Using Machine Learning To Stop Fraud in Real Time
Learn how machine learning can be used to detect and block fraud during the Spotify Checkout process from data scientist Cicely Robinson.
In this presentation, Cicely will explore the complexities and challenges of the problems encountered in this space, while also discussing the modelling techniques and the data requirements needed to build an effective fraud mitigation model in real-time.
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This MRC Virtual 2022 presentation highlights risks mitigation strategies, the types of fraud merchants may encounter, and provides useful insights into current and future regulations in the crypto space.
This highly educational presentation from subject matter experts explores this process in detail, and is a must read for anyone interested in implementing crypto or the blockchain into their payments stack.
FIDO is a standardized authentication protocol used to strongly authenticate a cardholder on their device, without relying on passwords or one-time passcodes, and for a new digital world with modern regulations, FIDO may be the answer.
FIDO can be used with EMV® 3-D Secure and Delegated Authentication to provide a secure, user-friendly way for the European payments industry to meet PSD2 SCA requirements. While several methods comply with SCA, FIDO is the only one that checks all the boxes: user convenience, PSD2 SCA compliance, security, scalability and so much more.
This presentation demonstrates how FIDO may offer better PSD2 SCA compliance, and how it may help more effectively optimize online payments for European merchants.
Many - if not most - enterprise retailers are overpaying for fraud solutions and chargebacks, as well as losing out on millions of dollars in sales because of suboptimal fraud strategies. Preventing fraudulent purchases from being approved is the most important goal of any merchant’s fraud strategy, but it also poses a dilemma when a good customer’s legitimate transaction is blocked. Eradicating false declines and getting as many good customers as possible through check-out is challenging, but can be optimized through productive, data-driven discussions with the payments supply chain to rectify any issues and, ultimately, supercharge approval rates.
In this presentation, payments experts will showcase the importance of benchmarks and data-science in helping merchants not only assess, but also scrutinize the performance of their fraud suites. It will also walk through what a data-driven supply-chain engagement strategy can do to ultimately boost digital sales.
Ultimately, in the world of payments, success depends on human factors, like how consumers perceive and respond to risk, reward and effort.
Against this backdrop, Token surveyed over 1,000 people across Europe about the attitudes, preferences and behaviours shaping their financial and digital lives.
Token presents: "Who Will Pay by Bank" a data-driven look at the human element that will fuel the future of open banking payments.
A glimpse into this report:
- Learn which consumers are paying by bank today and where will we see demand tomorrow
- Discover the behaviours and opportunities that could support continued uptake of account-to-account (A2A) payments
- Understand how consumers in Europe perceive the benefits of A2A payments and other payment methods on a country-by-country basis
- Uncover how consumers understand open banking's evolving role in their lives
- Read commentary from the Open Banking Implementation Entity, Open Banking Expo, American Express, Ban
False positives down, revenue up! Learn from an Experian fraud expert how machine learning strengthens fraud prevention, reduces false positives, and leads to new revenues.
Fraud prevention is one of the most exciting areas in commerce. However, it is also challenging to core business functions. This webinar will present how modern fraud prevention becomes more efficient through machine learning models and how this results in new revenue potential.
By participating in this webinar, the learner should be able to understand:
- State-of-the-art fraud prevention methods and current fraud figures
- How machine learning supports fraud prevention
- Why companies should rely on smart fraud prevention with machine learning models
We have met with many cases where analysts not only doubt themselves in their decision but also discouraged to take information into account that may not be considered as useful – not to mention the rise of Machine Learning in fraud prevention. Is our work that we do manually truly outdated?
Through examples we will re-explore the tools that we have at our expense and discuss how we can effectively use them in relation, from articulating sentences, to defining use cases and relying on AI.
- To find or regain the interest in the beauty of fighting fraud – making decisions that would be the key to stop the malicious activity yet keeping the mind open to changes.
- Utilising every aspect of the tool that are available and considering ways that we may have disregarded before and still could be useful, ultimately a boost for the year 2022 and to remain on the top of defense against harmful users.
Personal finance and investing platform FinTron caters to Gen Z—and that means meeting young users’ expectations for fast, seamless experiences. However, FinTron’s original new-account authentication process took up to 48 hours. That’s a very long time for eager new investors to wait for approval—and plenty of time for them to find an alternative app. In the early stages of implementing Deduce Identity Insights, FinTron can now access and approve qualified users in near real-time, so they can start using FinTron’s platform right away. Deduce achieves this by scoring applications as part of the signup flow and alerting FinTron’s manual review team to any that might be fraudulent, saving the team from having to manually review every new user signup. Hear from the FinTron founder and CEO, Wilder Rumpf, on how he and his team implemented a Trusted User Experience that delights his customers
- Reduce new account creation friction
- Eliminate new account creation churn
- Identify potentially fraudulent new user activity
- Map a path to passwordless log in
We all know that merchant acquirers and payment facilitators are responsible for customer chargebacks caused by fraudulent sub-merchants. Some people in payments processing and fintech think of this kind of fraud as just the “cost of doing business.” In reality, it can mean the difference between being profitable or going out of business.
Fortunately, there’s an additional set of data that can be easily integrated into your onboarding process to reduce the number of fraudulent sub-merchants making it onto your system: advanced location signals. In this white paper, GeoComply shares how fraudsters easily manipulate IP addresses to fake their location, and how using modern geolocation data to determine a user's true location improves efficiency and confidence when onboarding new sub-merchants.
This whitepaper begins with a snapshot of PSD2 and summarizes the directive's main changes, focusing on SCA. After highlighting how SCA will affect financial services companies, the paper examines how biometrics can meet SCA requirements while avoiding unnecessary friction. The document concludes with an overview of Nuance's biometrics solutions.