Artificial intelligence and machine learning – terms previously reserved for the tech-savvy – have entered the mainstream. While they may sound futuristic and complex, many are surprised to find machine learning is already being applied to the fraud prevention and detection arena.
From phishing, to credit card fraud detection and data analysis, artificial intelligence has the potential to save billions lost to fraud each year through faster analysis, quick detection, and the limiting of false positives. Fraud schemes are getting more nuanced and complex every day and older prevention and detection methods can struggle to keep up. Artificial intelligence platforms are the new answer to this problem.
Artificial Intelligence and Machine Learning
Put simply, artificial intelligence (AI) is a program that simulates human intelligence. Thanks to Hollywood, the term typically conjures up images of robots that look like humans taking over the world. Fortunately, that is not the case. Artificial intelligence is not designed to be human, but to execute tasks characterised by human intelligence like learning and reasoning. The tasks assigned to AI can be simple or complex – it all depends on the end goal.
Machine learning (ML) is the most common form of artificial intelligence, although not all AI uses machine learning principles. ML programs learn from data and study patterns to complete tasks accurately without human involvement. The algorithms can either be supervised – using predefined data sets and applying the principles to new data sets – or unsupervised – where the algorithm searches through data to find its own patterns. A combination of supervised and unsupervised algorithms typically yields the best results by allowing the program to effectively analyse trends and new data while picking up on other patterns, links, and anomalies an analyst may have missed.
How does it work?
Machine learning algorithms need to be trained with as much data as possible before they can effectively execute their desired task – in this case, fraud detection. The process starts by giving the algorithm a large amount of previous data to work through and learn from. The program will study patterns in the dataset and develop a method of detection based on the examples found in previous data. Once the algorithm has analysed the basic features of the data, you can train it further by providing your own scenarios with true or false answers. Essentially, the more practice an ML algorithm gets at the desired task, the more successful it will be as it is continuously learning. Once the algorithm is trained, it is ready to perform its fraud detection duty.
How can AI prevent fraud?
In the past, fraud detection programs have been developed by analysts using sets of rules that determine what activity is fraudulent and what isn’t. Unfortunately, this method has become outdated and ineffective. Advances in technology have made keeping up with changes in fraud schemes using rule-based approaches almost impossible. Amounts of data are expanding exponentially, and it is difficult and time-consuming to keep up with these developments in a space that is ever-changing. That’s where machine learning comes in. Not only does it develop its own rules based on previous data (eliminating this time-consuming process previously done by analysts), it continually adapts to new data, making it more effective in detecting new methods of fraud as they are developed and flagging them as fraudulent.
While better detecting new examples of fraud, artificial intelligence also completes the process much faster than other systems. Fraud schemes and anomalies in business transactions can take several weeks or months to detect, by which time the damage is already done. AI detects fraud in real-time, allowing businesses to take immediate action and prevent more damage from being done.
Machine learning data analysis is also beneficial for fraud and risk analysts. MI algorithms provide a complete and easily accessible picture of historical company data, allowing analysts to understand transaction history in seconds and adjust risk assessments quickly and efficiently.
Beneficial for businesses, AI is also beneficial for customers. When implementing rule-based approaches to fraud detection, customer experience can be impacted by making systems slower or more complex to use. This is especially pertinent in companies that have experienced cases of fraud and add several protections that frustrate customers. Machine learning alleviates this problem by maintaining a smooth customer experience and giving business owners peace of mind that any problems will be immediately detected. It also greatly limits instances of false positives in fraud detection that can frustrate customers, employees, and businesses owners by wasting time and resources.
The Biggest Challenge for Businesses
Fraud detection and prevention is already one of the most common applications of artificial intelligence and its current success means this use of AI is likely to increase in the years to come. According to an ACFE report, 13% of companies surveyed currently use artificial intelligence as an anti-fraud measure.
However, the implementation of AI as an anti-fraud measure is not without its challenges. According to the report, the chief challenge among companies is budget. New technologies can be costly to acquire, especially if specialised staff need to be hired or trained in the process. The second most common concern is the skills of in-house staff, as machine learning software is often developed within a company. Poor data quality and concerns about the perceived return on investment were also mentioned as stumbling blocks in acquiring new anti-fraud technologies like AI.
Despite these challenges, companies are looking to adopt AI anti-fraud technology more than any other measure within the next two years. While its use is not widespread currently, 25% of companies responded that they are looking to implement AI within the next two years and will either maintain, increase, or significantly increase their anti-fraud technology budgets. Machine learning algorithms, already prevalent in fraud detection, are primed to become the fraud detection and prevention methods of the future.
AI and machine learning has extensive applications in fraud prevention and detection. It is adaptive, fast, and capable of dealing with the fraud challenges presented by the advanced fraud schemes of the 21st century. Any business focused on a comprehensive, future-proof fraud detection plan is bound to benefit from the inclusion of artificial intelligence.