Banking: The Benefits of Explainability

About bunq

bunq is the second largest neobank in the EU, founded in 2012 by Ali Niknam. From its inception, bunq set out to challenge what banking is and can do. Pioneering many things considered impossible, the innovations bunq brought to the banking industry fundamentally changed how millions of people spend, save and invest their money.

Safety of its users is at the heart of bunq. For years, the neobank has been using AI to monitor and detect fraudulent transactions. Thanks to the transaction monitoring system built in-house, bunq dramatically reduces false hits by a factor of 2.5, compared to a “rule-based” approach often used by traditional banks. Moreover, it’s completely scalable (which is important for a growing scale-up). As of recently, banks are allowed to monitor transactions by a combination of rule-based models and AI models. Every transaction goes through this system and if either one of the two components generates a hit, the transaction gets sent to the compliance department for manual checking.

Being at the forefront of using AI to make users’ lives easy, bunq manages to both improve the user experience as well as user safety. bunq was one of the first to use data science to streamline its onboarding, screening, and risk processes. Ultimately, this enables a broader collaboration between the financial sector and online players, as AML can only be tackled effectively if companies work together.

Challenges in transaction monitoring

Bank transactions are monitored by a combination of a rule-based systems and machine learning. The Machine Learning system assesses the probability of fraud for each transaction based on historical patterns, profile information, and transaction details. In case a transaction is flagged, an investigation by a Compliance Operations specialist (ComOps) follows. The investigation can lead to a hit, reporting the user to the Financial Intelligence Unit (FIU), a hit and not reporting the user to the FIU or clearing the transaction

To keep control of both implemented systems, it is key to put monitoring, explainability, traceability, and the human-feedback loop in place between the Data Scientists team, the machine learning system, and ComOps agents.

It’s crucial to explain why hits are raised, giving ComOps agents clues about where to start their investigation, enabling them to handle hits efficiently. Furthermore, explainability is required in order to control the risks and provide effective human oversight for every stakeholder.

Together with bunq and the ComOps agents, Deeploy developed a tailored transaction monitoring explainer to make the model interpretable to the stakeholders, and to provide a good starting point for investigations. Other important aspects include continuous monitoring and an effective feedback loop.

The Deeploy platform solution

Deeploy provides the platform that enables the Data Scientists of bunq to technically deploy their models while meeting all compliance constraints and requirements with respect to explainability, traceability, and monitoring of the AI models. Without such a platform, converting extensive data science modeling, time to production and compliance-ready deployments is simply not possible, meaning expensive data science time is thrown away.

The data team of bunq uses Deeploy to easily deploy their models, explain predictions to end users (stakeholders), monitor performance and feedback, and actively improve & update models more frequently. bunq and Deeploy collaborated on the development of a domain-specific explainer for transaction monitoring, making sure the model is transparent and explainable to everyone involved.

With Deeploy, bunq now has a future-proof MLOps setup to keep control of current and future models, explain the models to their ComOps agents and regulators, and continuously improve them based on human feedback, a true loop of Responsible AI.