How Machine Learning Plays A Crucial Role For Frictionless Digital Payments Transactions
According to a leading online portal, online transaction fraud is a huge and growing concern that is “expected to reach $25bn by 2020, which means that $4 in every $1,000 will be fraudulent. A payment transaction cycle would take place within three seconds. The transaction process passes through variety of platforms like e-commerce stores, wireless terminals or mobile devices. In fraudulent card transactions, usually, the liability is borne by the issuer of that particular card if the merchant is compliant in terms of payment acceptance procedure. However, most of the fraud liability lies with the merchant for CNP (card not present)transactions. With chips on cards becoming highly secured, the fraudulent activities are now swarming around online transactions. For example, the e-commerce platforms have developed payment gateways, which partially store the information of the customer. This shopping behavior is not only capitalized on by online mega-retailers such as Amazon, but has also led traditional merchants to follow an omni-channel strategy. Growth in online sales will lead to higher CNP fraud liability. Fraud Prevention Strategies Source:wallethub The rise in smartphone usage led to major shifts in focus of banking and retail companies. These mobile devices, unlike desktop are more susceptible to data leakages. Few popular strategies to tackle fraudsters: Through Guaranteed PaymentsThrough Payer AuthenticationThrough Data Analytics Network providers such as Verified by Visa, MasterCard SecureCode, JCB J/Secure, and American Express SafeKey provide a 3D security layer where the authorisation is completed by an additional password or OTP known only to the cardholder. This method of payer authentication is widely in use today but a data leak is always around the corner. Fraud payment through Data Analytics, presently has a growing presence in the field of digital payments. Credit card fraud detection is a data mining problem and is challenging due to two major reasons – first, frequent profile changes making for a unstable behaviour and secondly, highly skewed credit card datasets. Source: Guardian Analytics The fraudulent activities are carried out in seconds and the traditional methods have very few options to thwart the attacks in real time. With data analytics fueled by machine learning algorithms, which devour on tonnes of historical data and user behaviour, makes it possible to detect outliers in real time. Basic fraud prevention systems are capable of listing the historical losses and techniques used by fraudsters in the past. Why Is Machine Intelligence A Big Deal Frictionless consumer payment experiences lie at the heart of any successful online payment services platform. The performance evaluation of fraud detection in any ML model is usually done based on following factors: AccuracySensitivitySpecificityMatthews correlation coefficientBalanced classification rate Logistic Regression, Modified Multi-Variate Gaussian, Modified Randomized Undersampling, Adjusted Minority Oversampling, and Adjusted Random Forest are the algorithms, which are widely deployed in ML models. To increase the accuracy of the machine learning models, new techniques are being built using the pre-existing models. One such improvement is done using a hybrid model, where a under-sampling and oversampling carried out on the dataset; say of credit card transactions. Companies like Paypal acquired startups like  Simility to boost their machine learning capabilities in tackling fraudulent activities. At Simility, a fraud detection model would generally follow three main steps: Identification of anomalies by using unsupervised machine learning modelsLabelling the anomaliesTraining a supervised model using these labels. These online merchants also use these models to identify consumer buying patterns like their social presence to offer suggestions on savings or tailoring the wealth and risk profile for them. AI will likely identify the culprit and help ensure it won’t happen again. With advances in machine learning and the deployments of neural networks, logistic regression-powered models are expanding their uses throughout PayPal. Road Ahead There will be more smart-mobile devices added to the market in the coming years. With the Government’s plan to make digitisation highly inclusive and far reaching, customer discretion and information security has to play catch up with the innovation on both the retail end and of that of the fraudsters. Even though blockchain technology had shown a promising start, key organisations are either still skeptical or are working towards building the infrastructure necessary to meet the demand. Whatever be the innovation, any transaction system will eventually has to prove its authenticity through privacy and security in a robust yet non-intrusive way.
According to a leading online portal, online transaction fraud is a huge and growing concern that is “expected to reach $25bn by 2020, which means that $4 in every $1,000 will be fraudulent. A payment transaction cycle would take place within three seconds. The transaction process passes through variety of platforms like e-commerce stores, wireless […]
Network providers such as Verified by Visa, MasterCard SecureCode, JCB J/Secure, and American Express SafeKey provide a 3D security layer
["Data loss prevention", "digital payments", "Machine Learning", "PayPal"]
["Go", "API", "machine learning", "PayPal", "AI", "neural network", "ML", "digital payments", "Machine Learning", "Git", "Data loss prevention", "analytics", "R", "fraud detection"]
["AI", "machine learning", "ML", "neural network", "analytics", "fraud detection", "R", "Go", "Git", "API"]
I have a master's degree in Robotics and I write about machine learning advancements.