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Data has always been the foundation of the banking industry. What has changed in recent years, of course, is the amount of data available, and the speed at which it is processed as well as the need to quickly respond to market changes.

One of the biggest roadblocks to Enterprise AI in banking is not a question of putting machine learning models into production or even of creating the models themselves. Rather, it’s data management, which (while seemingly simple) is essential to enabling the organization to leverage data from the bottom up, democratizing data use across teams and roles.


Regulations represent an obvious and unique challenge, and while it’s true that the ways in which personal data can be obtained and used are limited, cutting-edge banks know how to enable staff to work with data within the confines of a well-defined data governance strategy by choosing the right tools.

Top Use Cases for AI in Banking

Fraud Detection: Whether being used to detect ATM fraud, bad check writing, or insider threat, fraud detection is all about finding patterns of interest (outliers, exceptions, peculiarities, etc.) that deviate from expected behavior within datasets. Using multiple types and sources of data is what allows banks to move beyond point anomalies into identifying more sophisticated contextual or collective anomalies that point to fraudulent activity.

The Data Factory at BRED built a system in production that monitors the status of its more than 600 ATMS, delivering outage information in a meaningful way for business teams to take action (regarding maintenance allocation and activity, etc.).


The data: The model uses outage data coming from the ATMs themselves, including the state of its different components (like the network connection, card reader, etc.). Through initial analysis in Dataiku, the team found that this data was quite imbalanced: for example, only 20 percent of outages lasted longer than 15 minutes, while another 20 percent lasted for less than four seconds and 40 percent less than two minutes.


The model: Leveraging a clustering algorithm in Dataiku, the team found that a large group of ATMs (more than 70 percent) rarely have problems. Other clusters allowed the team to identify specific technical defects (e.g., with the card reader or with network connection) with certain ATMs. But ultimately, the project uses a mix of models – clustering, survival analysis, and time series – in its final solution.


The solution: The solution that The Data Factory at BRED delivered alerts the business team when maintenance needs to be sent to repair an ATM based on thresholds – for example, unavailability that lasts a few seconds doesn’t create false alerts. They also delivered dashboards for predictive maintenance of ATMs. 

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