Generally, fraud detection is a set of security measures designed to prevent individuals from obtaining funds or property through false pretenses. The crux of the matter is that incidents of fraud is widely pervasive in sectors such as banking and finance, insurance, healthcare and eCommerce because those institutions mainly collect personal information of individuals who conduct financial transactions with them. That said, the increasing adoption of mobile and online payment methods has resulted in a significant rise in fraud incidents across many other industries.
According to recent research by Statista, the global eCommerce losses to online payment fraud reached $41 billion in 2022. Those losses have been estimated to reach $48 billion by the end of 2023. As a result, detecting the incidents of payment fraud and preventing its devastating financial and reputational losses has become a prime concern for businesses and regulatory authorities. In another perspective, the global fraud detection and prevention market was valued at $ 29.8 billion in 2022. This is projected to reach $ 92.3 billion by 2030 at a compound annual growth rate (CAGR) of 16.8%. Clearly, the global fraud detection and prevention market is predicted to grow significantly due to the rise in the demand for machine learning and artificial intelligence to combat the growing phenomenon.
To note, machine learning is a sub-field of artificial intelligence. It is basically understood as the capability of a machine to imitate intelligent human behaviour. This means a computer-programed machine that can recognize a visual scene (pictures of people, time series data from sensors) and understand a text written in natural language or perform an action in the physical world. Machine learning only needs data to work and analyze large quantities of same to produce better outcomes. The function of a machine learning system can be descriptive in which the system uses data to explain what had happened. It is predictive when the system uses data to predict what will happen or prescriptive when the system uses data to make suggestions about what action to take to prevent or detect fraud. Even though there are many ways to verify the authenticity of financial transactions, financial fraud detection through the application of machine learning algorithms is considered fast, cost-effective and productive due to the extensive use cases.
Uses of Machine Learning
Increasingly, due to its ability to adapt to new information, machine learning uses more advanced techniques to analyze vast amounts of data in milliseconds to detect patterns of fraud. The patterns help to identify susceptible behaviour and prevent fraud related to money laundering, insurance claims (to identify false and duplicate claims), electronic payments, bank transactions and tax (evasion) among others. Thus, revenue authorities can use machine learning to identify unusual patterns to enhance tax compliance. This process is described as anomaly detection and relies on patterns to recognize legitimate financial transactions and flag suspicious activities that may indicate fraud. Some of the other visible techniques of machine learning to detect fraud include risk scoring, network or text analysis, identity verification and adaptive learning.
In the case of risk scoring, machine learning models assign risk scores to transactions based on various features such as customers’ transaction amount, location (IP address), frequency, payment methods and past behaviour. A higher risk score indicates a higher likelihood of fraud which then calls for further investigation based on the trends.
Based on the inter-connectedness of systems, fraudulent actors often collaborate with one another and form a seamless network to carry out their activities. This has heightened the need for the deployment of graph analysis technique to uncover those complex networks. Graphic analysis looks at the pattern of relationships between users, accounts or devices and identifies unusual connections. In the same vein, machine learning can use a text analysis technique to analyze unstructured text data such as emails, social media posts or customer reviews to identify patterns or keywords that establish incidence of fraud or scams.
Furthermore, machine learning models can analyze and verify user-provided information or add additional verification mechanisms such as face recognition and biometrics to confirm the true identity of an individual and prevent identity theft. For instance, financial institutions and iGaming companies, casinos and betting platforms need to verify that they are dealing with real users, hence the deployment of machine learning algorithms to allow or block any user with suspicious logins, identity theft or engaging in fraudulent transactions.
It is worth reiterating the fact that machine learning has the ability to learn and adapt to new information. This phenomenon is called adaptive learning which enables machine learning models to be retrained on new data and thereby allow them to detect emerging fraud patterns with cutting-edge updates. Adaptive learning is premised on the fact that fraudulent actors change their modus operandi in perpetuating financial fraud. Hence, it provides a pre-emptive response to block them from unleashing those tactics.
Conclusion
Overall, machine learning is an invaluable tool in the detection and prevention of financial fraud. It can help businesses to provide a more secure platform for their customers to enjoy the full benefits of technological applications and the convenience of digital transactions. Even though an organization can make an investment to build its own machine learning models in-house, it is also worth considering the time, effort and the initial high costs outlays. In that regard, the cost-effective option is to outsource it to a third-party provider usually referred to as managed IT services. Indeed, through well-designed data models and coherent business rules, the application of machine learning in fraud prevention can be a powerful tool for a business to improve its overall security and customer experience.
Bernard is a Chartered Accountant with over 14 years of professional and industry experience in Financial Services Sector and Management Consultancy. He is the Managing Partner of J.S Morlu (Ghana) an international consulting firm providing Accounting, Tax, Auditing, IT Solutions and Business Advisory Services to both private businesses and government.
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