As AI takes center stage this year, here are some of the ways through which it reshapes payment systems.
Client and merchant interface will be assisted by AI
Voiced-based virtual assistants serve as the interface between IoT-powered services and the user. More payment systems are building upon this by developing front-facing machine learning services that can address common user concerns. Forbes noted that the rise of chatbots which are fully personalised and have machine learning capability will help in improving user experience through better recommendations and payment processes.
AI will change the fraud detection game
Many companies still use manual reviews in transaction anomaly analysis to look for fraud and errors. This traditional rule-based fraud-detection system, however, is not capable of handling big and complex data. In fact, The Paypers claim that almost 52% of fraud flags are false positives. AI is changing the game not only by having the capability to consider more factors in fraud detection. It can also scan and recall patterns quickly, and learn through processing huge amounts of data. In truth, the more data that AI handles, the better it can develop its anomaly detection algorithms.
AI can now replace traditional credit scoring models
Most credit scoring models are currently based upon a limited set of data like yearly income. AI can utilise big data and create scoring models using a whole range of factors such as overall asset liquidity and business network value.
AI can optimise payment routes as well as automate telematics and logistics management
Payment success is often hampered by authorisation rates. Machine learning can streamline the process by learning from submitted payments and creating estimates using a set of parametres. UK-based startup Previse leverages AI technology to instantaneously assess the likelihood that a given invoice would be paid right after it is issued. This has enormous implications in logistics and overall efficiency of the supply chain. These systems can work in tandem with IoT-supported tools which optimise logistics operations. Across the Atlantic for instance, the US has mandated the use of electronic logging devices (ELDs) on freight trucks. Verizon Connect states that ELDs can gather data on delivery time and evaluate fleet compliance which can then be analysed by AI systems. This critical information can help to synchronise business operations. These aspects can easily turn problematic if you factor human error into the equation.
At Arvato, we have used the past decade in exploring and developing innovations around Machine Learning, Artificial Intelligence, as well as Predictive and Prescriptive Analytics. The ability to detect frauds and to manage credit risks is of immense importance to us as a trusted partner that navigates clients through the complexity of credit management. It is our role to enable seamless and secure financial transactions between businesses and consumers around the world.
With the help of Machine Learning and Artificial Intelligence, it has become possible to develop leading-edge credit management solutions that support business processes throughout the customer lifecycle. Fraud and credit risk management are essential parts of establishing new customers but the cumulative and ever-growing amount of data creates a perfect foundation for continuous, intelligent, and data-driven credit risk management. While providing tools for advanced fraud and credit risk management, Machine Learning and Artificial Intelligence also generate ideas for enhancing and improving the customer experience.
As a part of our development initiatives, we have chosen to strategically invest in online security and decided to partner with SecuredTouch. SecuredTouch is one of the pioneers in the field of digital fraud prevention and has focused on mobile applications and thus gained a leading role in the industry worldwide. Their technology detects fraudulent behavior, as well as attempted fraud through automated computer attacks carried out using emulators, bots and malware. Consumers have thus efficient protection against identity theft or account takeovers, as their patterns of behavior in online transactions can rarely be imitated.
All in all, AI is changing the payments landscape not only by refining established models. The tech also improves human-centred processes via integrating learning-capable machines. Therefore, AI introduces payment systems to the world of big data, and everything is only going to get bigger from here on out.
Feature by: June Blanchard & Lauri Holländer