The APAC region is notable for the explosive growth of its fintech sector. While the most staggering examples are the rise of super apps, such as Grab or WeChat, unique circumstances have shaped tech in the region.
During this webinar, we set our sights on the future with our special guest, Stephen Su, Head of Solutions at GBG. In this wide-ranging session, you can:
- Learn more about the operational differences between neobanks and legacy players.
- Understand the risks & opportunities of an underbanked population.
- Get tips and tricks on how to prepare for the key fraud trends, market landscape shifts, and upcoming laws and regulations.
- Learn how to prepare for the arrival of Gen Z, the new generation of consumers.
- Discover the unique challenges in the region.
- The Fintech Boom in APAC
- Trends to expect in 2022
- Risks of the Underbanked
- What’s Special about APAC?
- Upcoming Challenges
What makes the APAC such an exciting region for fintech growth?
Innovation to commercialization is only getting faster, along with the adoption of technology with more people in Southeast Asia now having a mobile phone (73%) than a bank account (50%) and the number of mobile phone users is estimated to grow to 80% by 2025. It’s particularly exciting times because we have the tools to solve banking exclusion. With 60% of the popular in Southeast Asia having no bank account, it’s a huge opportunity for fintech companies to capture this market. The main challenge is how they onboard these customers safely and quickly with minimum risk. – Stephen Su.
Financial inclusion is important as in the west, for example, it’s basically mandatory to have a bank account as if you aren’t accepting your wage via a traditional bank account, immediately there’s a level of risk for fraud managers. However, what we’re seeing in the APAC region is this push for financial inclusion coupled with the fintech explosion. This puts companies in a difficult place because if someone is underbanked, they will likely have a thin profile or next to no paper trail so as much as it’s an opportunity, it’s also a lot of risk. – Gergo Varga.
What are the best practices to onboard unbanked & underbanked users?
From our experience, fintechs need to have a layered approach when onboarding the unbanked & underbanked. For example, if we were onboarding someone that hasn’t got a credit profile you can leverage a lot from their device to run an alternative credit risk check. You can also use data such as the digital footprint, facial liveness check to make sure that the person behind the mobile device is who they say they are against their ID cards. Taking this layered approach, understanding the many data points you can uncover, and using technology to support you is the safest way to onboard such users. Infusing a whitebox machine learning model can also help distinguish the good from the bad with a clear explanation as to why a decision is made. – Stephen Su.
The most pain-staking part of working in anti-fraud is trying to train yourself to spot patterns but now we have sufficiently advanced machine learning models that a human might miss. Tailoring your risk roles to spot these saves you the headache of picking out those tricky. At this stage, for any high transaction volume company, it’s almost mandatory to use machine learning otherwise you’re going to suffer. Similar to what Stephen said, if you’re worried about customer insult rate and if your fraud team handles anything customer-facing, you need whitebox machine learning to overrule a decision with confidence as you can understand better where the mistake was. – Gergo Varga.
What can be done to mitigate threats surrounding emerging fintech services?
Start with quick and agile products to suit your business model, taking into account the importance of data. With the growth in fintech, there are many vendors available so take your time in deciding the best solution. As well as this, ensure that fraud & compliance are about your initial design plans. Thoroughly planning out what assets are needed with consideration of future regulatory requirements is key over the longer term. For example, with the wealth of data a BNPL company is working with, don’t just look for fraud could you use this data for credit risking and vice versa? Data is king so orchestrating this with a clear idea of what your future plans are from the off can extend the use cases of your data as you enter new markets or grow into other services. – Stephen Su.
SEON has a clear approach in that we believe you should be looking for risk across the entire customer journey so not just looking at sign-ups and transactions but every that happens in between can be key pieces of the puzzle that is fraud. And this is can be done through the use of alternative data, instead of relying on property databases. Take the APAC region, if you have a mobile-first user preference, they will be using a lot of digital services from the get-go. Being able to trace their digital footprint acts almost like a soft credit check for online lending. If your customer is a loyal Amazon and Airbnb customer that’s a good signal, as opposed to a fraudster who might be able to source ID cards but can’t replicate a complete digital footprint. If you know these services trust this person, and the more platforms they’re present on, you can take more confidence in any decision making. – Gergo Varga.