Best AML Software for Apache Spark
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Compare the Top AML Software that integrates with Apache Spark as of November 2025
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This a list of AML software that integrates with Apache Spark. Use the filters on the left to add additional filters for products that have integrations with Apache Spark. View the products that work with Apache Spark in the table below.
What is AML Software for Apache Spark?
Anti-Money Laundering (AML) software is designed to help financial institutions and businesses detect and prevent money laundering activities by monitoring and analyzing financial transactions. The software uses algorithms to flag suspicious patterns, behaviors, or activities that may indicate illicit financial activity, such as large transactions or unusual account behavior. AML systems often include features like transaction monitoring, identity verification, and risk assessment to ensure compliance with regulatory requirements. The software also helps organizations generate reports for regulatory authorities and supports ongoing investigations into potential criminal activities. By implementing AML software, businesses can reduce the risk of financial crime and avoid penalties for non-compliance. Compare and read user reviews of the best AML software for Apache Spark currently available using the table below. This list is updated regularly.
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Quantexa
Quantexa
Uncover hidden risk and reveal new, unexpected opportunities with graph analytics across the customer lifecycle. Standard MDM solutions are not built for high volumes of distributed, disparate data, that is generated by various applications and external sources. Traditional MDM probabilistic matching doesn’t work well with siloed data sources. It misses connections, losing context, leads to decision-making inaccuracy, and leaves business value on the table. An ineffective MDM solution affects everything from customer experience to operational performance. Without on-demand visibility of holistic payment patterns, trends and risk, your team can’t make the right decisions quickly, compliance costs escalate, and you can’t increase coverage fast enough. Your data isn’t connected – so customers suffer fragmented experiences across channels, business lines and geographies. Attempts at personalized engagement fall short as these are based on partial, often outdated data.
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