Artificial intelligence in fraud detection refers to the application of Artificial intelligence technologies, including Machine learning, Deep learning, and expert systems, to identify and prevent fraudulent activities in financial and business operations. It is widely used in the financial sector, especially by accounting firms, to help detect fraud and enhance the accuracy of audits. The shift from in-person work to remote work has increased access to data, raising the need for automated systems that can analyze large volumes of transactions and flag suspicious patterns.
In 2022, PricewaterhouseCoopers reported that fraud has impacted 46% of all businesses worldwide. According to a 2022 Federal Trade Commission study, customers reported fraud of approximately $5.8 billion in 2021, an increase of 70% from the previous year, with imposter scams and online shopping frauds being the majority. Artificial intelligence plays a crucial role in developing advanced algorithms and machine learning models that enhance fraud detection systems, enabling businesses to stay ahead of evolving fraudulent tactics in an increasingly digital landscape.
Expert Systems
Expert systems were first designed in the 1970s as an expansion into artificial intelligence technologies. Their design is based on the premise of decreasing potential user error in decision-making and emulating mental reasoning used by experts in a particular field. They differentiate themselves from traditional linear reasoning models by separating identified points in data and processing them individually at the same time. However, these systems do not rely purely on machine-learned intelligence.
Information regarding rules, practices, and procedures in the form of "if-then" statements are implemented into the programming of the system. Users interact with the system by feeding information either through direct entry or import of external data. An inference system compares the information provided by the user with corresponding rules that are believed to specifically apply to the situation. Using this information and the corresponding rules, the system creates a solution to the user's query. Expert systems generally do not operate properly when common procedures for a specified situation are ambiguous due to the need for well-defined rules.
Implementation of expert systems in accounting procedures is feasible in areas where professional judgment is required. Situations where expert systems are applicable include investigations into transactions that involve potential fraudulent entries, instances of going concern, and the evaluation of risk in the planning stages of an audit.
Continuous Auditing
Continuous auditing is a set of processes that assess various aspects of information gathered in an audit to classify areas of risk and potential weaknesses in financial internal controls at a more frequent rate than traditional methods. Instead of analyzing recorded transactions and journal entries periodically, continuous auditing focuses on interpreting the character of these actions more frequently. The frequency of these processes and the highlighting of areas of importance is up to the discretion of the implementer, who commonly makes such decisions based on the level of risk in the accounts being evaluated and the goals of implementing the system. Performance of these processes can occur as frequently as being nearly instantaneous with an entry being posted.
The processes involved with analyzing financial data in continuous auditing can include the creation of spreadsheets to allow for interactive information gathering, calculation of financial ratios for comparison with previously created models, and detection of errors in entered figures. A primary goal of this practice is to allow for quicker and easier detection of instances of faulty controls, errors, and instances of fraud.
Machine Learning and Deep Learning
The ability of Machine learning and Deep learning to swiftly and effectively sort through vast volumes of data in the forms of various documents relevant to companies and documents being audited makes them applicable to the domains of audit and fraud detection. Examples of this include recognizing key language in contracts, identifying levels of risk of fraud in transactions, and assessing journal entries for misstatement.
Machine learning models, such as Neural networks, are trained on historical data to identify patterns indicative of fraud. Deep learning, a subset of machine learning, uses multi-layered neural networks to automatically extract features from raw data, improving detection accuracy. These technologies enable systems to adapt to new fraudulent tactics over time, as they can be retrained with updated data.
Applications in Big 4 Accounting Firms
Deloitte created an AI-enabled document-reviewing system in 2014. The system automates the method of reviewing and extracting relevant information from different business documents. Deloitte claims that this innovation has reduced time spent going through lawful contract documents, invoices, financial statements, and board minutes by up to 50%. Working with IBM's Watson, Deloitte is developing cognitive-technology-enhanced commerce arrangements for its clients. LeasePoint is fueled by IBM TRIRIGA (which evolved into IBM Maximo Real Estate and Facilities) and uses Deloitte's industrial information to create an end-to-end leasing portfolio. Automated Cognitive Resource Assessment employs IBM's Maximo innovation to improve the proficiency of asset inspection.
Ernst and Young (EY) connected AI to the investigation of lease contracts. EY (Australia) has also received AI-enabled auditing technology. Collaborating with H2O.ai, PwC developed an AI-enabled framework (GL.ai) capable of analyzing reports and preparing reports. PwC claims to have made a significant investment in natural language processing (NLP), an AI-enabled innovation to process unstructured information efficiently. KPMG built a portfolio of AI instruments, called KPMG Ignite, to upgrade trade decisions and forms. Working with Microsoft and IBM Watson, KPMG is creating instruments to integrate AI, data analytics, cognitive technologies, and robotic process automation (RPA).
Advantages: Efficiency
The process of auditing an entity to detect fraudulent activity requires repeating investigatory processes until an error or misstatement is identified. Under traditional methods, these processes are carried out by a human being. Proponents of artificial intelligence in fraud detection state that these traditional methods are inefficient and can be more quickly accomplished with the aid of an intelligent computing system. A survey of 400 chief executive officers created by KPMG in 2016 found that approximately 58% believed that artificial intelligence would play a key role in making audits more efficient in the future.
AI systems can process large datasets in a fraction of the time required by humans, reducing the time and cost of audits. For example, Large language models can analyze unstructured text in contracts and emails to identify potential fraud indicators, while automated tools can flag anomalies in real-time.
Advantages: Data Interpretation
Higher levels of fraud detection entail the use of professional judgment to interpret data. Supporters of artificial intelligence in financial audits claim that increased risks from instances of higher data interpretation can be minimized through such technologies. One necessary element of an audit of financial statements that requires professional judgment is the implementation of thresholds for materiality. Materiality entails the distinction between errors and transactions in financial statements that would impact decisions made by users of those financial statements.
AI can assist in setting materiality thresholds by analyzing historical data and industry benchmarks, reducing the risk of human bias. Additionally, machine learning models can identify subtle patterns in data that might be overlooked by human auditors, improving the overall effectiveness of fraud detection.
Challenges and Limitations
Despite the advantages, the use of artificial intelligence in fraud detection faces several challenges. Expert systems require well-defined rules, making them less effective in ambiguous situations. Machine learning models can be susceptible to bias if training data is not representative, and they may produce false positives or negatives. Additionally, the complexity of deep learning models can make them difficult to interpret, which is problematic in audit contexts where explanations are required.
Data privacy and security are also concerns, as AI systems often require access to sensitive financial information. Regulatory frameworks may not keep pace with technological advancements, creating uncertainty for organizations adopting these tools. As of the early 2020s, many firms are still in the early stages of integrating AI into their audit processes, and the full impact on fraud detection is yet to be realized.
Future Directions
The future of artificial intelligence in fraud detection is likely to involve greater integration of Generative AI and Transformer (architecture)-based models, which can generate synthetic data for training and improve anomaly detection. Collaboration between technology companies and accounting firms is expected to grow, with cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud offering scalable AI services. Research institutions such as MIT CSAIL and Stanford AI Lab are exploring new algorithms for fraud detection, while industry leaders like OpenAI and Google DeepMind are advancing the capabilities of AI systems.
As AI becomes more sophisticated, it will likely play an even larger role in detecting and preventing fraud, helping businesses protect themselves in an increasingly digital economy.