Using Machine Learning to Detect Fraud
Machine Learning is a form of artificial intelligence that uses algorithms to iterate and adapt. The machine learning program is given a set of parameters to operate in, where adjustments can be made. With each iteration, the program becomes closer to its programmed goal. Over time, the machine “learns” by adapting to its environment and modifying behavior based on past iterations. Machine Learning (ML) is used in a wide variety of fields, including medicine, logistics and manufacturing. Machine Learning excels at pattern recognition. Once a particular pattern or set of patterns are learned, the program can execute its tasks with extreme precision. Machine Learning and Fintech Machine Learning is also used widely in the world of finance. Fintech offers an edge in applying this technology because fintech companies are more technologically integrated when compared to more traditional financial institutions. In addition, most fintech companies are smaller and can pivot much faster in order to implement the latest strategies in an ever changing landscape. The fintech world used Machine Learning in a novel way: to detect fraudulent transactions within a given financial system or network. Fraud is a persistent problem in the financial world. Fraud can encompass a broad range of negative behaviors. But it essentially describes bad actors that take advantage of a financial network to benefit through illicit means. The most common incidence of fraud is money laundering. Fraud poses such a risk because it compromises the integrity of financial networks and the financial entities that operate within those networks. Fraud prevention and detection are crucial to continuously maintain the integrity of financial networks. Not all bad actors can be stopped from abusing the financial system. But new tools and protections can be used to combat fraud and make it more difficult for fraud to occur. How Does Fraud Detection Work? In order to better understand fraud detection, the nature of fraud itself must be considered. The main question is, why would someone engage in illegal financial activity? There is an explanatory framework called the “Fraud Triangle” –coined by Steve Albrecht– that explains the conditions in which fraud can occur in an organization. There are three main elements: motivation, opportunity, and rationalization. Overall, fraud occurs when there is sufficient motivation, when there is an opportunity to bend the rules, and when there is a lack of accountability, leading to rationalization among bad actors to justify their actions. Fraud detection helps to combat these conditions by preventing opportunities for fraud to occur. If fraudulent transactions can be quickly detected, then the fraud can be quickly stopped before major financial damage has occurred. Another point to consider is the concept of “red flag behaviors”. This term explains behaviors that are considered outside social norms. For example, a high volume of small transactions over a short period of time would be considered a red flag behavior because the number of transactions is outside of the norm and could indicate fraudulent behavior. Machine Learning helps to detect fraud by using pattern recognition to identify anomalous transactions. When such transactions occur, they are flagged for review by a human moderator or compliance officer. Machine Learning programs are adept at spotting small discrepancies in a vast amount of transactional data. In certain aspects, machine learning can outperform a human auditor. The algorithm raises red flags autonomously, identifying financial behavior that is outside of the norm. Benefits of Machine Learning The benefits of using Machine Learning to detect fraud are immense. Machine Learning can help to enable real-time fraud detection that operates 24 hours a day. It can also be used to catch mistakes or patterns that human staff may miss. The tool can also enable individual users to detect fraud if they are providing services through a third party, or running a business that handles financial transactions. Timely fraud detection can allow proper action to be taken in order to halt bad actors. Machine learning can also extend the reach of auditors and others who seek to detect fraud. The automation of critical financial processes allows for a greater degree of freedom and accountability. With Machine Learning working around the clock to keep you safe, the chances of fraud occurring reduce dramatically. Join the conversation in the Rapyd Developer Community and learn more about how Rapyd is incorporating machine learning into our organization.
Machine Learning is a form of artificial intelligence that uses algorithms to iterate and adapt. The machine learning program is given a set of parameters to operate in, where adjustments can be made. With each iteration, the program becomes closer to its programmed goal. Over time, the machine “learns” by adapting to its environment and modifying behavior based on past iterations.
Machine Learning (ML) is used in a wide variety of fields, including medicine, logistics and manufacturing. Machine Learning excels at pattern recognition. Once a particular pattern or set of patterns are learned, the program can execute its tasks with extreme precision.
Machine Learning and Fintech
Machine Learning is also used widely in the world of finance. Fintech offers an edge in applying this technology because fintech companies are more technologically integrated when compared to more traditional financial institutions. In addition, most fintech companies are smaller and can pivot much faster in order to implement the latest strategies in an ever changing landscape.
The fintech world used Machine Learning in a novel way: to detect fraudulent transactions within a given financial system or network. Fraud is a persistent problem in the financial world. Fraud can encompass a broad range of negative behaviors. But it essentially describes bad actors that take advantage of a financial network to benefit through illicit means. The most common incidence of fraud is money laundering.
Fraud poses such a risk because it compromises the integrity of financial networks and the financial entities that operate within those networks. Fraud prevention and detection are crucial to continuously maintain the integrity of financial networks. Not all bad actors can be stopped from abusing the financial system. But new tools and protections can be used to combat fraud and make it more difficult for fraud to occur.
How Does Fraud Detection Work?
In order to better understand fraud detection, the nature of fraud itself must be considered. The main question is, why would someone engage in illegal financial activity? There is an explanatory framework called the “Fraud Triangle” –coined by Steve Albrecht– that explains the conditions in which fraud can occur in an organization. There are three main elements: motivation, opportunity, and rationalization.
Overall, fraud occurs when there is sufficient motivation, when there is an opportunity to bend the rules, and when there is a lack of accountability, leading to rationalization among bad actors to justify their actions.
Fraud detection helps to combat these conditions by preventing opportunities for fraud to occur. If fraudulent transactions can be quickly detected, then the fraud can be quickly stopped before major financial damage has occurred.
Another point to consider is the concept of “red flag behaviors”. This term explains behaviors that are considered outside social norms. For example, a high volume of small transactions over a short period of time would be considered a red flag behavior because the number of transactions is outside of the norm and could indicate fraudulent behavior.
Machine Learning helps to detect fraud by using pattern recognition to identify anomalous transactions. When such transactions occur, they are flagged for review by a human moderator or compliance officer.
Machine Learning programs are adept at spotting small discrepancies in a vast amount of transactional data. In certain aspects, machine learning can outperform a human auditor. The algorithm raises red flags autonomously, identifying financial behavior that is outside of the norm.
Benefits of Machine Learning
The benefits of using Machine Learning to detect fraud are immense. Machine Learning can help to enable real-time fraud detection that operates 24 hours a day. It can also be used to catch mistakes or patterns that human staff may miss.
The tool can also enable individual users to detect fraud if they are providing services through a third party, or running a business that handles financial transactions. Timely fraud detection can allow proper action to be taken in order to halt bad actors. Machine learning can also extend the reach of auditors and others who seek to detect fraud.
The automation of critical financial processes allows for a greater degree of freedom and accountability. With Machine Learning working around the clock to keep you safe, the chances of fraud occurring reduce dramatically.
Join the conversation in the Rapyd Developer Community and learn more about how Rapyd is incorporating machine learning into our organization.
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