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A collection of over 250 uses for artificial intelligence

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Public service fraud detection

Introduction

In the public service sector, a major challenge that can be identified is that related to fraud triage and identification. This problem is not only financially damaging to the public service organisations but also creates loss of confidence of the public in these organisations. With the help of technological advancements especially the use of machine learning (ML) and artificial intelligence (AI), this issue can be addressed effectively. These technologies have the capability to work through large sets of data and identify trends, which can greatly improve the current methods of detecting fraud and help the public service organizations to save a lot of money and at the same time maintain the efficiency of their operations.

Challenges

There are many barriers that public service organizations encounter in the attempt to integrate ML AI for fraud detection. The first problem is the lack of quality and quantity of data that is used for training the ML algorithms. Poor quality of data, missing or incomplete data as well as the presence of bias in the data can lead to generation of wrong results such as false positives or false negatives thereby wasting resources in fraud detection. Second, the variability and complexity of the fraud schemes that are prevalent in the market present a challenge of developing efficient and flexible ML models that can pick out subtle features of fraud. Third, there is a major concern on the privacy and security of the data since the ML models usually need to be exposed to certain information. Finally, another challenge can be seen in the implementation of ML AI into the current systems, especially in companies that have outdated equipment that might not be prepared for such advancements.

AI Solutions

These challenges can be addressed by AI and ML solutions in the following ways. Supervised learning models can be trained with historical data to recognize known patterns of fraud and supervised learning models can be used to detect new patterns of fraud which might be the new form of fraud. Text data such as emails or customer reviews can be analyzed using natural language processing (NLP) to search for fraud. It is also possible to use AI to perform boring and repetitive tasks thus enabling people to solve difficult issues. In addition, there are techniques that can help to encrypt and anonymise data, which is sensitive but useful for training ML models. Lastly, there are cloud-based AI solutions that can be incorporated with the current systems without necessarily having to change many things about the current infrastructure.

Benefits

There are numerous advantages of applying ML AI in fraud detection especially in the public service sector. These technologies can help to enhance the efficiency of fraud detection thus reducing the time taken and increasing the accuracy of detection which in turn reduces costs. They can also enhance the efficiency of the system through the delegation of mundane tasks thus freeing the workforce to handle more important and strategic problems. In addition, AI can enhance the quality of the customer service by decreasing the number of false positives, which can be stressful and tedious for the customers. Finally, with the improvement in the fraud detection, AI can also assist in enhancing and sustaining the trust of the public in such organizations.

Return on Investment

It can be argued that the ROI of using ML AI in fraud detection can be quite high. A study by McKinsey, stated that companies that leverage AI in its full extent can boost their revenues by 10% through effective fraud detection and management. Also, the financial loss that can be prevented from fraud is not insignificant. For example, the UK’s Department for Work and Pensions mentioned that it saved £1.1 billion in 2017-2018 through the application of data analytics in detecting fraud. Furthermore, the benefits of AI include enhanced efficiency and better customer service which may translate to other financial gains in the process.

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