Sanctions screening and fraud prevention solutions use real-time detection to prevent terrorist financing and financial crime; whereas anti-money laundering (AML) primarily follows an “observe and report” process. Such a process is all that is currently required by many regulators. Increasingly though, international compliance teams are choosing to stop transactions before they are executed – based on suspicions of money laundering activity. More and more, the industry has been asking itself if this approach of rejecting suspicious activity is a more effective strategy to prevent money laundering. This paper explores where and why AML real-time detection might make sense as a new paradigm for global financial institutions.
Global anti-money laundering (AML) standards have long required that understanding beneficial ownership be a part of a financial
institution’s AML program. Beneficial ownership outlines the identity of individuals with a controlling interest in a privately held company, enabling a financial institution to understand the ultimate beneficiary of a financial transaction. Identifying beneficial ownership can be a complex process, but it’s one that institutions must conquer if they are to remain in compliance with industry rules and legislation.
Featuring Andy Schmidt, Principal Executive Advisor
Knowing your customer is key to driving a successful strategy for client security and retention. Watch this video to get a picture of how KYC can be deployed for risk minimization, work with compliance, and make security measures more frictionless using cutting edge technology.
Financial institutions seeking to attract new customers and revenue channels are expanding into digital services, real-time payments and global transactions. However, with every new service, criminals are developing innovative ways to infiltrate financial systems, and older technologies that mitigate fraud no longer work as effectively.
So how can financial institutions respond to this growing threat?
Fortunately, more advanced technologies hold great potential for real-time financial crime mitigation. Learn about five current and emerging technologies that could impact money laundering and fraud mitigation, including artificial intelligence/machine learning, blockchain, biometrics, predictive analytics (hybrid model) and APIs.
Read the latest Fiserv white paper: Five Tech Trends That Can Transform How Financial Institutions Detect and Prevent Financial Crime.
For the past decade, financial institutions have created sophisticated digital platforms for consumers to access, save, share and interact with their financial accounts. As sophisticated as these digital platforms have become, cyber criminals continue to pose an ever-present risk for everyone – from individual consumers to large corporations
In his recent article, 2018 Outlook: Customer Experience and Security Strike a Balance, Andrew Davies, vice president of global market strategy for Fiserv’s Financial Crime Risk Management division, explains how and why security will become a key differentiator for financial institutions as they respond to a changing landscape, which includes:
•Global payment initiatives
•Open Banking standards
•Artificial intelligence and machine learning
•Consumer demand for real-time fraud prevention and detection
Published By: Teradata
Published Date: Jun 12, 2013
The ever-changing environment has offered fraudsters some tactical advantages to probe for holes in the defenses of financial services companies. Conventional approaches to fraud detection and remediation remain effective to a point, but conventional tools cannot effectively and economically process what is known as big data.
For most financial institutions, it’s no longer a question of ‘if’ but ‘when’ they’ll be attacked..
If you’re like most financial institutions, you have controls that identify breaches, but need proper procedures that’ll enable you to recover from such an event. In this presentation at the CUNA Technology Council Conference, Tom Neclerio, BAE Systems’ VP of Cyber Consulting Services, discusses the current threats across the financial marketplace and explores strategies for implementing a successful incident response program as outlined in the FFIEC’s cyber resilience guidance.
As our unpredictable world becomes more complex, interdependent and dangerous, it’s becoming harder to manage third-party risk. Traditional financial and operational risks seem like the good old days. Now procurement has to manage, mitigate and avoid risks as disparate as conflict minerals, cybercrime, natural disasters, resource depletion and many others.
For the past decade, financial institutions have created sophisticated digital platforms for consumers to access, save, share and interact with their financial accounts. As sophisticated as these digital platforms have become, cyber criminals continue to pose an ever-present risk for everyone – from individual consumers to large corporations.
In his recent article, 2018 Outlook: Customer Experience and Security Strike a Balance,
Andrew Davies, vice president of global market strategy for Fiserv’s Financial Crime Risk Management division, explains how and why security will become a key differentiator for financial institutions as they respond to a changing landscape, which includes:
• Global payment initiatives
• Open Banking standards
• Artificial intelligence and machine learning
• Consumer demand for real-time fraud prevention and detection
What if you could use just one platform to detect all types of major financial crimes?
One platform to handle the analytical tasks of fraud detection, including:
Data processing and aggregation
Statistical/mathematical/machine learning modeling
One platform that could successfully reduce complex and time-consuming fraud investigations by combining extremely different domains of knowledge including Business, Economics, Finance, and Law. A platform that can cover payments, credit card transactions, and know your customer (KYC) processes, as well as similar use cases like anti-money laundering (AML), trade surveillance, and crimes such as insurance claims fraud.
Learn more about TIBCO's comprehensive software capabilities behind tackling all these types of fraud in this in depth whitepaper.
Fraud is one of the biggest overheads for most financial firms. Detecting crime is hard as fraud constantly evolves and the tools have to be able to evolve with it. Also one of the key areas of focus for most firms is to address the cost of handling the false positives that all automated systems generate.
Watch this short demonstration to learn how TIBCO’s advanced analytics and data science solutions can help you overcome these challenges.
In this whitepaper, Andrew Foose, vice president of NAVEX Global’s Advisory Services Team, analysed recent legal developments in the U.K. and uncovered four valuable clues on how enforcement of financial crimes may play out in 2015
The Internet has proven to be a vital communications medium for worldwide commerce, but as an open and unprotected global network it can also present a wide range of threats that can cripple any business organization. Several years ago, most Internet threats were relatively benign examples of a young adolescent’s technical expertise but over time they have evolved into increasingly sophisticated domestic and foreign attacks that are designed to capture financial, personal, or strategic business information. Threats now come in the form of deliberately malicious acts, and exploitative opportunities for hackers and/or organized crime. The impact is serious, and the landscape of victims is getting broader every day. In response, no organization can afford to have its networks remain unprotected.
Failing to contain financial crime hits banks with the double impact of crime-related losses and fines imposed by regulators and law enforcement agencies. Depending on the magnitude of a bank’s failure to stem financial crime, fines can run into hundreds of millions of dollars – and even higher in exceptional cases. More importantly, institutions are keen to protect their brand from association with transnational organized-crime rings and scandals related to corruption.
Financial organizations are deploying artificial intelligence and machine learning in the fight against financial crimes. David Stewart, Director of Pre-Sales for the Global Security Intelligence Practice at SAS, offers tips to help separate fact from market hype when reviewing new data analytics tools. You’ll learn about:
• The new industry intrigue with artificial intelligence and machine learning.
• How these emerging solutions can benefit financial institutions.
• The SAS approach of “crawl, walk, run” when it comes to adopting new analytics tools.
This white paper provides insights into the current threat landscape for the financial services industry. Information is based on cyber security event data collected by IBM, as well as data derived from responding to, and performing forensics on, cyber security incidents.
The malware industry supplies all the components cybercriminals need to easily perpetrate malware-driven financial fraud and data theft. In today’s virtual world, the
scope of organizations vulnerable to malware-driven cybercrime is quite broad. In addition to banks and credit unions that are subject to online banking fraud, financial fraud can be perpetrated on insurance companies, payment services, large e-commerce companies, airlines and many others.
"The malware industry supplies all the components cybercriminals need to easily perpetrate malware-driven financial fraud and data theft. In today’s virtual world, the scope of organizations vulnerable to malware-driven cyber crime is quite broad. In addition to banks and credit unions that are subject to online banking fraud, financial fraud can be perpetrated on insurance companies, payment services, large e-commerce companies, airlines and many others. "
These emerging technologies and solutions certainly are not unique to financial services. But Stewart, a business director of security intelligence solutions within the SAS Security Intelligence
Practice, sees particular interest and application in AML circles.
"There remain a good number of manual processes within financial crimes departments in financial institutions, and AI can help automate some of those rote tasks such as document review or alert triage," he says. "Due to investments in technology, there is a lower barrier of entry for midsized institutions. "And finally, there's this anxiety over the unknown - those risks they are not able to detect, that may be hidden using traditional techniques - so they're hoping that more advanced, unsupervised learning techniques can be used to identify those edge cases or behaviors that are out of norm." In an interview about analytics and the AML paradigm shift, Stewart discusses:
• The new industry intrigue with artificial intelligence a
This white paper provides a top-level overview explaining what business analytics can do for your company - and the 8 key steps to accelerating product innovation, optimizing pricing and discovering drivers of financial performance.
The traditional managed reporting approach to BI is challenged to keep up with changing demand for business information. This research finds that providing business users with highly visual/interactive tools can help ensure they get what they need.