First Published on 25th January, 2018
Ai Editorial: Companies can defend themselves adequately by using a tool like machine learning, and at the same time there needs to be reliance on rules and the human component as well, writes Ai’s Ritesh Gupta
Data breaches and compromised credentials are on the rise, and the task of a Chief Security Officer (CSO) or Chief Information Security Officer (CISO) is becoming more challenging to safeguard against takeover of loyalty accounts.
According to a recent study by Connexions Loyalty, travel accounts could be quite valuable on the dark web (airline loyalty accounts: $3.20-$208 each).
As Sift Science highlighted in one of our recent articles, in most likelihood, every one’s credentials have already been compromised, and it is imperative for e-commerce companies to strengthen the “authentication” aspect, and damage can be controlled as far as account takeover (ATO) or gaining access to a loyalty account is concerned.
And one of the main tools for the same today is machine learning.
Kevin Lee, Trust & Safety Architect, Sift Science says finding unknown unknowns is a key to making machine learning powerful. “If you are creating a rule, it is typically being created because there has been a mishap in the past. So rules are created with certain parameters. It is very tough to create one-off rule – say number of clicks on a particular item, over $100, with a particular contact number, email id and block it or allow the user to redeem it, then one can get buried in such circumstances and gets difficult to figure out the performance. The trouble with that is fraudsters are literally being financially incentivized to reverse engineer those systems. In the case of machine learning, it creates a more complex scenario making it more challenging to reverse engineer.”
Lee, a speaker at the recently held Loyalty Fraud Workshop in Palm Springs, California, added that machine learning can look at the entire span of an account and look for anomalies. A human analyst’s capabilities are restricted, evaluating a certain number of signals at a time and come up with a verdict. “But there is enough data out there and that’s really when machine learning comes into play. With thousands or tens of thousands of members in a loyalty program, machines become smarter and identify anomalies (in usage of accounts or user behavior).” So by identifying anomalous areas within large data sets, one makes intelligent judgments accordingly.
Efficacy of machine learning
Companies can defend themselves adequately by using a tool like machine learning, and at the same time there needs to be reliance on rules and the human component (intervention and feedback) as well. “All of this works together in conjunction to deliver the best results,” said Lee. Other than putting in place strong measures for authentication (related to accessing accounts), Lee recommends that there needs to be analysis to assess whether there is any problem with the system yet. What is the current level of account takeover on the platform? “What sort of data are companies tracking and measuring? And this isn’t related to fraud or ATO purposes, but in general. So many organizations don’t have grasp over their own data. So it becomes tough to assess how big the problem is. So the first area that needs to be assessed is around data quality and data volume in terms of how clean that is,” he said. Once a virtuous data pipeline is in place, it can be built upon with machine learning models, with rules, and create tools to help the team analyze the ATO problem.
Crafting a holistic picture
How about data from airlines specifically? Lee said this is a crucial area. There are signals that fraud prevention specialists lookout for. And this is just not related to transactions, but also about buying pattern, post booking behavior etc. With the data collected, one can churn the data through various permutations and combinations to identify potential fraud patterns that may be left behind by fraudsters, who have made micro-changes between transactions in one coordinated fraud attack to trick the system. Using real time pattern recognition, even micro-changes can be proactively identified and tagged to the same fraud pattern group.
The data that Sift Science leverages includes attributes associated with the identity of a user, behavorial (browsing patterns, keyboard preferences etc.), location data, device and network data, transactional data, decisions (business actions taken), 3rd party data (geo data, currency rates, social data etc.) plus custom data that is specific to a particular merchant.
A couple of examples:
· On-site behavior: Site data including mouse cursor movements or every single step of that journey is collected and analyzed to reveal insights into users’ traits. It can all be relevant information collected and used. “With enough data it can be observed that the average person – when they redeem gift cards or loyalty points, most likely that’s not their first time. People tend to take their loyalty program or points/ miles seriously. Even before the transaction takes place, with machine learning one can map the holistic behavior. So one keeps on checking a particular redemption option and when they have enough currency, they go for it. It might take them months to complete this. So these are all good indicators. On the other these are missing in account takeover (instances),” said Lee.
· Post transaction behavior: So let’s say if a ticket from an airline or an OTA has been bought or redeemed, a legitimate user can email the same or share itinerary with their family or friends. “But in case of a fraudster this generally doesn’t happen,” said Lee.
“A city pairing, time of the day, seasons…there could be a flight booking that might be risky, and another might not be risky at all. So a combination of factors can come into play,” said Lee.
The team has also worked on a set of capabilities that enables one to build custom fraud processes with less code.
Types of machine learning
The power of machine learning is still in the supervised state, asserts Lee. Typically, supervised machine learning focuses on a cycle of training, predicting, and acting stages. “(The industry) is still sometime away from functioning in an unsupervised way,” he said. When you have humans involved or there are known “bads” such as chargebacks, the system can learn quicker in such supervised environment. “Unsupervised machine learning tends to be less accurate (in comparison). It is lower maintenance of course.” Sift Science uses an array of predictive models, including ones specific to a business plus network models because spotting bad behavior on one site helps to identify it on other sites as well.
As for not being vulnerable to new types of fraud attacks, companies like Sift Science look at how fraudsters are trying to break existing system controls and rules. So with reference to finding a way to attempt a fraud via email id or address by to circumventing the controls enforced, data normalization coupled with n-gram analysis extracts the key substrings in the data field to identify repeatable data patterns. And that’s one example of how machine learning plays it part.
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The problem of what to do with those coins in circulation in Australia that may now be surplus to requirements is compounded by seigniorage. Seigniorage is the difference between the face value of the coin or the banknote and its production costs. In the case of the RBA, the issuance of a new banknote leads to a liability being raised on its Balance Sheet in case that banknote is returned, and the seigniorage held as an asset to help fund (at least part of) the potential repurchase of the banknote; hence the RBA should be relatively ambivalent as to whether “excess” banknotes are returned to it or not.
In the case of the RAM, the issuance of a new coin into circulation sees the seigniorage booked as a profit for the enterprise, as in a normal manufacturing business: Revenue (face value of coin) less Cost of Goods Sold (cost of coin production) equals Profit (seigniorage)
Hence the RAM is potentially “reluctant” to the concept of taking back “excess” coins due to the loss that will be incurred on its Income Statement (with a commensurate outflow of funds), as it will need to pay face value for each coin and the coin’s metal content is almost certainly worth a lower amount. This understandable lack of interest by the RAM in “repatriating” the surplus coins is therefore likely to see a build up of coins held by the commercial banks around the country. One could surmise that, as the commercial banks’ investment in this unnecessary and unproductive working capital of surplus coins grows, the commercial banks will begin to energise requests to the Department of the Treasury (to which the RAM reports) for a “buy back” - albeit one which is likely to see the RAM generate a loss. In the meantime, a period of stalemate might occur until this pressure builds.
With Australia leading the world in the adoption of contactless card payments at retail (in terms of the number of transactions per adult per year), which have been very potent at eroding the use of cash, the experience of the RAM over the next few years in handling the surplus of circulating coins will be watched closely by many other Mints around the world, who may themselves be in a similar situation before too long.
First Published on 9th January, 2018
Ai Editorial: Merchants and fraud prevention specialists need to evaluate several areas such as data breaches, phishing, malware etc. to make it tough for fraudsters to gain access to a loyalty account, writes Ai’s Ritesh Gupta
Airlines need to prepare diligently for the threat of account takeover or ATO, especially considering their business falls in the “high ticket value, with a low margin” category.
Why ATO is proving to be lucrative for fraudsters at this juncture?
There are multiple reasons behind this. First, this type of fraud can be more valuable than credit card fraud. Second, organizations don’t have stringent measures in place to fight against ATO. As the team at Sift Science points out, the time available to exploit the information before detection is typically longer. Third, this type of cheating isn’t easy to detect. Since the account already exists and is related to a genuine customer, the fraud is relatively tougher to spot and the fraudster has more time to operate before they are caught.
One breach - eventually key to many accounts
ATO in the loyalty space (featuring airlines, hotels etc.) is coming under scrutiny owing to data breaches, says Kevin Lee, Trust & Safety Architect, Sift Science, a speaker at the recently held Loyalty Fraud Workshop in Palm Springs, California.
Highlighting how one data breach can impact several verticals, Lee says, “Let’s say a customer has an account in both Uber and United Airlines. And if there is a data breach at Uber, and although United Airlines hasn’t faced any attack and are safe from that perspective, but if a user happens to use the same login credentials for both the companies, then the credentials are vulnerable for illegitimate use at other places. And about 55% of the people in the U. S. re-use passwords.” So in today’s password driven economy, if users are spending majority of their time in using 10-12 apps on their smartphones, it would be unreasonable to expect them to use different passwords for all the apps. “People tend to take a short-cut (when it comes to passwords) and won’t have unique passwords. So this makes them vulnerable to ATO.”
So everyone’s credentials have already been compromised? Is it the case?
As Google also pointed in November, account takeover is sadly already a common challenge for users across the web. The company also acknowledged that password stealing tactics pose a risk to all account-based online services. Key findings from a study (analysis spanning over one year till March last year, featuring study of numerous black markets that traded 3rd party password breaches as well as 25,000 blackhat tools used for phishing and keylogging):
· It was found 788,000 credentials were lifted via keyloggers, 12 million credentials stolen via phishing, and 3.3 billion credentials exposed by 3rd party breaches.
· Password stealing ways mean all account-based online services are under a threat. According to Google, in the case of 3rd party data breaches, “12% of the exposed records included a Gmail address serving as a username and a password; of those passwords, 7% were valid due to reuse. When it comes to phishing and keyloggers, attackers frequently target Google accounts to varying success: 12-25% of attacks yield a valid password”.
· Also, considering the fact, a password alone is hardly enough for securing access to a Google account, gradually more fraudster plan for garnering sensitive data that is requested when verifying an account holder’s identity. Google underlined that 82% of blackhat phishing tools and 74% of keyloggers tried to obtain a user’s IP address and location, while another 18% of tools collected phone numbers and device make and model.
According to Sift Science, fraudsters get access to stolen credentials from a number of sources:
· From data breaches, sold on the dark web
· Phishing with fake websites
· Malware, trojans, spyware
· Social engineering
· Hijacking a mobile device
Lee says, “My general assumption is that every one’s credentials have already been compromised.” He added, “We have actually reached the point of no return.” It might not be a straightforward task to gain access to everyone’s account, but just like solving a puzzle or putting several pieces together, fraudsters can sneak through the defence. So from one data beach one can get a vital piece of information about users. And then another breach sharing more details about users and so eventually cracking all details of one account. “So that’s how an entire identify of a user could be worked out,” said Lee.
Certainly organizations can look at preventing “own” credentials from being stolen. So, working in unison with the IT team, it can be ensured that information stored in servers and people accessing them is secure. “Unfortunately your consumers have become your weak spot. If they reuse their credentials and passwords then it remains a big issue (for organizations).
Be as strong as possible in authentication
Airlines need to look for more protections beyond just passwords. The claim for owning an account needs to be handled carefully. Machine learning comes in to understand the user behavior. Advancements in computing and big data power, as well as the gaining prominence of API-based machine learning solutions, mean that machine learning is emerging a scalable method to grow without increasing risk. It identifies patterns in data that aren’t spotted by humans. So this can result in lesser number of false positives and false negatives.
So let’s say a user booked a flight and then after a month is redeeming miles from the same device. So from a machine id or device fingerprinting standpoint, that would be a good signal from the authentication perspective. Also, consistency in the timing of redeeming miles or points could be another indicator. Another area is behavior on the digital interface – the way redeeming is being done, the time taken to reach the checkout stage etc. Such actionable intelligence from all possible data inputs can help in curbing loyalty fraud. Machine learning evaluates massive volumes and varieties of data to deliver real-time decisions. “With enough data it can be observed that the average person – when they redeem gift cards or loyalty points, most likely that’s not their first time. People tend to take their loyalty program or points/ miles seriously. Even before the transaction takes place, with machine learning one can map the holistic behavior. So one keeps on checking a particular redemption option and when they have enough currency, they go for it. It might take them months to complete this. So these are all good indicators. On the other hand these are missing in account takeover (instances).
So even as credentials have been stolen, it is imperative for organizations to bolster the authentication process. This way the risk of loyalty fraud can be minimized. So it comes to down to authentication and one of the tools is machine learning, sums up Lee.
(We will take a detailed look at the role of machine learning in curbing loyalty fraud in the upcoming articles).
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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 airline.com. 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 airline.com 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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First Published on 17th November, 2017
Ai Editorial: Airlines continue to struggle to avert the danger of friendly fraud. There are new developments, ones related to machine learning and biometric authorization, but are they robust enough to protect merchants? probes Ai’s Ritesh Gupta
Criminal Fraud, friendly fraud and merchant error are all major sources of chargebacks. The utility of data and technology in combating various forms of fraud is coming to the fore.
As for friendly fraud, it remains probably the biggest challenge and quite often the significance of an effective fraud mitigation strategy is underlined.
Friendly fraud refers to “fraud that is committed when an individual had knowledge of and/or was complicit with and/or somehow benefited from the transaction on their own account, although the individual reported the transaction as unauthorized”.
This type of fraud is a major issue for merchants as it can be tough to detect at the time of purchase, the chargeback process does not adequately address friendly fraud, and also it is time consuming to fight against the same.
Functioning of the industry
“The predicament (pertaining to friendly fraud) is getting worse,” says a senior executive.
The executive pointed out that the available data is limited. Merchants definitely suffer from industry-wide lack of transparency. Their stance is feeble as there are plenty of factors outside merchants’ control that influence their reluctance to make a more substantial effort. “There is hardly enough information available pertaining to chargebacks and friendly fraud. This means there isn’t a strong foundation to bank on, to comprehend the situation. It’s challenging to amass authentic information on the matter without substantial contribution from banks, card networks, and merchants,” added the executive.
As highlighted by Chargebacks911 in one of the interviews with us, until there is a reason code labelled ‘friendly fraud,’ merchants will forever be engaged in a guessing game—is this claim legitimate or friendly fraud? This uncertainty is what drives merchants’ inaction.
It is also pointed out that issuing banks and card networks decline to divulge critical data or specific numbers on chargebacks such as: dispute win rates. They typically don’t keep the kind of comprehensive records on the subject that would enable a broader view of the matter. Merchants need to blend professional assistance with chargeback management technology specifically designed to identify the true source of the transaction dispute.
One can question policies and regulations set forth for the entire industry. Issuers usually accept a customer’s assertion, and there is hardly any scope in terms of collaborating with issuers. It is clear that ecommerce wouldn’t prevail if card networks and issuers hadn’t taken initiatives to step up the buyer confidence when it comes to payment card use and liability. By abating cardholder’s fears about potential losses tied to fraud, networks and issuers have enabled entities to experience optimum profitability via card-not-present transactions. However, by advertising zero liability, issuers have inadvertently incentivized friendly fraud.
Airlines tend to be at the receiving end, for example, a cardholder buys airline tickets but intends to change the itinerary at a later stage. This could be due to any reason. Since the traveller doesn’t qualify for a full refund from the airline, the same passenger files a friendly fraud chargeback and points out that buy wasn’t authorized—when in fact, it was. So how to cope up with such cases where airlines suffer? In terms of sophistication, fraud prevention specialists are finding ways to evaluate the behavior of consumers and relying on machine learning for the same.
For instance, Nethone, a data science company, highlights that by identifying distinctive behavioural characteristics of each user, one can craft their digital profile and relate the same with behavioural profiles of previously identified fraudsters. The company, in one of their recent blog posts, stressed that it is viable to discover behaviour demonstrating that someone else than the rightful account owner is logged in, before the transaction is done. And this way merchants can secure transactions by activating a conditional authentication layer. Analysis can be around the purchase log from the past, taking into account the frequency of shopping, their average order value, in case there were any chargeback request previously, too, etc. Also, device fingerprinting, too, can be taken into account whether a given device has previously featured before for a fraudulent transaction. Importantly, Nethone also added that any level of additional authentication or “friction” should be added only where it’s essential and the probability of fraud is high.
The industry is also counting on biometric technology and additional layers of security and authentication.
From friendly fraud perspective, biometric authorization can used as a proof that at a customer did validate a transaction.
But this kind of authorization isn’t a complete solution on its own.
Yes, questioning a chargeback hinges on the merchant’s capacity to establish that the cardholder validated the transaction and that the merchant was in compliance with all applicable regulations. Biometrics can end up being a constructive part of evidence for merchants; the fact that biometrics are intrinsically tough to deceit is sound proof that a cardholder did, in fact, validate a transaction. But, as explained by Chargebacks911, the issue is that policies and standards laid down by the card networks do not keep up with the fast development of consumer authentication technologies. Biometrics can show that a cardholder almost definitely authorized a transaction, but if the card network won’t accept biometric data as evidence, that knowledge is useless.
In many ways, card network regulations are stuck in the past, unable to adapt to the rapidly-changing realities of ecommerce and the payments industry,” points out Chargebacks911’s COO, Monica Eaton-Cardone.
It is pointed out that card networks need to make biometric authorization a cornerstone of the dispute process.
Also, the stance of various stakeholders toward friendly fraud definitely needs to evolve, as much as new technologies can help.
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First Published on 3rd November, 2017
Ai Editorial: Specialists point out that if a merchant isn’t being able to accept payments via WeChat Pay and Alipay then the acquirer needs to be questioned, ensure they explain any barriers and how to fix the issue, writes Ai’s Ritesh Gupta
Is accepting payments via Alipay or WeChat Pay a smooth process?
Irrespective of the answer, it is imperative for any travel e-commerce player focused on Chinese travellers to come to grips with payment processing as far as Alipay or WeChat Pay are concerned. The adoption of e-wallets/ mobile wallets in China is being driven by the ubiquity of indigenous Internet giants – Alibaba (Operated by Ant Financial Services Group, Alipay currently has over 520 million active users) and Tencent (combined monthly active users of Weixin and WeChat app is already over 965 million). Merchants across the globe are looking at in-app web-based payment, QR Code payment, in-app payment and payment at a particular location, say onboard aircraft or at the airport.
Is it really tough or just wrong notion?
“Payment is quite wide and diverse (in the Asia Pacific region). And China is indeed a unique market in the whole of Asia. It’s almost that you can think of China as one area, and can segregate it from the rest,” Trevor Spinks, Head of Sales and Distribution, Scoot-Tigerair mentioned during one of our conferences in Singapore.
“Scoot flies to 18 destinations in China, and that’s a significant part of our network. We will be offering WeChat as a payment option soon. The complexity for WeChat pay is huge. It doesn’t use normal software language. WeChat Pay have their own language. So one needs to work with WeChat or 3rd party experts,” says Spinks. It is important as a massive chunk of population uses WeChat. “So it is about using what they use every day to fly Scoot. But, yes, China has very requirements, and different rules and regulations.”
Referring to a diverse region such as the Asia Pacific, Spinks mentioned that in terms of how an airline manages and works around a variety of options to pay in this region, consider an airline which flies to 10 countries and each country has 5 forms of payments. “And if all forms of payments are different from all the other markets, then there would be 50 forms of payments. You do need payment providers and acquirers. We work with a global specialist. They are already working with a number of payment distribution capabilities in several countries, and when airlines reach a certain point, they can work with one specialist and this allows an airline to straightaway tick, say 30 out of 50 payment methods, at one go. At times, there is a need to work directly with 3rd party suppliers. WeChat is a great example. We might have to work directly with WeChat to work it out for us. So it is a very diverse and hard area to manage. There is a need for a dedicated person within the airline to look after this. Also, you need expertise within each of the market to understand, whether say is 7-Eleven convenience store a viable option or is the popularity decreasing and in two years time no one would be interested in paying via this option. So then no point in investing in that payment method,” explained Spinks.
As a specialist in this arena, Chargebacks911’s COO, Monica Eaton-Cardone says, Alipay and WeChat have authorized partners, and these entities specialise in managing cross border payments and dependent on your geographic location there are several options to provide partnership.
“Alipay works with a variety of financial institutions including MasterCard and Visa. Outside of China, WeChat will only accept credit cards to link to the account. As an e-commerce entity if you are already have the functionality to deal with cross border payments through other payment rails then you have the knowledge and experience to deal with WeChat and Alipay,” she said. “E-commerce companies already have numerous rails to accept payments. Accepting payments via WeChat and Alipay would not be challenging anymore than your existing network of payment channels. If you deal with Paypal you can deal Alipay and WeChat Pay. If payment isn’t accepted then your acquirer needs to be questioned, ensure they explain any barriers and how to fix the issue. Both Alipay and WeChat are a form of e-wallet which are funded via a variety of payment options including international payment/ credit cards as well as Chinese domestic bank cards/ accounts.”
Issue of fraud
Spinks mentioned that fraud becomes a bigger problem, bigger the airline becomes.
“So when we were small, we weren’t worried about fraud, we had relatively bigger issues (to sort). But now we have around 40 aircraft, and flying to 18 different countries, fraud can be a big “number” annually. So a partner such as Adyen or Worldpay can also help with fraud solutions. But what you need here and what generally falls under the finance department, you need people would be measuring and tracking fraud. So if one country had a fraud value of 1% and the norm is 3%, then its fine. And another one had a value of 10%, so there are significant issues in that country and you have got to measure it. And the onus also lies on the 3rd party partner to sort it out. And of course, fraudsters also find new way of cracking the system, so it is always a cat and mouse game,” he said.
Referring specifically to Alipay and WeChat Pay, Eaton-Cardone said as with any platform the prospect of fraud is real.
She said fraudsters target new payment channels or newly implemented processes as they are easier to exploit and find weaknesses until you plug the holes.
“However with effective fraud monitoring this can be managed. Review of transactions and fraudulent behaviors using reporting tools, analytics of customer spending, how transactions were initiated, time of day which device was used, analysis of chargebacks will all help mitigate fraud issues. If monitoring is done at every available stage you will manage fraud issues. This is where we come in as we can help provide these skills and products to help,” she said.
Eaton-Cardone also mentioned that if there is an effective fraud monitoring process in place, then the ecosystem, say Alipay or any other, wouldn’t matter as one can apply this to wherever the payments are being generated. “When reviewing mobile transactions check your order data: What was the device used? Was a mobile phone number provided? Is there a GPS location? Does the GPS location it differ than the shipping/billing address? Don't rely on IP geolocation. Review the time of usage, tablets tend to be used more in the evening and with higher spends. Know your customer, review their typical spending pattern? Do they have a history of denying transactions.”
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Developments related to chatbots continue to intrigue. Not too long ago the utility of chabots was being questioned, about their ability to understand tone, language and intent or the value they can offer. And today certain travel companies, including established airlines, are gearing up to accept payments within the conversational/ messaging interface, and hence calling them transactional chatbots. So are AI chatbots finally living up to their intelligent branding?
The situation needs to be assessed from the perspective of who is the real user of such offering? It is already being pointed out that the mobile-first lifestyle or the tendency to interact with a connection via a messaging platform, especially in the case of a “Millennial”, is one major driving force.
So be it for a conversational travel insurance chatbot or a flight search chatbot, the use of artificial intelligence to interact with travellers in a conversation style is on the rise. And expectedly, “seamless” payment option via chatbots is emerging as a possibility. As Kaivalya Paluskar, Solutions Consultant, APAC, Ingenico ePayments mentioned, the users largely have been redirected to a new page till date, but now this is evolving gradually.
What it means is – the user would never be sent to a website to finish the transaction.
The team at Ingenico has worked on what it describes as an “in-built” solution, where the user “doesn’t go out of the chatbot to make the payment”, said Paluskar. “We can facilitate this for different platforms, including Facebook or any open API platform,” he said.
According to specialists, there could be an instance, where microsite opens when a user attempts to make the payment for the first time, but that would be just a one-time occurrence. Consequently, the user would remain within the chatbot interface for completing transactions.
Airlines are relying on partners to step their capabilities in natural language processing, and accordingly, stepping up the user experience.
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First Published on 4th September, 2017
Engaging people from China on Tencent’s WeChat or Alibaba demands an unwavering effort in order to make the most of these unique ecosystems. Foreign travel brands need to be proactive, rather than being reactive, since consumers in China purse “hot” trends and the likes of Tencent, Baidu and Alibaba are quite progressive in terms of introducing new initiatives or features.
“Alibaba and Tencent are almost coming across as “two different types of Internets”. (The challenge) is that these ecosystems don’t talk to each other,” says Matthew Brennan, co-founder, China Channel. So what this means for e-commerce players or the advertisers is that they are sort of locked in a data ecosystem, which is not transferable. So this becomes a case of a “walled garden” – you can’t get data out of an ecosystem.
There is no dearth of peculiar developments in case of WeChat, for instance, a fashion blogger selling 100 limited edition MINI Coopers, worth $42000 on WeChat in 5 minutes or the release of new style QR codes for Mini Programs. Even as questions are being raised how the usage of the WeChat app can be scaled up from the current level of 963 million users (at the end of Q2), there is no denying that WeChat remains a popular destination for shoppers in China.
WeChat Key Opinion Leaders or KOLs, WeChat search, Mini Programs, WeChat Pay, Official Accounts…if you are well-versed with Tencent’s WeChat, then you would definitely know these are some of the features of how a brand can get associated with this ecosystem.
“WeChat is neither just social media, nor just WhatsApp nor just payments either. Rather think of it as an operating system, akin to Android or iOS,” says Brennan.
By Ritesh Gupta
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First Published on 30th August 2017
Ai Editorial: Paying for ancillary products at the airport or limited payment options for in-flight shopping havent been as streamlined as some of the other options. Ai’s Ritesh Gupta learns how Amadeus is sorting these issues out.
Security and convenience are two key aspects of completing a transaction that make a traveller comfortable and assured about paying an entity.
But this has been one big hurdle for airlines as far as payments within the airport environment is concerned. Despite airlines selling more services at the airport, until now there has been no way to pay that is optimal for both the traveller and the airline. For instance, it is common for travellers to hand over their payment card to the check-in agent to use an infrastructure that’s shared by many airlines. The safety of such transactions can be questioned.
In this context, the roll-out of Amadeus Airport Pay, starting with the Lufthansa Group a couple of months ago, is set to help carriers to take secure payments at check-in desks. It is a combined software-hardware solution which is wireless, making it completely independent of the common use check-in infrastructure found at the airport.
“Amadeus Airport Pay is the first wireless solution in the industry, which accepts EMV chip card or EMV compliant smart wallet payments and can be used by multiple airlines and ground handlers, and multiple banks, in any airport across the world,” says Dan Greaves, Senior Manager, Marketing, Payments, Amadeus IT Group.
According to Amadeus, it is also the only EMV solution that can be integrated with the Departure Control System, booking and ticketing flow, meaning payments are faster, more accurate and automatically accounted for. Pocket-sized and wireless, the solution has brought real mobility to airport payments and helped to improve the passenger experience.
Countering the problem
The Chip and Pin cards are far more secure, and installing a Chip and Pin terminal which only needs to talk to one bank is a straightforward process. The problem in the airport environment has been that check-in agents may represent one airline for 3 hours during the morning then a completely different airline a few hours later. So they need to process transactions, which will be directed to many different banks.
“The problem is compounded by the fact that the providers of the shared infrastructure at airports are – understandably – reluctant to integrate third-party hardware. Until now there hasn’t been a Chip and Pin solution which is compatible with the number of different merchants and banks found in the airport environment,” said Greaves.
“That means that either check-in staff have to send customers to a different desk at the other end of the terminal to pay for ancillary services such as excess baggage, which is clearly not a great customer experience, or payments are processed by swiping the magnetic strip on the back of the card. This is the same technology as was used in the old cassette tapes and just as easy to copy so security is clearly an issue.”
As Amadeus explained, there were three basic challenges to enabling travellers to pay for additional services at the check-in desk:
· Security – While most of the world has migrated to EMV Chip and Pin payments in face-to-face environments, there are still many airport payments where the card data is entered via either magnetic swipe or, worse, manual entry.
· Multi-bank / multi acquiring – check-in desks are shared between airlines so a payment system must be able to identify which payment is for which airline and, process the payment accordingly to the relevant airline’s bank.
· Mobile – “It was not in our original solution. When first conceiving a solution we imagined it would be connected directly to the check-in desk. It was Lufthansa Group who suggested we “cut the cable!” to make a wireless solution. This makes the solution completely independent from the airport technology provider, making deployment much quicker, and enables airlines to take payment anywhere in the airport, not just at the check-in desk,” explained Greaves.
Amadeus’s payment platform, which provides the capability to process payments from different airlines each with different banks, has combined with Ingenico’s mobile payment gateway which gives access to a range of wireless EMV payment terminals.
Role of Ingenico
As for working with Ingenico, how did Amadeus go about the wireless gateway and meeting the contactless mandates from card schemes? According to Amadeus, as Lufthansa Group requested a wireless solution and at the same time Visa’s mandate requires contactless capabilities, the team had to find a partner to help achieve both these objectives. There was a need to set up the right architecture, which would ensure compatibility with these mandates, as well as providing with future proofing against as yet unseen developments.
“We achieved this by ensuring that the architecture was not dependent on the payment terminal itself; new, updated terminals can be swapped in as required,” shared Greaves.
The arena of on-board retail, especially with the rollout of on-board Wi-Fi, has opened interesting opportunities for both travellers and airlines.
“Definitely, on-board Wi-Fi opens up the opportunity to process onboard payments in a much more flexible way, much the same as payments are processed on airline websites today. This has the potential to reduce fraud, increase the number of inflight payment options and reduce the overall cost of payment for on-board transactions,” said Greaves.
In the aircraft, travellers typically have the option to pay by cash or by card. But when a transaction takes place mid-flight it is often an offline process, which means that the payment is only processed after landing. This can leave airlines vulnerable to fraud.
A lot of airlines are also limited in the number of payment methods they can accept for inflight sales – in the vast majority of cases, inflight payments are limited to cash and cards.
“But with the growth of new forms of payment there is growing demand for customers to be able to pay using payment methods such as Alipay, PayPal and others.
The growing availability of inflight Wi-Fi is solving some of these issues for airlines and travellers and opening up the possibility to manage inflight payments in the same way as payments are currently managed on an airline’s website,” mentioned Greaves.
Point-of-sale based malware has proven to be an area of concern in the retail industry. It has resulted in maximum credit card-related breaches.
Acknowledging the same, Greaves mentioned that this is a critical point and one of the main drivers for developing the solution in the first place. “The credit card data is encrypted by the payment device itself and is not stored there. With this point-to-point encryption we assure that the credit card data cannot be compromised. In addition, Ingenico put – as part of their general terminal products – measures in place that prevent the Chip and Pin terminals from being manipulated. Amadeus Airport Pay uses EMV technology that has a high layer of security thanks to their embedded microchip, which authenticates the card and allows to authenticate cardholders via PIN. This makes them a lot harder to counterfeit than magnetic stripe cards, which contain static information in the magnetic strip and is overall an older, less secure technology, which is more susceptible to fraud. The payment card details are encrypted by the payment terminal and are not stored on the terminal; the credit card data does not pass through the airport workstation either, reducing the risk of data being compromised.
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First Published on 26th August, 2017
Ai Editorial: Be it for cash for transactions or an ecosystem like WeChat or use of credit/ debit cards, payment in Asia remains wide and diverse. How are airlines gearing up for the same, explores Ai’s Ritesh Gupta
Any airline operating in the Asia Pacific region needs to diligently prepare for accepting payments. Working on such initiatives features many aspects that go beyond finalizing payment methods, and these include setting up processes and controls (currency management, currency heading, fraud prevention, and reconciliation and reporting), and compliance (PCIDSS, sensitive data protection, costs and reliability).
For instance, there are many markets such as cash-driven countries like Philippines where credit card acceptance simply cannot be compared with Singapore or Australia. And then China can be completely different, considering the popularity of payment options such as Alipay and WeChat Pay.
“Payment is quite wide and diverse (in the Asia Pacific region). Go back by five years there were only few forms of payment -cash, credit card, debit card…and that’s changed significantly over the last couple of years. Even if you consider just one country, say Singapore, where Scoot is based, it is a credit card, debit card-led market. And if one considers Philippines, more than 85% is via cash. For an international airline, with operations across Asia, one size doesn’t fit all,” says Trevor Spinks, Head of Sales and Distribution, Scoot-Tigerair.
Relying on local agents/ staff
So how an airline can gear up for Australia, a market which is credit card, debit card-led versus Philippines which is going to be different with cash being the preferred payment option?
“There is a need to remain close to your international markets. Do we have the correct strategy for payments in these countries? There are some countries where you need to cater to cash, there are some countries where an airline would need to take payment via 7-Eleven convenience stores. We have recently witnessed (the emergence of) Apple Pay, Samsung Pay coming in to the market, and expect Google Pay to available soon. So each market has a lot of different payment methods,” explained Spinks.
“So relying only on credit cards and debit cards as a method of payment as an international carrier is wrong. There is a need to work on a payment strategy for each country you are in. The best way to approach the same is seek feedback from GSAs (general sales agents) or country managers. They are ones who know their respective markets inside out and share popular payment methods and trends. So one can prioritize and be ready to accept payment via methods that are relevant, and can be fulfilled by airline websites or call centres.”
China is a unique market in the whole of Asia.
It’s almost that you can think of China as one area, and can segregate it from the rest. Facebook and Google aren’t really relevant or functional in China, and as Spinks, says payment methods are even more distinctive in this market.
“Scoot flies to 18 destinations in China, and that’s a significant part of our network. We will be offering WeChat as a payment option soon. The complexity for WeChat pay is huge. It doesn’t use normal software language. WeChat Pay have their own language. So one needs to work with WeChat or 3rd party experts,” says Spinks. It is important as a massive chunk of population uses WeChat. “So it is about using what they use every day to fly Scoot. But, yes, China has very specific requirements, and different rules and regulations.”
He further explained: “So in terms of how you manage and work around this diverse payments world in this region, consider an airline which flies to 10 countries and each country has 5 forms of payments. And if all forms of payments are different from all the other markets, then there would be 50 forms of payments. You do need payment providers and acquirers. We work with Worldpay. They are already work with a number of payment distribution capabilities in several countries, and when airlines reach a certain point, they can work with one specialist and this allows an airline to straightaway tick, say 30 out of 50 payment methods, at one go. At times, there is a need to work directly with 3rd party suppliers. WeChat is a great example. We might have to work directly with WeChat to work it out for us. So it is a very diverse and hard area to manage. There is a need for a dedicated person within the airline to look after this. Also, you need expertise within each of the market to understand, whether say is 7-Eleven convenience store a viable option or is the popularity decreasing and in two years time no one would be interested in paying via this option. So then no point in investing in that payment method.”
As for consumers, airlines need to study how smartphones are shaping up their payment choices. How age and gender play a role in payments and where does travel as a shopping category fits in.
As new payment types become culturally engrained, users initiate to count on them for higher value transactions such as travel.
Other factors that need to be considered are:
· Know the local requirements, such as whether airlines are required to partner with a local entity in order to start connecting with local consumers. What sort of benefits does a local payment gateway offer, other than meeting legal requirements? Can one partner facilitate different methods - convenience store (tend to be semi-digital payments - a consumer takes a code or a QR Code associated with a booking and pays), online banking etc.?
· What are the complexities of integrating with a particular alternative payment method? Is extra cross-channel payment interface design and development required if airline goes directly with local payment platform?
· Unlike credit card, each of the payment options in Asia has its uniqueness, e.g. transaction limit, availability of refund, no pre-authorization, chargeback rights. What is needed to design and implement necessary payment interfaces and processing flows?
· What is needed to consolidate payment transaction especially for more easier reconciliation and reporting of sales and settlements across payment options?
· Implement necessary payment controls according to the difference of processing by payment types (e.g. refund, void, capture).
· Implement fraud monitoring and prevention across payment options. “Fraud becomes a bigger problem, bigger the airline becomes. So when we were small, we weren’t worried about fraud, we had relatively bigger issues (to sort). But now we have around 40 aircraft, and flying to 18 different countries, fraud can be a big “number” annually. So a partner such as Adyen or Worldpay can also help with fraud solutions. But what you need here and what generally falls under the finance department, you need people would be measuring and tracking fraud. So if one country had a fraud value of 1% and the norm is 3%, then its fine. And another one had a value of 10%, so there are significant issues in that country and you have got to measure it. And the onus also lies on the 3rd party partner to sort it out. And of course, fraudsters also find new way of cracking the system, so it is always a cat and mouse game,” concluded Spinks.
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