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Slide link: https://ibm.box.com/v/clari5-1215-webinar
20231215 - IBM Z APAC webinar - Clari5.mp4
As a complex system par excellence, the financial and banking sector presents many relevant risks, which needs to be addressed specifically. Indeed, money laundering is a highly impactful threat to democracies , as it allows organized crime to create a subterranean economy able to destroy social fabric, and to gain prizes from criminal activities. To give a snapshot, the United Nations Office on Drugs and Crime (UNODC) estimates that between 2 and 5% of global GDP is laundered each year, meaning between 715 billion and 1.87 trillion euros each year. Anti Money Laundering rules require gatekeepers (banks and other obliged entities) to apply measures to prevent money laundering and terrorist financing
Introduction Banks are facing an increasing demand from regulators to monitor and mitigate money laundering risks. One common challenge is that Anti-Money Laundering (AML) Transaction Monitoring systems do not always result in suspicious investigations or actionable results
For example, if an attacker collects a ransom in bitcoin or ether, they need a cryptocurrency exchange to launder the money. In September, the U.S. Department of the Treasury added the exchange Suex to its list of sanctioned entities due to laundering ties with ransomware attackers
Demo videos Realtime detection and prevention of credit card fraud with AI on IBM zSystems - demo video Training Fraud model on IBM Financial Services Cloud and inferring on IBM zSystems - demo video Anti Money Laundering with AI on IBM zSystems - demo video Loan risk detection with sample voice assistant - demo video IBM Z Deep Learning Compiler (onnx-mlir) - demo video Solution Briefs Solving fraud in real-time on IBM zSystems - solution brief Solving Anti Money Laundering on IBM zSystems - solution brief Solving Image Recognition and Natural Language Processing with IBM zSystems - solution brief Redbooks Optimized Inferencing and Integration with AI on IBM Z Introduction, Methodology, and Use Cases, redbook link A Fraudulent Claims processing use case study with US State Govt - link Fraud detection and co-location use case study with Financial services - link Optimized Inferencing and Integration with AI on IBM Z Introduction, Methodology, and Use Cases - link Demystifying Data with AI on IBM Z - link What AI can do for you: Use cases for AI on IBM - link Become Data Driven with IBM Z Infused Data Fabric - link Others Celent paper on Operationalizing Fraud prevention on IBM z16 - link Watson Machine Learning for z/OS 2022 blog - link AI on IBM zSystems 101 page - link AI on IBM zSystems Content solution - link IBM Z Deep Learning Compiler - Technical blog IBM Z Optimized for TensorFlow - Release blog IBM Z Optimized for TensorFlow - Open-beta registration
Monitoring transactional activity with anti-money laundering regulations Anti-money laundering (AML) refers to regulations and procedures used to prevent criminals from disguising illegally obtained funds as legitimate income
Customer identification is the most crucial component of KYC (Know Your Customer), which is a key part of the fight against financial crime like money laundering. It comes first and aids the other steps of the procedure to run more smoothly
For example, if an attacker collects a ransom in bitcoin or ether, they need a cryptocurrency exchange to launder the money...Department of the Treasury added the exchange Suex to its list of sanctioned entities due to laundering ties with ransomware attackers