offering a comprehensive understanding of contextual factors influencing each transaction. Integrating diverse data streams in real time not only boosts the precision of fraud detection but also enables organisations to stay ahead of emerging threats and swiftly address potential risks in a dynamic digital environment.
4. Rules-based Systems
Rules-based systems use predefined criteria to detect potential fraud, operating on established rules crafted from parameters like transaction thresholds, user behaviour, or patterns linked to fraudulent activities. These rules can trigger alerts for large transactions, deviations from regular user behaviour, or recurring patterns suggestive of fraud.
While rules offer a structured fraud detection approach, the best rules-based systems can still use Dynamic Friction to customise the user experience. Dynamic Friction incorporates adaptive security measures based on real-time risk factors, ensuring a responsive and context-aware fraud prevention approach. Balancing a stringent rule set with flexible customisations helps companies promptly identify and mitigate potential fraud risks while ensuring a positive customer experience.
5. Behavioural Analysis
Behavioural analysis, a sophisticated fraud prevention approach, examines user or entity patterns and habits to detect anomalies suggesting potential fraud. Considering factors like transaction history, geographical location, device information, and time of day, this method establishes a baseline of normal behaviour.
The system can then effectively identify deviations, such as atypical transaction frequency or unusual login patterns, going beyond static rules.
Behavioural analysis adapts to evolving user behaviours, providing a dynamic and context-aware strategy for real-time flagging of suspicious activities. This nuanced understanding of user habits enhances fraud detection precision by focusing on behavioural deviations indicating potential fraudulent intent.
The Importance of Payment Fraud Solutions in the UK
Recognising the risk of payment fraud is the first step in protecting your organisation’s financial integrity and reputation. Investing in robust payment fraud analytics enables businesses to stay ahead of emerging threats and proactively prevent fraud.
For instance, Sift’s Payment Protection solution identifies and blocks risky transactions using dynamic friction to apply appropriate security measures. By minimising manual intervention and analysing complex trends in real time, it enhances the customer experience for legitimate users while stopping cybercriminals effectively.
With a data network analysing over one trillion events annually and making five million monthly fraud decisions, Sift offers a comprehensive payment fraud analytics platform essential for safeguarding businesses in the UK.
