GEMS Data Model and Growing Global Business at the Speed of Trust

As technology and fraud techniques advance, businesses need to move beyond traditional fraud detection techniques and incorporate user behavioral features to boost accuracy. In this double presentation, Agoda and Booking.com will share how they leverage behavioral features to detect fraud in the travel industry. Agoda will explore the process of generating behavioral features, the technology behind incorporating them into machine learning models, and some of the techniques used to amplify the difference between fraudulent and non-fraudulent behavior. Booking.com will share their approach to detecting fraudulent hotel listings, which includes a multi-stage machine learning system that monitors the behavior of new hotels as they onboard onto the platform . The models monitor hotel actions from the early stages of signup up until the hotel is live on the website and interacting with guests.

Join dLocal as they discuss: 

The importance of BINs analysis to discriminate risk 

The importance of closer relationship between bank and merchants/psp to optimize revenue and CX

There’s a big gap in digital commerce. Merchants have very detailed data on their entire customer journey up until one of the most critical points: the payment. At that point, many are lacking clear visibility and real insight into their approval rates and other business metrics, often resulting in loss of revenue and higher total cost of payments acceptance. This is mainly due to unclear or insufficient payment data—payment data with which they could easily answer questions like Why are 15% of my orders declined and what could/should I do about that? Most merchants don’t have easy access to the answers.

Participants in the ecosystem such as payment gateways, payment service providers (PSPs), third-party fraud tool providers, BIN service providers, acquiring banks, card brands, and issuing banks (and APM schemes) must all come together in some capacity for a single payment to go through successfully.

All these layers between buyers, merchants, and approved transactions have different technology standards, formats, data fields and, most importantly, definitions of data and data fields—both in what is sent out (for approval) and what comes back (in response). It all adds up to a huge challenge for businesses to easily access and turn payments data (whether from one or many PSPs/processors) into actionable insights. How can you improve performance if you don’t know what to tweak or why? How can you monitor any changes over time?

All merchants have some form of blind spots in their payments data that can help them uncover and address costly issues or opportunities to improve. And what could be more relevant today—in an economy where companies, teams, and people are being asked to do more with the same (or less) resources—than finding ways to optimize your existing payment stack?

The dramatic shift to online shopping means retailers see their customers less than they ever have. Additionally, with daily data breaches and lax password security, account credentials are readily available for purchase.

This is a boon to fraudsters looking to pose as legitimate customers. So good in fact, that from 2019 to 2021, identity theft increased 81.8% globally. For too long, organizations have played defense and responded to suspicions of fraud reactively. It’s time to start establishing digital trust with customers early in the journey, just like retailers would do in the day when they knew their customers personally.

Identity is at the root of all fraud. Merchants that can safely trust their customers' identities will reap the benefits. First, they can greatly reduce the impact of fraud on the business. Second, they have more—and better—data for reducing customer friction, increasing loyalty and targeting sales or marketing efforts more effectively.

So how can merchants know for certain a customer is who they claim to be?

Traditional identity verification (IDV) solutions validate some customer attributes, but these are no longer enough to establish trust. To establish that an identity can truly be trusted, it’s imperative to take a more holistic look and verify the connections between that data and the person claiming to be a customer.

Professional fraudsters can try to impersonate legitimate customers, but they can’t create trusted identities. Even synthetic identities fail when exposed to this scrutiny.

This is why trusted identities offer a safer approach to IDV. By identifying trust, the company focuses less on behaviors and more on the individual making purchases.

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

Identity underlies every transaction. With rising levels of fraud, false declines, and chargebacks, it's time to rethink the concept of "identity" within the context of digital commerce. Pipl will share why the traditional concept of identity is failing merchants, payment processors, and ecommerce customers alike—and how it should change. We'll introduce the new paradigm of trusted identity, describe what comprises trusted identities, and provide real-life examples of how trusted identities are already being used to prevent fraud, combat chargeback and friendly fraud, and extend trust into the ecommerce lifecycle.

Learning Objectives:

• Understand the importance of deep connections between identity data fragments
• Identify trust signals and be able to recognize a trusted identity
• See how the concept of trusted identity is being applied across manual review, chargebacks, friendly fraud, and third-party fraud

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

Learning Objectives:

• Reduce new account creation friction
• Eliminate new account creation churn
• Identify potentially fraudulent new user activity
• Map a path to passwordless log in

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

Learning Objectives:

• Reduce new account creation friction
• Eliminate new account creation churn
• Identify potentially fraudulent new user activity
• Map a path to passwordless log in

It’s Time for a New Approach: Digital trust and safety is shaping up to become a critical, revolutionary issue for online organizations, communities and everyday users. The online world depends on being able to move forward with trusted interactions, which are only possible when you know who is real and who isn’t. The value of trust far outweighs the cost of fraud. Organizations that can successfully identify trust in online interactions will improve the customer experience and strengthen brand loyalty while extending customer lifetime value.

At the core of trust in online engagements is digital identity verification. And at the core of identity verification is identity trust. If a user’s identity can’t be verified, how can that user be trusted? Even with verified identities, it’s even more important to establish that the individual behind the identity can be trusted. This guide provides best practices any organization can follow to develop effective trust and safety initiatives to protect their platform and their users.

Putting digital trust and safety into operation is a daunting job. It affects everything from the development of online products to setting standards, deploying internal teams and enforcing policies. It’s also too big of a job to do alone. Implementing an effective digital trust and safety culture requires partnering with social, government, law enforcement and technology organizations. These third parties can share useful information and meaningful insight around risk and best practices.

KYC has its roots in the early 2000s in an effort to make it much more difficult for criminals to launder money through banks, insurers, and adjacent institutions, as well as to stop terrorism funding. However this approach has been made more and more difficult to help online businesses verify fraudsters.

Combined with social media lookup and strict KYC checks, we'll be looking at how we can implement better restrictions of fraudsters and help AML efforts.