Sygno | Know good, catch bad

What Does Trump’s expected FDIC chair and Dutch AML priorities have in common? Discover our CEO Sjoerd Slot's insights on the evolving financial regulatory landscape across both sides of the Atlantic in this article.

Transaction monitoring that generates excessive false positives risks unnecessary invasion of privacy. It's an ongoing balancing act: protecting customer data under GDPR while meeting Anti-Money Laundering (AML) obligations. With evolving regulations, how to effectively monitor transactions and catch financial crime without compromising privacy? This article explores how responsible AI can create transaction monitoring solutions that hit that “just right” balance.  https://lnkd.in/eEu_UnQi #knowgoodcatchbad #transctionmonitoring #privacy #gdpr #aml #goldilocks #regtech #compliance #bankingai #finance

What drives your efforts to optimize transaction monitoring? Optimizing transaction monitoring (TM) is often driven by a set of challenges and objectives - from reducing false positives to navigating regulatory requirements - specific to your institution's situation, context, and needs. Common questions we encounter: --| How to reduce false positives and manage alert fatigue (AF)?  --| How can AI be leveraged to improve transaction monitoring?  --| How can we detect unknown financial crime patterns?  --| What are the specific regulatory requirements for transaction monitoring based on our region or institution type? But what’s driving 𝐲𝐨𝐮𝐫 TM optimization efforts? We’d love to hear what challenges you're looking to overcome and what goals shape your strategy. #compliance #regtech #knowgoodcatchbad #fincrime #transactionmonitoring

#5 - Balancing benefits and risks of AI in finance – conclusion      This fifth post concludes our series! We explored them per stage in the AI development cycle—data, model, deployment—as outlined by an article from the ECB’s Financial Stability Review (https://lnkd.in/eTSDNikG) and looked at them through the lens of transaction monitoring.       𝐋𝐞𝐭'𝐬 𝐫𝐞𝐜𝐚𝐩 𝐬𝐨𝐦𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬:     --| The 𝐝𝐚𝐭𝐚 𝐬𝐭𝐚𝐠𝐞 comes with risks such as bias in training data and poor data quality, both of which can lead to false positives and missed threats. However, AI's ability to process large datasets enables us to uncover unknown unknowns, find emerging risks, and build a strong understanding of legitimate behavior.      --| In the 𝐦𝐨𝐝𝐞𝐥 𝐬𝐭𝐚𝐠𝐞, complexity is a challenge, which can make models harder to explain and validate, and knowledge cut-off, possibly resulting in inaccurate and less-than-up-to-date outcomes. Designing explainable models anchors human oversight into the process, while continuous training, updating, and validation ensure models stay current and aligned.     --| During the 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐬𝐭𝐚𝐠𝐞, risks include the possible unpredictability in behavior once deployed and resource-intensiveness of AI implementation. On the flip side, there’s benefit in automating routine tasks and improved detection accuracy, and solutions that integrate seamlessly into (legacy) systems without needing expensive migrations or specialized teams.     In conclusion, AI in transaction monitoring can potentially improve effectiveness, detection accuracy, operational efficiency, and decision-making. It can do so by helping us better understand legitimate customer behavior and overcoming technical and resource-related challenges.     Hopefully you enjoyed this series and got something out of it. If so, feel free to share your ‘something’ in the comments! Also, reach out anytime (https://lnkd.in/enJzACSy) to talk about how leveraging AI and automated model generation could enhance your transaction monitoring.     #aml #machinelearning #banking #compliance #transactionmonitoring #knowgoodcatchbad #automatedmodelgeneration #fincrime #finfraud #ecb

#4 - Balancing benefits and risks of AI in finance, the deployment stage We explored the benefits and risks of the data and model stages in AI development, as identified in the ECB’s Financial Stability Review (https://lnkd.in/eTSDNikG). Now, let’s look at what they are for the final stage—deployment—and see how they translate to transaction monitoring. 𝐑𝐢𝐬𝐤𝐬 include predictability and human oversight. AI models are typically adaptable and dynamic, which could make ensuring their predictability and controlling their behavior challenging once deployed. This underscores the need for robust, ongoing human oversight to ensure models continue to perform as expected and intended. Also, deploying and integrating models into existing (legacy) systems can be resource-intensive, posing challenges for institutions with scarce (technical) resources. 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 include improved decision-making, facilitated via ‘... 𝑓𝑎𝑠𝑡𝑒𝑟 𝑎𝑛𝑑 𝑚𝑜𝑟𝑒 𝑐𝑜𝑚𝑝𝑟𝑒ℎ𝑒𝑛𝑠𝑖𝑣𝑒 𝑖𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑖𝑛𝑔'. Additionally, the automation of routine tasks, such as training and updating or transaction reviews and compliance checks, potentially lowers operational costs and frees up specialists to focus on business-critical, high-value work. 𝐖𝐡𝐚𝐭 𝐒𝐲𝐠𝐧𝐨 𝐝𝐨𝐞𝐬: we use Automated Model Generation to create transparent, explainable models that facilitate and simplify human oversight. Our models seamlessly integrate into your existing transaction monitoring system, eliminating any costly migration to new systems or the need for building a specialized AI team. This makes leveraging AI’s capabilities for better detection accuracy and improved efficiency in transaction monitoring accessible to all institutions, regardless of size and available resources. 𝐔𝐩 𝐧𝐞𝐱𝐭: What’s your take? How do you see AI’s impact on transaction monitoring, especially in the deployment stage of the AI development cycle? Let’s continue the conversation in the comments! #aml #machinelearning #banking #compliance #transactionmonitoring #knowgoodcatchbad #automatedmodelgeneration #fincrime #finfraud #ecb 

#3 - Balancing benefits and risks of AI in finance, the model stage This third post of our series gos into the model stage of AI development, exploring risks and benefits highlighted in an article (https://lnkd.in/eTSDNikG) from the ECB’s Financial Stability review. 𝐑𝐢𝐬𝐤𝐬 include the complexity and explainability of AI models, meaning as models are adjusted incrementally over time, it becomes harder to follow and reconstruct how they arrive at conclusions. Additionally, training data is typically cut off at some point, limiting up-to-date accuracy to a model's latest changes. 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 are in an AI’s learning capabilities that, combined with human oversight on model validation and approval, can help draw more in-depth insights from available data. This translates into smarter, more informed decisions and improved accuracy.    𝐖𝐡𝐚𝐭 𝐒𝐲𝐠𝐧𝐨 𝐝𝐨𝐞𝐬: we use AI to generate transparent, explainable, and auditable AML and fraud models. Automating updates helps to minimize the technological knowledge cut-off and keeps them as up-to-date as possible. Ongoing cycles of learning and validation enable a robust understanding of legitimate customer behavior and provide deep insight over time. This allows you and your team to more confidently identify and investigate previously unseen threats, giving you a competitive edge in managing risks.    𝐔𝐩 𝐧𝐞𝐱𝐭: what are your thoughts on balancing AI's risks and benefits at the model stage? Share them in the comments! Also, stay tuned for our next post - exploring deployment stage risks and benefits of AI in transaction monitoring. #aml #machinelearning #banking #compliance #transactionmonitoring #knowgoodcatchbad #automatedmodelgeneration #fincrime #finfraud

#2 - Balancing benefits and risks of AI in finance, the data stage  The first post of this series based on an article from ECB’s Financial Stability review (link in comments) highlighted the three stages of AI development: the data stage, the model stage, and the deployment stage. This one looks at balancing risks and opportunities for the data stage and relates them to transaction monitoring.   𝐑𝐢𝐬𝐤𝐬: data bias and poor data quality are areas of concern, as either can lead to inaccurate or undesirable results. In transaction monitoring, if systems are trained on past indicators of suspicious activity, they may focus only on finding that activity, missing emerging risks (the unknown unknowns), overlooking blind spots, and potentially flagging legitimate activity as suspicious. After all, just because something happened once does not mean it will again. 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 include AI’s ability to process and analyze large datasets and derive (previously unknown or undetected) patterns hidden in that data. This could help institutions stay ahead of evolving threads by improving the accuracy of transaction monitoring and facilitating better decision-making.  𝐖𝐡𝐚𝐭 𝐒𝐲𝐠𝐧𝐨 𝐝𝐨𝐞𝐬: we acknowledge that suspicious behavior is fluid and ever-changing, while legitimate behavior is much less so. By focusing on establishing these legitimate behavior patterns, our AI models can reduce false positives by up to 80% and uncover unknown unknowns, allowing analysts to focus on real threats with more confidence. This leads to more accurate case management and, overall, more effective transaction monitoring. 𝐔𝐩 𝐧𝐞𝐱𝐭: what are your thoughts? Share them with us! We're always curious. Also, stay tuned as the next post of this series goes into balancing the risks and opportunities of the model stage. #finance #banking #ai #fintech #transactionmonitoring #aml #knowgoodcatchbad #ecb #dataquality #bankingai #artificialintelligence #machinelearning #automatedmodelgeneration #casemanagement

#1 – Balancing benefits and risks of AI in the financial sector The ECB published the Financial Stability Review earlier this year which included an article on the rise of Artificial Intelligence (AI), detailing an analysis of AI’s impact on financial stability through benefits and risks. Some of its insights:  // 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 𝐫𝐢𝐬𝐤𝐬 𝐚𝐧𝐝 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬. Successful integration of AI into the sector requires an all-encompassing approach, one that needs constant evaluation of balancing AI's risks and opportunities as technology evolves.   // 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐬𝐭𝐚𝐠𝐞𝐬, 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐩𝐫𝐨𝐟𝐢𝐥𝐞𝐬. Each AI development stage -Data, Model, Deployment- has its own risks and benefits. Distinguishing between them and understanding those distinctions is important for shaping effective regulation and successful integration of AI. // 𝐂𝐥𝐞𝐚𝐫 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬, 𝐭𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬. Lastly, clear definition of use cases allows for a focused approach to tackling specific challenges and leveraging opportunities, given these differ per and are specific to each use case.  With a series of posts after this one, we’ll explore how AI risks and benefits per stage (data, model, deployment) translate to transaction monitoring. Stay tuned! Full ECB article: https://lnkd.in/eTSDNikG  #finance #banking #ai #fintech #transactionmonitoring #aml #knowgoodcatchbad #ecb

Imagine! Your case analysts freed up and ready to effectively conduct meaningful investigations. A clean backlog for your data scientists, allowing them to focus on other bank-specific risks. Transparent, explainable AI models that equip your compliance team in their work with regulators. Wouldn’t that be something? And the best part? No tricky transition or lengthy migration to new systems, but a reliable and easy-to-implement solution that seamlessly plugs into your existing infrastructure and runs on your existing TMS. Would you like to know more? Feel free to reach out and let's chat. #transactionmonitoring #aml #tms #knowgoodcatchbad #frauddetection #compliance #explainableai #automatedmodelgeneration #machinelearning

The value isn’t in the system, it’s in the detection models running on it.   Our automated machine-learning technology generates detection models based on your raw data and regulatory requirements. It deploys these models in your transaction monitoring system of choice. They then enhance your monitoring system to better identify known and yet unknown financial crime, making everyone’s lives easier and freeing up your specialists.  Curious to know how our technology can enhance your transaction monitoring system? Feel more than welcome to contact us (link in comments below). Talk soon!  #transactionmonitoring #financialcrime #frauddetection #automatedmodelgeneration #machinelearning #aml #knowgoodcatchbad #detectionmodels