Ai Editorial: 5 reasons why data is set to play an integral part in combatting fraud
First Published on 14th December, 2018
Ai Editorial: Data is going to be a key weapon in the arsenal of airlines as the industry attempts to fight emerging fraud threats, writes Ai’s Ritesh Gupta.
Airlines acknowledge that they need to be in a position to probe as many data source as possible in order to improve the probability of uncovering and combatting fraudulent activities and transactions.
Going forward, airlines not only need to focus on their own unique data, but they also have to count on external data plus be open to collaborating with other stakeholders to stop fraudsters’ malicious moves.
1. Tracking and consolidating own data: Blending all the available transactional data into a single system and analysis model is critical considering where the industry stands today with CNP purchases and e-commerce sales. In addition to ticket-related revenue generation, keeping a vigil on frequent flyer miles, loyalty points, gift cards etc. is must. Considering the way fraud evolves, airlines can’t ignore options like e-gift cards. Fraudsters are capable of breaking through gift card codes through various methods such as phishing or social engineering. Airlines’ own data, especially on their own channels like a website, is important to refine analytics around it.
Big data is first used to collect information about the user’s behaviour on the website (for instance, how the mouse moves, words per minute etc.), and this information is combined with machine learning, which uses pattern recognition to map the pattern of his behaviour to match it either with positive (genuine) or negative (fraudulent) behaviour, as well as predictive analytics that records the positive/ negative behaviour and uses that on future transactions for potential signs of fraud. After the point of data collection, airlines have to amplify and triangulate the data, analysing the data through multiple permutations and combinations so as to better understand the fraud patterns left behind by fraudsters in their attempt to brute force the system.
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. Blending data from other sectors for the benefit of airlines: Specialists serving the travel industry state that the fraud-related issues must be confronted collectively. There is strength in numbers and insight in data—and help is available to leverage them both. Specialists like Accertify are working on airline-specific offerings, and their machine learning technology aggregates and transforms information from a diverse set of sources to identify emerging fraud risks and attacks. External data can complement and lend a new dimension to internal data sources, offering a better view of shoppers and the authenticity of transactions. Evaluating IP addresses, credit card data, and email addresses can enhance a carrier’s interpretation of who is doing what—and from where they are doing it.
3. Accuracy of machine learning: The collection of more and relevant data would help to improve the accuracy of the machine learning models by churning the data through various permutations and combinations to identify potential fraud patterns. However, ultimately a multi-disciplinary approach, that combines machine learning and other techniques to make sense of the score automatically, is required to fully automate the fraud screening process. Machine learning models are only able to provide a fraud score, of which a bulk of transactions are automated but humans are still required to review a good number of transactions that are considered borderline.
4. 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.
5. Collaboration: A shared database or working together with relevant partners is going to be the biggest factor in combating fraud. IATA Perseuss allows members to check suspect transactions against a community database holding records from around the world. Still there is plenty to learn from other industries or law enforcement in a particular market that has managed to control fraud to an extent. With a partnership featuring different players from the industry, the government and law enforcement agencies, fraudsters are being punished. For instance, the Banking Protocol scheme in the U. K. allows bank branch staff to immediately alert police and Trading Standards if they suspect fraudulent activity. The Dedicated Card and Payment Crime Unit (DCPCU), backed by the finance industry, made 84 arrests and interviews under caution in the first half of 2018, which led to 26 fraudsters being convicted. As for capitalizing on data, intelligence is also shared with law enforcement including the National Crime Agency. A campaign is being led by Financial Fraud Action UK to help everyone protect themselves from preventable financial fraud and is being delivered with and through a range of partners in the UK payments industry, financial services firms, law enforcement agencies, telecommunication providers, commercial, public and third sector organizations.
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