Anomaly detection in banking
Several high-profile banks are leveraging anomaly detection solutions for fraud and anti-money laundering. While some banks and AI firms provide information on how their solution works or how their chosen solution worked for them, it can be hard to determine which ones are successful today. In this article, we provide two detailed examples of AI firms that offer evidence of success with their anomaly detection solutions. These solutions are made to detect and stop a variety of fraud methods.
These range from malware and more conventional banking fraud methods to money laundering attempts through receiving and sending payments from suspicious entities. The anomaly detection solutions we explain include:
o Feedzai's Anomaly Detection-based Fraud Detection Platform: Using AI-powered anomaly detection technology to recognize and stop attempts at bank fraud.
o Ayasdi's Anti Money-laundering Solution: Anomaly detection used for recognizing changes in customer behavior and analyzing them for patterns related to money laundering or fraud.
We begin our explanation of these anomaly detection solutions with Feedzai's fraud detection platform and its success with Citibank.
Feedzai's Anomaly Detection-based Fraud Detection Platform The AI startup Feedzai sells their anomaly detection platform to banks and other industries for fraud detection. They claim their software can help their clients prevent fraud and money laundering by using detailed risk profiles for customers and scoring their likelihood of fraud based on granular data regarding each customer.
Anomaly detection applications require a machine learning model that is trained a continuous stream of incoming data, such as banking transactions happening in real-time. Feedzai's models can purportedly be trained to recognize a baseline sense of normalcy for the data within banking transactions, loan applications, or new account information.
Like most other anomaly detection applications, the software can alert a human employee of any deviations from the typical pattern so that they may give it a closer review for fraud.
The employee can choose to accept or reject this notification, which the machine learning model can use to recognize if its determination of fraud from a single transaction is correct. This further trains the model to understand that the deviation it recognized is acceptable, or indeed an indication of fraud.
The image below provides a simple depiction of how Feedzai's platform works. The raw transaction data flows from the real-time data stream, large data stores referred to as "data lakes," and APIs into the OpenML Engine platform. This platform then routes the data towards the endpoints necessary to conduct experiments and real-time analysis. At the same time, this data funnels into the "Feedzai risk studio," a database the company uses to improve future ML models, products, and software development methods
Feedzai's software platform can purportedly complete these tasks by monitoring each transaction for unusual payment behavior or any discrepancies in the information used to validate, authenticate, or otherwise confirm them. It would then need to analyze each anomaly before determining if the transaction is likely fraud. If the transaction proved to be legitimate, the software would then clear it to continue through the client bank's payment processing system.
Feedzai claims their OpenML Engine, a user interface for creating new machine learning models, can help data science teams at banks to customize new ML algorithms using provided samples. This can be invaluable for banks that are trying to build a data science team and train them to have expertise with banking data that could indicate fraud. Our readers can find a demonstration of how one might use this interface here, although Feedzai does require some identification information before displaying the video.
We spoke about how banks may benefit from this type of AI tool in an interview with Lee Smallwood, COO of Markets and Securities, North America at Citi, on our Podcast, AI in Banking. Smallwood emphasized his view on how banks should approach building and leveraging their data science and AI teams. He focused on how banks can create a culture of innovation so that their company can find success and benefits outside of direct ROI. When asked how a large bank might be able to do this, Smallwood said,
…Probably the biggest difference in that kind of culture of innovation is … a comfort with failure that I think a lot of large financial institutions still struggle with.