Digital Payments have become an integral part of the consumer’s everyday activities. They are happening at every moment in the life of today’s mobile consumers as they are surrounded by digital products and services.
From pay-per-use, subscription payments, invisible payments, credit/debit card transactions, one-click checkouts, mobile payments, and top-ups – Digital Payments are estimated to grow at a rapid pace to account for more than 70% share of all payments by 2025. Moreover, the continued innovation and increasing performance in digital technology; consumer demand for one-touch payments/ touchless payments; policy push towards financial inclusion and desire to marginalize cash; and an exponential rise in digital/ mobile applications and services, are the mega-trends that are driving the Digital Payments revolution.
As technology is the backbone of this sector, the payment industry is increasingly being driven by information and data, which is now allowing financial institutions to rapidly respond to changing customer preferences and emerging trends to drive future growth.
From loan issuers, credit card companies, retailers, and insurers, the ecosystem has understood that big data processing techniques will not only simplify complex tasks risk management and financial inclusion but will also empower fintech players to serve potential customers better. Big data analysis is helping refine and personalize financial products and services thus enabling innovation in the digital financial ecosystem. Data science as a subset of technology has combined synergies with digital finance ecosystem quite seamlessly.
We have entered an era of possibilities, opportunities, and developments. The analytics tools and the algorithms developed to process big data are revolutionizing the traditional approach of operating businesses. The introduction and continued improvements of these tools enable fintech players to transform the process of automation, segmentation of consumers and consequently provide customized offerings.
Having said that, the fintech industry was among the earliest proponents of data science. It avidly used algorithms to build quicker and more precise services than traditional banking institutions. And for this swiftness and preciseness, fintech opened its doors to a completely new consumer base – people who are digitally savvy, wanting seamless financial services on the go. Using data science, fintech companies could analyze and make better decisions to manage finance, almost real-time, across a variety of areas, starting from fraud detection and risk analysis to trading and customer management.
While both offline and online retail has always made use of data for actionable insights on consumer behavior, leveraging of data analytics in the Digital Payments space has unlocked opportunities for cross-selling and upselling, optimizing marketing outreach, hyper-personalized services, innovative partnerships, creating new synergies between products, and managing strategic risks.
For example, the ‘Buy Now Pay Later’ method of transaction is an interest-free payment option where users can make payments within a stipulated time. It gives users the convenience to shop for products and services that they may not be able to afford at the time or would like to conveniently pay for it all at once. This kind of payment system needs real-time automated underwriting, powered by data analytics that verifies customer information, credit capacity, and processes the line of credit almost instantaneously.
Furthermore, the adoption of next-gen tools such as Artificial Intelligence (AI) and Machine Learning (ML) in payment data analytics has given fintech players an opportunity to revolutionize digital lending, as companies can responsibly build insights on consumers’ lending stories, past transactions associated with their credit cards, and even evaluate the applicant’s repayment prospects. According to an industry report, the global business value of AI in finance will be USD 300 billion by 2030 with 77% of the world’s population using an AI-powered device.
Additionally, by implementing AI and ML, fintech companies are not only removing the risks to enable seamless access to credit, but are also connecting businesses to consumers, and are onboarding new customers digitally – and personalizing the customer journey along the way. For example, automated data and analytics provide key insights into customer payment preferences, enabling them to devise innovative partnerships, like co-branded offerings, that grab the user’s attention, build trust, and then seamlessly onboard them with tailormade services based on their personal needs and preferences.
This is a stellar service that technology like Big Data, Machine Learning, and the Internet of Things provide fintech players with. Through these very many innovative techniques to choose from, a merchant can easily enhance the loyalty of the existing customers. The concept of loyalty programs, offers, and discounts are utilized optimally with the implementation of these techniques.
These methods not only ensure customer retention for merchants but also improve the efficiency rates of products and services made available to them. A seamless customer experience is certain to generate impact for merchants and big data analysis along with other innovative methodologies are contributing to it.
While developing digital-first experiences is critically important for today’s payment companies, data should never be undermined. Data-driven analytics powered by tools such as AI and ML can enable crucial business outcomes and help companies better understand rapid changes in the market and consumer behaviour.
Having said that, the key element is the combination of human intellect with artificial intelligence that delivers optimal solutions to consumers. Big Data is highly sensitive and requires classified protection. The human factor will always remain in the equation to ensure that data protection is never compromised. Used wisely, big data will prove to be revolutionary for the fintech industry in the years to come.
