According to Signicat, 40% of consumers have abandoned new account applications before completion. Of these consumers, almost ¾ said this was because the process took too long or required too much information.
Banks must adopt a new approach that prevents AML non-compliance — or else risk reputational damage, significant fines, and increased regulatory scrutiny. Luckily, one critical gamechanger can clear the path for banks in their fight against money laundering.
Download this eBook to learn how machine learning enables you to:
- Overcome your money laundering challenges
- Consolidate data to get 360-degree views of transactions
- Predict your customers’ immediate needs and behavior
- Save your team countless hours
What capabilities can banks adopt to efficiently cut friction, acquire more customers, and mitigate risk? Find out here.
Download this eBook to learn how to:
- Troubleshoot your account opening challenges
- Increase customer acquisition with tailored workflows, customizing the account opening process based on risk assessment
- Use machine learning to onboard the right customers with operational efficiency
Current anti-money laundering solutions rely on techniques that generate excessive false positive rates which require burdensome manual reviews.
Legacy money laundering solutions cannot keep pace with the increasingly sophisticated layering schemes, as well as growing compliance requirements from regulators.
Can your bank/financial institution successfully navigate the growing regulatory demands? Is your bank equipped with the newest digital and AI based tools to stay ahead of the new global trends in money laundering?
Read this Ebook to understand:
- The key challenges and constraints arising from money laundering that impact banks’ bottom-line
- The use of AI platforms that enables banks to drastically reduce false positives while accurately detecting increasing incidents of suspicious behavior
- AI concepts of advanced profiling and real-time processing capability that reduce the cost of AML processing, detect substantiated money laundering risk and report it in time, with great detail.
As challenger banks challenge the status quo with completely reimagined user experiences, they unknowingly open themselves up to numerous risks. On the surface, these risks may look harmless. However, as they continue to gain traction, these risks could prove fatal.
Read our new Ebook to understand:
- How challenger banks can prevent reputational damage caused by financial crime
- How machine learning solutions can help combat evolving AML strategies
- What challenger banks should be looking for in a machine learning solution as they fight financial crime
Member behavior is changing in front of our eyes. Are you ready?
Read our new Ebook to understand:
- How new developments in fraud and financial crime prevention technology arm credit unions in the battle against bad actors
- How to leverage omnichannel interactions to retain members, while encouraging new members to join
- How artificial intelligence plays a key role in future growth, and how it can encourage new member acquisition
- The risks that these changes bring, and how to effectively mitigate them
Banking’s digital transformation is here. Are you prepared?
Banking customers are evolving from omni-channel to digital-only, with 88% of all banking interactions expected to occur on mobile by 2022. With this massive shift in customer behavior, banks face numerous risks.
How do you prepare for digital transformation without increasing your risk?
If you’re waiting until checkout to make risk calls, you’re waiting too long. Fraudsters are leveraging attack vectors at every stage of the ecommerce journey.
Transactions are points in time. How can merchants see beyond these points, and build complete storylines of truth for the lifecycle of an ecommerce user?
The problem is that the most advanced fraud-fighting vendors often constrain your data scientists with singular data science environments and proprietary frameworks.
That’s why Feedzai built the OpenML Engine. We believe your data scientists should have the flexibility to build models in any language, using any library, and on any platform. They they should be free to import these approaches to a platform that was purpose-built, from the ground up, to fight new and evolving financial crime.
The PSD2 transformation promises to make life better for customers. But will these changes come at a cost?
As PSD2 disrupts commerce, it threatens standard fraud prevention strategies too. We all know that fraud evolves constantly, and without an established risk plan to guide you through the new world of open banking, your organization is exposed.
How to fight fraud, increase business and stay competitive
It’s no secret that our push towards technological innovation in digital payments has made fraud a clear and present danger for acquirers. According to the Association of Financial Professionals, 74% of organizations have experienced attempted or actual payments fraud in 2016.
Retail banks face unique challenges at the point of account opening. There’s a “thin-file problem” due to gaps in historic data, and bad digital experiences as customers travel between silos. It’s no surprise that 74% of financial institutions stated having multiple independent projects underway to improve customer experience.
What’s the current state of explainability in machine learning for fraud?
In this ebook, read about the critical need for an AI system that can share its thought process with us in perfectly human terms, so we can transform machine learning into machine teaching.
Read this free 11-page report and get actionable insights to help you create a strategy for taking charge of the AI disruption that’s well underway.
Download this report to peek into the future of fraud. Learn more about precise segment-of-one profiling, explainable whitebox AI, better enterprise fraud protection, and how to future-proof data ingestion.
Download this report to peek into the future of fraud. Learn more about how merchants can use explainable Whitebox AI, precise segment-of-one profiling, Future-proof data ingestion, and better enterprise fraud protection.
Download this E-book to learn: The Four Types of Risks in Online Accounts, An Omnichannel Risk Prevention Strategy, and An Omnichannel Machine Learning Solution.
Learn how to eliminate high maintenance rules based engines, reduce false positives & make better decisions with machine learning basics.