- Payment & Fraud Editorials

29^th February, 2020

Ai Editorial: Astute infrastructure that facilitates capturing of real-time data and processing the same with minimal latency is key to setting up an apt risk assessment for legitimacy of transactions, writes Ai’s Ritesh Gupta  

A key factor in sharpening a merchant’s fraud risk assessment for transactions relates to data infrastructure and its scalability. E-commerce players need to excel in this area, and ensure all of it is streamlined so that the experience of a travel shopper isn’t hindered. It is about conducting the check for the legitimacy in a fraction of a second, so that the evaluation doesn’t adversely delay the transaction/ payment. Travel merchants must be adept at probing and investigating data in real-time to sense fraudulent transactions or any other anomalous activity.

Fraud detection specialists acknowledge challenges associated with the performance of digital assets and the significance of a scalable application.

 Some aspects that must be considered before looking at the infrastructure that support real-time fraud detection:

• Scaling is a bi-directional process: systems must scale up to meet increased demand and scale down when demand is low, as highlighted by SecuredTouch. Count on the proficiency of microservice workloads when it comes to scaling and automating.  The team at SecuredTouch chose to scale horizontally by replicating services across the cluster, or scale vertically by adding new nodes.

• Evaluate budget: While stepping up the performance is important, one shouldn’t forget that initiatives like switching from HDDs to SSDs, adding intermediate caches etc. also tend to increase  costs significantly, according to SecuredTouch.

• Processing data in real-time tends to be expensive and computationally intensive, so it’s critical to ensure the fields that are required in real-time are utilized for that objective, but the others are not. Also, here we are looking a real-time or near real-time processing. Then comes the methodological choice, and for this entities have to rely on the sort of data, its volume and the database.

Key infrastructure-related areas for fraud detection

The turnaround comes from having the capability to analyze data via cloud-scale data ingestion and real-time analytics. To garner and examine a huge magnitude of transaction data calls for a vigorous database component for storage and management. Plus, a large-scale distributed computing component for running algorithms is also mandatory.

Also, from infrastructure perspective, one has to do away with managing individual servers.

Streaming data requires a data architecture that can handle rapid input and on-time output with efficient data processing. At the core of the entire exercise is to bank on a query established in advance and the objective is to alter the input stream and evaluate it based on a fraudulent-transaction algorithm. And in case there is anomaly detection, the same is conveyed to the output interface.

Some of the infrastructure-related requirements when it comes to ingestion, storage, processing, and analytics :

• A major component is a real-time streaming platform and event ingestion offering, adept at processing a big volume of events per second.

• Another aspect is having the ability to pulling out relevant details from data streams to identify meaningful pattern and relation.

• Focusing on stringent security, for instance, how to deploy and manage cloud set up.

Key metrics, according to SecuredTouch, in this context are the time taken for a service to receive and respond to a request, and the time it takes to communicate with the end user.

In the whole exercise, the decisions that are related to right-sizing data, data processing method, the chosen database etc. are extremely important.

Keen on exploring fraud prevention and payment-related issues?

Check-out Ai’s conferences scheduled for 2020: https://lnkd.in/fE7UK_T

3^rd Februrary, 2020

Ai Editorial: Dealing with credit card decline codes is a daunting task. Ai’s Ritesh Gupta explores how a deeper analysis of these codes and collaborative approach can help in payment authorization.

Evaluating ways to improve upon approval rates for online card payments is always high on the agenda of travel merchants.

Independently travel e-commerce players are looking at ways to seamlessly authenticate users across the omnichannel customer journey. The role of cloud-based intelligence, backed by artificialintelligence and machinelearning, is coming to the fore. Assessment of both risk pointers and positive identity indicators is the way to go. This way travel merchants can better comprehend the context of a shopper, their behavior, and their score in terms of digitalidentity trust and risk. Other than ensuring that a legitimate shopper shouldn’t suffer owing to a wrong decline of a card, travel merchants also need to be in control of processing costs as well as focus on fraud prevention. There is no secret sauce for all this in the payment landscape, but crafting an astute authorization strategy is an ongoing effort that demands continuous introspection. Working with other stakeholders holds key here.

When it comes to authorization and acquiring for more than one market or cross-border transactions, a merchant can assess options such as  working with a payment services provider, setting up a local legal entity and entering into merchant agreements with local acquiring banks etc.

Coming to grips with soft and hard declines

Technically, credit card rejection happens when a card payment cannot be processed and the transaction is declined by the payment gateway, the processor, or the bank issuing the money. A credit card decline code is a message issued in response to a request for authorization during a transaction.

It is here dealing with the travel shopper in an apt way – via a simple and transparent communication – can help.

According to Chargebacks911, the issue is credit card decline codes are not standardized; they differ from one payment gateway to the next. They also tend to be rather unclear, as this helps in shielding the cardholder’s privacy and avoid giving away sensitive information in the event of a genuine fraud attack. Details pertaining to why a payment tends to get rejected can be provided by an acquirer and this in turn can boost the conversion rate. As Ingenico points out, even though the rejection or response codes offered by acquirers may appear dauntingly technical, it’s extremely useful to understand what they mean.

Adyen recommends that  profile of each transaction needs to be considered based on its amount, if it’s recurring, local regulations, issuers' authentication preferences, your relationship to your shopper, and more.

Some declines may be the direct result of the cardholder's actions while others are the result of external factors. The most important distinction is between “hard” and “soft” declines. A hard decline happens  when the issuing bank or processor denies the processing of the transaction and retrying the card won’t help at all. Hard declines are not recoverable at the time of the transaction. Whereas soft declines are generally a temporary issue. Retrying the provided payment method information may be successful.  One way to deal with such scenario is to automatically route selected failed transactions to a secondary acquirer for a “retry”. This can increase authorization with virtually no impact on the customer experience, asserts Ingenico. Essentially merchants need to constantly explore ways to salvage such situations.  A partner should be adept at analysis of past declines, transparent data, ongoing analysis of global transaction types etc. Also, developments like PSD2 are all about more carefully processing and managing data, including payment transactions.

PSD2 SCA 2020 - how to go about it as a travel merchant?

Not just merchants

And it’s not just merchants, but even other stakeholders, including card schemes and issuers, too, are focusing on sorting some common issues that tend to block transactions that simply should not have failed in the first place.

Traditional companies are stepping up their efforts  in the wake of increasing competition from alternative form of payments plus new developments that are fueling emergence of fintech digital payment specialists. For instance, it is being acknowledged that as a vital link in the payment chain issuers need to share relevant details regarding why the transaction has been declined. Many tend to supply response codes that are ambiguous and tough to comprehend. And in certain cases such codes cannot be interpreted at all. Effective fraud prevention and detection requires real-time collaboration and data sharing. In fact, with a collaborative approach where data on fraudulent and suspicious transactions is shared (and keeping it anonymous, too, where required), details are out on new fraud attempts no matter where they first appear.  But all of this demands a diligent effort. For instance, considering the case of passing SCA or Strong Customer Authentication  messages through complex transaction flow in the travel e-commerce sector.  

It is imperative for merchants to work collectively internally (fraud and risk management, customer service, operations, technology and product management teams) to optimize authorization and fraud strategies, and work with various external stakeholders as well for the same.

Keen on exploring fraud prevention and payment-related issues?

Check-out Ai’s conferences scheduled for 2020: https://lnkd.in/fE7UK_T