Spiralling interest rates, high energy prices and economic uncertainty have put a dent in consumer confidence.
The flow-on effect has impacted online retailers and changed the landscape. As we all adjust to the ‘new normal’ of market conditions, the fundamentals of online retail haven’t changed, but the stakes are higher.
PayPal’s Evolution of Commerce research shows two-thirds of Australians (66%) are afraid of a potential recession, 80% are being financially cautious and more than half (52%) have reduced their discretionary spend. Against this backdrop, delivering a great experience remains critical, while building trust and offering flexibility have never been more important.
Offering Payment Flexibility & Personalised Choice
In the current environment, it’s likely that consumers may, from time to time, feel a greater need to spread their purchases across multiple funding sources. For example, they might have maxed out one credit card and want to charge a purchase to their bank account or vice versa. Many merchants don’t have a transaction flow that offers their customers an easy way to try the purchase again, with a different funding source, without them having to restart the whole purchase.
Additionally, having a purchase declined because of insufficient funds may be disheartening or frustrating for consumers, leading them to abandon the purchase or try another retailer. Offering a low-or-no-friction way for consumers to retry a purchase with a different funding source may increase engagement and decrease lost sales. Digital wallets, such as PayPal, that contain multiple funding instruments (such as debit card, credit card and bank account), allow consumers to easily retry a different funding method if one fails and can help minimise declines due to ‘insufficient funds’.
Offering a pay later solution, such as buy now, pay later (BNPL), is also an option that consumers may want to use to spread out larger purchases and help manage their budgets – particularly younger consumers who may not have or want a credit card. While not all consumers use BNPL, it’s an option that’s important to offer to ensure you’re providing an adequate level of choice and flexibility in payment methods.
Optimising Authorisation Rates
Payment flexibility and choice must be balanced with security. ‘Card not present fraud’ accounts for 91% of all card fraud in Australia, costing the eCommerce industry close to half a billion dollars annually.
Optimising your authorisation rate is all about stopping fraudulent transactions without turning away legitimate customers. If your fraud rules are too tight, you’ll turn away good business and if your systems aren’t smart enough, they can let bad transactions go through.
Authorisation rates don’t sound super exciting – but if you get it wrong, it can hurt your bottom line needlessly. Conversely, even small improvements in authorisation rate across your total sales volume can yield big results.
The fraud prevention solution your business needs to optimise authorisation rates depends on the size of your organisation or the trajectory of growth that you are on. Therefore, it can be effective to look for fraud protection partners who offer tiered solutions that tailor to your business needs.
Smaller retailers and SMBs do not have the resources to hire risk teams to build sophisticated strategies and customise their own rules. In these cases, fraud protection tools that have a fixed set of rules, can be setup easily and are affordable, are often preferred. Whereas larger companies with more established risk teams often require custom tools that can match the complexities of their organisation. Whereas larger companies with more established risk teams often require custom tools that can match the complexities of their organisation, and through automated Machine Learning based technologies, learn and adjust these rules to meet a business’s changing needs
It’s important that you partner with a fraud prevention vendor that fits your current requirements and can scale to your needs.
Balancing fraud prevention with revenue
Consumers, now more than ever, demand a seamless and safe online experience and may abandon shopping carts when faced with too much friction, including friction intended to stop fraudsters.
Satisfied customers are more likely to become return customers, and it’s important to quickly differentiate legitimate customers from bad actors. Technologies are available to make that distinction automatically and invisibly, and drive customer satisfaction by ensuring ‘false positives’ don’t stop the vast majority of legitimate customers from purchasing.
False positives occur when a fraud engine thinks a legitimate transaction looks ‘wrong’ and rejects it and can lead to good customer orders being lost to competitors. For example, a legitimate customer, with a legitimate credit card, might place an order from a device they don’t usually use, or from a location/IP address they haven’t used before. This could trigger less sophisticated fraud systems and a legitimate order could be rejected.
Accuracy is key. Technologies today can quickly give analysts the capabilities they need to make more accurate decisions on transactions. At PayPal, our graph-based case management dashboard allows retailers to visually depict how transactions are linked through shared attributes. This means retailers can better analyse and understand transactions as well as automatically approve, decline or flag transactions based on unique filters. These dashboards also allow for the creation of specific custom fields, such as a store number or SKU, which can be applied to filters.
Rich data and machine learning
Getting authorisation right means reducing chargebacks, lessening friction at checkout, and increasing conversion with customer satisfaction. Outside of transactions, it also improves brand reputation and reduces manual processes.
The biggest value-add when it comes to tapping into the right technology to support your risk management strategy is the substantial amount of historical transaction data from which machine learning and artificial intelligence can develop unique risk rules and algorithms.
Using machine learning, risk prevention systems can detect fraud before it happens and reveal the risk before bad actors can achieve their goals — so merchants can decide whether to accept or reject a transaction within seconds. For example, “device fingerprinting” uses characteristics about the software and hardware of remote computing devices to identify unique devices and track the behaviour of devices that access a website or application. Device fingerprinting doesn’t use personal information but allows for returning devices to be verified in real time by their unique configuration characteristics. If there is any correlation or pattern between the device and fraudulent behaviour, this can result in a high-risk score and the transaction will be rejected.
Flexibility, Choice and Safety
In a tough economic climate, it’s important to give customers flexibility and choice of payment options to meet their needs at the time. This includes offering an easy way to retry a different funding option as well as payment options such as BNPL (preferably with low or no consumer fees or interest).
However, choice and flexibility must be balanced with safety. Australian consumers have seen numerous large data breaches over the last few years and are wary of providing online retailers with non-necessary information. Your checkout needs to be optimised to reduce the amount of data a consumer needs to input – such as address information that can be pulled through from their existing digital wallet.
When every cent counts, it’s important to optimise your authorisation rate and reduce the risk of fraud while allowing the maximum number of legitimate transactions to proceed. There are tiered fraud protection offerings out there that provide solutions for diverse needs under one roof. Look for the solution that works for you both now and that will scale up as needed in the future.
Fraud prevention technologies that leverage large historical data sets to inform machine learning and artificial intelligence will naturally make more informed and accurate fraud decisions. There are lots of out of box solutions out there with machine learning built in – but it is the richness of the data set that can make the most difference.
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