What are the aims of Project Athena?
Project Athena's aim is to detect large scale NHS fraud quickly using advanced data science techniques. These include machine learning and data analytics. Project Athena attempts to identify fraud at an early stage enabling our teams to identify suitable fraud prevention or enforcement measures.
How does Project Athena use machine learning to detect NHS fraud?
Project Athena uses machine learning to detect fraud by analysing data patterns. Just as machine learning can spot unusual shapes in medical scans, our system flags suspicious activities, like anomalies in NHS payment transactions. Once data is gathered following agreement from partners, it is prepared for analysis. Our data scientists apply machine learning, while analysts interpret the findings. They work closely with our response team to determine the best course of action. This collaboration ensures emerging insights lead to effective counter-fraud strategies.
Why is human expertise still important in fraud detection within Project Athena?
What our system flags as an anomaly might be better understood and explained by a subject matter expert with deeper knowledge. While our data science methods improve detection accuracy and reduce errors, human expertise remains essential.
Are similar advanced data science techniques being used to detect fraud in other sectors?
Banks use techniques like machine learning to identify suspicious transactions, unusual behaviours or fraudulent claims. Other sectors, such as insurance and retail also use data analytics to spot fraud, including fake claims or fraudulent transactions, to reduce losses and improve prevention.
How is Project Athena different?
What sets Project Athena apart is that we are applying proven methods for the healthcare sector. We tackle the complexities of large-scale healthcare data, aiming to protect NHS resources and detect fraud early and efficiently.
How does Project Athena identify and investigate potential fraud?
Our data science team flags anomalies in the data, which may indicate potential fraud. However, not all anomalies point to fraud—some could result from poor policy or practice rather than malicious intent. Once potential loopholes are identified, it’s crucial to understand how they’re being exploited. This is done through further analysis and investigation, with collaboration between our response team, data scientists, and fraud specialists. Teams work with policy leads to close these gaps and prevent future misuse, ensuring a proactive approach to fraud prevention.
Why is the use of data so important in Project Athena?
Data tells a story that can guide us to where fraud might be found, enabling us to lead a proactive counter-fraud agenda and ensuring that money meant for patient care is used appropriately.
How do I find out more?
Follow us on LinkedIn, Facebook and Twitter where you can find out about developments of Project Athena.