These tools can be extensively used for fraud detection.
Since the early 2010s, major banks have used anomaly detection – an AI technique for identifying deviations from a norm – for automating fraud, cybersecurity, and anti-money laundering processes. Fraud Detection with Machine Learning becomes possible due to the ability of ML algorithms to learn from historical fraud patterns and recognize them in future transactions. Machine Learning algorithms appear more effective than humans when it comes to the speed of information processing. Also, ML algorithms are able to find sophisticated fraud traits that a human simply cannot detect. Teradata is an AI firm selling fraud detection solutions to banks. They claim their machine learning platform can enhance banking fraud detection by helping their data analytics software recognize potential fraud cases while avoiding acceptable deviations from the norm. In other cases, these deviations may be flagged and end up as false positives that offer the system feedback to “learn” from its mistakes.
According to a case study listed on their website, Teradata helped Danske Bank modernize its fraud detection process and reduce its purported 1,200 false positives per day. The case study states that by the time Danske Bank had finished installing and implementing Teradata’s solution, they were able to reduce their false positives by 60% and were expected to reach 80% as the machine learning model continued to learn, increase detection of real fraud by 50% and refocus their time and resources toward actual cases of fraud and identifying new fraud methods.
Anomaly detection-based fraud detection and prevention solutions are more common than predictive and prescriptive analytics. This type of application requires a much more common machine learning model that is trained on a continuous stream of incoming data. The model is trained to have a baseline sense of normalcy for the contents of banking transactions, loan applications, or information for opening a new account. The software can then notify a human monitor of any deviations from the normal pattern so that they may review it. The monitor can accept or reject this alert, which signals to the machine learning model that its determination of fraud from a transaction, application, or customer information is correct or not. This would further train the machine learning model to “understand” that the deviation it found was either fraud or a new acceptable deviation.
This kind of baseline could also be established for interactions with various other banking operations or entities. In addition to account owners, fraud can come from merchants and issuers, and their transaction information can be used to train a machine learning model to recognize transactions processing properly. This would usually involve pricing, but could also involve the omission of unpaid merchandise. Another possibility is spending behavior, which would allow the machine learning model to recognize fraudulent details of retail shopping or eCommerce. Geolocational data may be important for these types of applications, as it is common that fraudulent transactions occur far away from where the account owner lives. One vendor selling anomaly detection-based fraud detection software to banks is Feedzai. The company claims its software can help banks prevent fraud and money laundering by developing detailed risk profiles on customers and scoring them based on granular data.
The company claims that its OpenML engine software can help a bank’s data science team build their own machine learning models for fraud detection using the software provided, fraud models. Feedzai claims to have helped one of the top retail banks in the US more accurately detect fraud. They published a case study showing the bank’s success with the software, but did not mention them by name. This is important to watch out for when considering AI software vendors, but because of Feedzai’s team of AI talent and marquee clients such as Citibank, we are confident that they do actually use AI. According to the case study, the client bank found that their current fraud detection process for the online application of their main application processing system had been rejecting over half of the applicants. This resulted in significant losses the bank wanted to prevent in the future. They needed a risk scoring application that could run through new account applications and only accept those that revealed a low-risk rate for fraud. The bank wanted to make sure the only applications to be pushed to manual review were indeed a risk, and that the risk factors were highlighted for faster decision-making on the part of the human monitor. The client bank deployed Feedzai’s fraud detection software within their application processing system using their own databases. This purportedly made Feedzai’s software the chief decision-making engine for the onboarding process for new customers and could check their identities, eligibility, and assess fraud risk of individual customers. The case study also states that software was also able to ask follow-up questions specific to the customer when it was not presented with enough information to make a decision. Feedzai claims in the case study that the client bank saw a 70% increase in newly onboarded customers after integration with their software. They also say the bank saw no increase in fraud losses even though the number of approved applicants increased.
