Ai Editorial: Role of real-time data in payments optimisation comes to the fore

First Published on 23rd November, 2017

Ai Editorial: Big data and real-time machine learning is being counted upon for securing payments as well as protecting user accounts and monitoring loyalty miles claims, writes Ai’s Ritesh Gupta


The role of data in stepping up the conversion rate and curbing fraud is coming to the fore.

The traditional ways of removing pain points of shopping as well as managing fraud have largely been reactive measures. But, with the availability of relevant, real-time data, a more proactive approach is improving efforts in this arena.

1.     Sector-specific analysis: As e-commerce entities, airlines need to dwell on sector-specific data analysis, for instance, gaining understanding of the user profiles that shop on Specialists recommend that specific data fields such as loyalty miles claims can be assessed to check for any irregularity. Similarly, the words per minute typed, the movement of the cursor around the site etc. is being evaluated, rather than only focusing on the card blacklist. Real-time data from can also help in curbing fraud. Blacklists rarely work because hackers will never use the same credit card information twice, while white-lists are inaccurate since white-listed customers can be compromised anytime. Real-time machine learning can help against blanket blacklists and white-lists by focusing on the customer’s behaviour instead. It works with real-time live data collected on the merchant’s website, where the system trains itself with each incoming transactions to identify fraud patterns instead.

2.     Authorization rates: Among the other areas, data is being relied upon for improving upon the authorization rates.

As highlighted by Adyen, on average, 5%-15% of ecommerce credit card transactions are rejected by issuing banks, and out of these, a quarter don’t work due to shortage of convincing reasons, mostly due to old and inefficient systems. And in certain markets, authorization rates across issuers take a dip because of suspicion of fraud. In this context, it is imperative to bank on data to evaluate the main reasons behind those declines and take appropriate initiatives. For instance, one areas that could be looked upon is - issuer-specific authorization rate trends. These actions may include optimizing the type of data submitted or identifying optimal routing for a given transaction.

3.     Evaluating the next buy: Adyen has also indicated that it is gearing up for shopper-centric reporting and this would help in analysing the next buy, and when and how the purchase will be made.

4.     Data from multiple sources: Other than unique merchant data for airline-specific analysis, travel e-commerce players can also capitalise on industry-level data. This could be details about synchronized fraud incidents, which may be shared across various carriers as all of them are equally susceptible to coordinated hackers/ fraudsters. Industry data on existing or current fraud attacks can also be useful information to share from airline to airline, but both types of data should be collected for analysis of anomaly detection. In fact, the way various sectors have shared data to control payments fraud, the same is gaining traction for a relatively new malice - loyalty fraud. This is important as hackers or cyber criminals have shifted their focus to loyalty fraud. The plan is to spot loyalty fraud patterns and potential fraudulent loyalty transactions. The fraudsters are leveraging loopholes as seen in the case of data breaches featuring even established airlines. So be it for loyalty or any fraudulent transaction, the more data that is collected, analyzed and linked, the more likely airlines and other merchants can avert the danger. It is quite possible for offenders to use stolen credentials across multiple merchants.

5.     Only historical data isn’t enough: It is time to look beyond traditional machine learning that tends to only rely on historical data for training the system. So limitations of acting on previous attacks have to be ascertained. Since supervised machine learning creates probability scores for each transaction, this means this method results in manual reviews as well. Due to the need for manual reviews, rules-based systems also start to show cracks at high volumes, and curtail an airline’s ability scale on demand.  On the other hand, the promise of unsupervised machine learning, too, needs to be scrutinised closely. It lets the system learn on the fly with real time data collected. 

Specialists recommend that airlines should take control of their payment data, which should not be restricted by default. So closely look at the country, industry, and type of device that is used, and cater their payment offering accordingly.

This data can merged with big data, so that organisations can work out a robust data strategy for curbing of fraud, analysing user behavior to assess the overall shopping pattern etc. Also, by working on their own fraud tools that are able to capitalize on their own sources of data, airlines can even challenge the efficacy of existing mechanisms. For instance, being realistic with Dynamic 3DS, the same is controlled by card issuers and is therefore still working with the same set of data as before. They are unable to tap on the merchants’ data for more information on fraud. But armed with their own data, airlines as merchants can improve upon their situation. Airlines need to update their fraud management systems with information from both internal and external sources, including chargeback data, information traded on the dark web etc.

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