Compliant AI Transaction Monitoring
Using AI for transaction monitoring can speed up processes by 90% compared to traditional rule-based approaches. However, a lack of oversight over running models, regulatory requirements, and a lack of trust from end users and stakeholders is hindering implementation in financial institutions.
Leveraging explainability and oversight over running models is the key to successful implementation, giving financial institutions that apply this a head start on competitors and a strong foundation to avoid hefty regulatory fines. Read on for how bunq and other financials have used Deeploy to meet requirements of AI usage in banking and ensure successful productionalization.
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How AI revolutionizes transaction monitoring
Successfully implementing AI in transaction monitoring
AI models for transaction monitoring assess the probability of fraud for each transaction based on historical patterns, profile information, and transaction details. When a transaction is flagged, an investigation by a Compliance Operations Specialist (ComOps) follows. This investigation can lead to a hit and report of the user to the Financial Intelligence Unit (FIU), a hit but with no reporting of the user or complete clearing of the transaction.
While promising, institutions run into issues of lack of oversight over running models, regulatory requirements, and a lack of trust in AI decisions from ComOps and stakeholders.
How does Deeploy solve these challenges?
Centralized model deployment & monitoring
Through an intuitive interface, data science teams can then monitor running models across vital metrics and set personalized alerts to quickly address any degradation or anomalies. Furthermore, seamless integration with leading MLOps platforms such as Sagemaker, Azure ML, and MLFlow facilitates adoption for any team.
Deeploy also has all the necessary safety features in place to ensure sensitive data stays in control of the institution and model access is well managed trough various levels of user authorization and use of credentials.
Tailored explainability & feedback loops
Employees and stakeholders may be hesitant to fully trust AI-based transaction monitoring systems. AI models can be highly complex, making it challenging to understand how they arrive at certain decisions or predictions. Lack of interpretability can also hinder regulatory compliance efforts as authorities often require explanations for flagged transactions.
In addition, AI models can also inadvertently perpetuate biases present in the data used for training. Biased transaction monitoring may lead to unfair treatment of certain individuals or groups, which can result in legal and reputational risks for financials.
Deeploy makes it easy for teams to deploy models along with explainers, which can then be connected to an interface that allows ComOps agents to see which features affected a particular transaction flagging.
ComOps agents can overrule model decisions as well as provide feedback on the decision and the usefulness of the explanation provided. This feedback can then be used in the so-called human feedback loop which allows for continuous model improvement.
On Deeploy, data science teams can then monitor the percentage of overruled decisions trough the disagreement ratio metric. When big increases in this metric are detected, there might be a reason to retrain models and explainers. Teams can also set alerts for this metric, allowing for a greater manageability and oversight.
Traceability, reproducibility & compliance
Since AI systems require access to sensitive financial data to effectively monitor transactions, compliance requirements are a substantial concern for stakeholders.
Regulatory compliance often requires maintaining detailed audit trails and documentation of transaction monitoring activities. Financial institutions must ensure that their AI systems can provide sufficient documentation to demonstrate compliance with regulatory requirements. AI systems used in transaction monitoring must also be capable of generating accurate reports in compliance with regulatory requirements. Deeploy offers comprehensive auto recording of all deployment updates and events and the ability to traceback & reproduce all predictions/decisions.
In addition to this, evolving regulations on the use of AI, such as the EU AI Act, intensify scrutiny of the deployment of AI systems in high-risk scenarios. Deeploy’s platform is built with these regulations in mind, ensuring an appropriate level of transparency and human oversight over AI models and offering dedicated compliance features.
How Deeploy fits in your architecture
Deeploy empowers financial institutions to deploy high-risk AI systems while upholding transparency, control, and compliance standards.
By leveraging Deeploy, organizations can significantly reduce the burden on their data teams, saving valuable time spent on maintenance tasks and facilitating the monitoring process. Moreover, a full stack of integrations makes adoption seamless, avoiding costly implementation time.
How to get started with Deeploy
Want to learn more about how Deeploy can suit your organization? Submit a request to get in contact with our experts, who can advise you on what the best solution & implementation is!