The explosive growth of online gaming over the past 18-24 months presents many companies with some burdens of success — namely increased exposure to risk and fraud. But combating fraud in online gaming is a bit tricky. There’s a delicate balance to be struck between fighting overall risk and maximising acceptance rates.
Gaming platforms expect new player acceptance rates to exceed 80%, but often see rates below 50%. It’s not about leaving money on the table, but whether money reaches the table in the first place.
So, what’s causing this gap between fraud prevention and acceptance rates? It’s largely due to the complexity of risk decision-making and reliance on broad, catch-all models that decline too many legitimate users. A “less is more” approach, focused on targeted and refined risk modeling, will help (and is already helping) gaming platforms increase acceptance rates and improve customer acquisition, while simultaneously preventing fraudulent transactions.
The problem with saturated risk modelling
Account-to-account payments allow players to transfer money directly from their bank account to their in-game account, also enabling eligible banks to receive instant payouts. The solution satisfies the near-80% of gamers who prefer instant withdrawals. It also enables real-time risk assessment, but many providers aren’t building the right models to effectively balance risk and acceptance. Many solutions measure success by the number of parameters for their models. They oversimplify risk, creating rigid rules that result in lower acceptance rates for legitimate users. Others try to leverage credit data or impose basic transaction caps, but these approaches rely on correlative data rather than directly addressing the complexities of bank transfers. This leads to lower conversion rates and weakens bank payments as an acquisition funnel for merchants.
The role of guaranteed ACH
Chargebacks are incredibly common in online gaming because players are more likely to file disputes to recoup their losses. In fact, buyer’s remorse is the most common type of dispute in Daily Fantasy Sports. And there’s a 28% chance a player will dispute again if they can do so the first time.Guaranteed ACH offers gaming companies protection from returned or disputed payments. The provider shoulders the risk, allowing instant, guaranteed access to funds for merchants.
But not all providers are the same, and many have limitations that merchants aren’t aware of up front. Many guaranteed ACH providers implement additional verification steps and criteria to fulfill their “guarantee.” This filters out legitimate transactions and has lowered the industry’s average acceptance rate for guaranteed payments to a mere 60-70%.
The limitations of (some) A2A payment providers
In theory, A2A payments are more affordable for merchants than card payments because they avoid the high costs of interchange fees. But this isn’t true for providers that impose added fees on returned transactions alongside the cost of the return itself (which is common for some of the largest players in this space). Additionally, not every A2A provider has the functionality to track users across multiple merchants — meaning each interaction creates a new user ID in their system. When a user enters a new platform for the first time and links their checking account, there is no prior information about this individual’s financial habits. This fragmentation limits the ability of payment processors to accurately predict returns, meaning legitimate transactions are blocked and acceptance rates are lowered.
The Solution: Striking a Balance
An effective risk management system uses AI and machine learning to continuously adjust based on user activity across various merchants, industries and behaviours. Rather than simply guaranteeing certain ACH transactions, a balanced approach is used to minimise returns while improving acceptance rates. This type of model can adapt to the complexities of account-to-account (A2A) payments, managing more than 50 return codes, predicting available funds at the time of a transaction and identifying dispute patterns.
With a 95% approval rate, this approach helps enhance conversion rates and user satisfaction while preventing fraud and reducing return fees.
