Role of machine learning and AI in changing the payments landscape
“We are surrounded by computers that can see, hear and learn. Welcome to the future.” – David Waters.
With Machine Learning as the medium, systems are analyzing vast amounts of data over time to carry out tasks independently; leading to better insights. Machine Learning solutions simply learn from experience without being programmed and this Artificial Intelligence has come a long way in the past decade and is now an integral part of our day to day lives. AI and machine learning are now in the thick of technology debate and thanks to advances in big data, cloud computing, and faster processing speeds it has become a mainstream solution in most sectors. The world is going digital and institutions can now access vast amounts of data; in fact more data has been created in the last couple of years than in the entire previous history of the human race. AI processes use this data to identify patterns and irregularities which helps companies increase protection and security, reduce operational costs, achieve better compliance and an increase in revenues, because of improved customer engagement, better productivity and an enhanced user experience.
How AI and Machine Learning have changed the Payments Landscape
This transformative technology has brought about a revolution in the financial industry where the demand for safe, swift and easy payment structures has been keeping financial institutions on their toes for far too long. However, this industry also has the added advantage of having access to a huge database and the capital and staffing resources, to make the most out of these revolutionary technologies to transform the different aspects of the traditional financial processes. Generally, the more data that is fed to the machine, the more accurate are the results and there is an infinite amount of financial data available, which is why global innovation in AI and ML in the financial industry are at an all-time high.
As consumers are becoming more and more connected, they need to be able to pay through any device, anywhere at any given point of time. The number of connected is set to grow to 50 billion in 2020 and a trillion in 2030. Machine learning in payments can be utilized in a wide range of situations, ranging from using data to complete the KYC (Know Your Customer) procedures entirely online, near real time authorization of transactions, changing the way people invest, predicting borrower delinquency, to improving customer service.
This is a noteworthy development, particularly in terms of the unbanked population who were previously overlooked as a result of cumbersome challenges and infrastructures. While the use of AI in the payments industry is still in its early days, it can harness the different potential applications of the technology through its large scale and broad applicability. Previously KYC processes were complex, slow, and very often ineffective. Today, the processes rely on a basic match process between the identity information provided by the government and biometrics on a small scale. On a larger scale, financial institutions also access and analyse a range of third-party data sources; including credit reports, CIBIL scores, watch lists, social media, transaction history and other consumer and business information. This has brought a large number of people into the mainstream financial sector, even those who were once a part of the unbanked population, which has improved their financial situation and increased the trust among customers in banks and other financial institutions.
The financial services industry is also suffering from fraud-related losses more than any other industry. Modern fraud detection goes beyond traditional financial fraud detection and this is where Machine Learning and Artificial Intelligence in the financial industry comes in to play. For instance, Machine learning algorithms can constantly evaluate huge amounts of data on loan repayments or company stocks or spending patterns and predict trends that can have a huge impact on lending, insurance and access to credit. The warning systems can also be used by financial institutions to spot irregularities, predict frauds, reduce risk and provide insights on what to do in case of fraud. Machines can access and analyse huge amounts of data in mere seconds to identify and flag off unusual behavior wherein the system will delay potentially fraudulent transactions, until a security team can take a decision on it and this can ultimately save financial institutions billions.
ChatBots have also proven themselves as a powerful tool to ensure customer satisfaction and also helps companies save a lot of time and money. Research shows that over 70 percent of the clients of banks consider measures that saves a customer’s time to be the most valuable aspect of good service. While using technology like chatbots which are available to customers 24/7, machine learning helps financial institutions to solve customer issues immediately by providing them with instant information and answering their questions, thereby freeing up customer service representatives for the more complex requests, which takes a lot of weight off the customer support system. These bots can also gather intelligence on customer needs, emotions and reactions which automatically helps them learn and provide better service.
Billions are being invested in Machine Learning and Artificial Intelligence processes across the world. There are numerous potential applications for machine learning within the real-time payments market that have been recognized and are being utilized by innovative companies. The possibilities are exciting and endless, and India could be a forerunner in this journey, thanks to the population, data and talent that is available.
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