Artificial Intelligence and Big Data-Driven Audit, Fraud Detection, and Prevention in Environmental Agencies-C
The use of cutting-edge technology to aid professionals in environmental agencies’ audit, fraud detection, and prevention programs is what is known as artificial intelligence (AI).
Artificial Intelligence (AI) and big data are increasingly being utilized in environmental agencies to enhance audit, fraud detection, and prevention programs.
AI enables experts to perform tasks that would typically require human intelligence, such as identifying significant features in large spatial data sets and establishing complex relationships between variables like pollution and public health.
The internet era has brought new challenges to the environmental audit process, with the increasing quantity and variety of data available. Digital environmental audit is characterized by the use of electronic data, a focus on data-intensive fields, and an increased capacity to store, assess, and interpret data for evidence-based conclusions. Big data analytics enable deep and rich data analytics, making it increasingly the area of focus for audit and fraud prevention activities.
The environment encourages the use of big data to promote continuous improvement in audit and fraud detection, focusing resources in targeted areas to reduce harm or losses from fraud. Critics argue that big data may be an expensive initial outlay with no guaranteed cost reduction or efficiencies, and may lead to a focus on a narrow field of study. As our understanding of big data develops, a broader opportunity to promote collaborative interdisciplinary research and application will emerge, ensuring continuous innovation and a multi-faceted aspect to how fraud prevention measures are developed and deployed.
AI can analyze complex data, leading to significant advancements in resource usage and efficiency. It can also enable predictive risk analysis, promoting proactive measures of environmental protection. Autonomous drones equipped with real-time processing and detection software provide instant tangible data to environmental auditors, allowing them to work more efficiently during the audit walk phase. Advancements in image analysis and machine learning allow for the identification of fresh dumping grounds, which could be crucial in the scrutiny placed upon companies and corporations that provide detailed and digitized environmental statement reports.
Similarly, data mining combines artificial intelligence, machine learning, statistics, and database systems to discover patterns in large data sets. By continuously monitoring environmental data, experts can integrate these sources and find essential data more rapidly. Big data-driven fraud detection techniques involve a big data environment that continuously collects and integrates diverse sources of information, using techniques such as pattern detection and anomaly detection, which are also known as profiling and exception analysis.
Data-driven fraud detection methods have revolutionized environmental audit and inspection activities, enabling efficient storage and organization of transactional data. Analytical applications can be used to store and analyze data without the need for additional software, allowing users to perform simple or complex analysis using data mining tools or fraud diagnostic tools. However, the use of IT technology can conflict with privacy concerns, such as the right to confidential communication when investigating electronic data of suspects. However, the ability to collect and visually analyze fraud data with advanced fraud diagnostic tools can significantly enhance fraud and error detection efficiency.
Predictive analytics for early fraud detection is another important aspect of data analysis. With the increasing deployment of sensors and advanced monitoring methodologies in environmental agencies, massive amounts of data are being collected around the clock. Predictive analytics can be utilized to inspect this big data either in real time or on a periodic basis, recognizing the probability of fraudulent activities at the early stage. Common predictive models in fraud detection include decision trees, regression analysis, and neural networks, which can identify patterns in data by analyzing relationships between different factors. By scanning through enormous data sets, predictive analytics can find cases of known fraud and minimize false positives.
More so, fraud and revenue leakage cases are common in local authorities, and they are using advanced technologies to detect and prevent fraud. Real-time monitoring and alert systems, such as the National Benefit Fraud Hotline and online fraud reporting platforms, help public awareness about benefit fraud. Social network analysis on large datasets helps investigators narrow down the most interested individuals and develop investigation focal points effectively. Machine learning models like random forest algorithms have been implemented to increase operational efficiency and generate high accuracy in risk prediction.
Link analysis and social network analysis are used for fraud investigation, identifying anomalies and suspicious activities that might not be apparent in traditional inspection or detective work. By visualizing and interpreting networks, investigators can see the complete picture of how different elements within a network interact with one another, providing a more comprehensive way of understanding certain activities and allowing for the detection of behaviors that would usually remain hidden if only individual elements are examined in isolation.
It should be stated unequivocally that, the application of AI and big data in preventing fraud in environmental agencies requires careful and strategic implementation. Preventative strategies should consider data privacy and security, ensuring that personal data is protected and accurately processed. A top-down strategy is always recommended in implementing AI and big data technologies, with leaders identifying objectives and potential AI solutions first. Compliance with rapidly changing laws and regulations is also crucial, and machine learning (ML) can be used in practice to learn from data.
Therefore, it is my believe that integrating AI and Big Data Analytics into the finance and audit of environmental management can block leakages, prevent fraud and revolutionize altogether in the sector.
The Author is a Chartered Accountant by profession and a Fellow of the Institute of Chartered Accountants Ghana. He is PhD candidate at the Girne American University, he also has an MBA in Finance from the Central University of Ghana and Two (2) Bachelor’s Degrees in Commerce and Law from University of Cape Coast and KNUST respectively.
Complimentary to these are Certifications in Big Data/ Data Science from IBM, Certified Scrum Master from Scrum Alliance, and BlockChain from the Blockchain Council. He works currently as the Chief Compliance Manager at the Forestry Commission of Ghana.
