It goes without saying that Artificial Intelligence (AI) and Machine Learning are having a considerable influence on the world around us. From robotic process automation and speech recognition, to virtual agents and driverless cars. The extent of its impact is said to have moved us from a world that is mobile-first, to one that is AI first (according to Google’s CEO Sundar Pichai).
Ten to fifteen years ago, very few people were even familiar with the idea of Machine Learning or AI; today, however, the marketplace is a very different place. A recent global study from Pega found that 72 per cent of people now understand what AI is and that only 28 per cent are uncomfortable with the thought of it. It’s no surprise so many industries, companies – and those in the media – are so focused on it. Indeed, you only have to look at the prevalent discussions and outputs from the recent 2018 CES (Consumer Electronics Show) event to see this evolution.
Beyond the hype and heightened media attention around both AI and Machine Learning, and the numerous startups and internet giants racing to acquire them, there has been a significant increase in investment and adoption by enterprises. In fact, according to a Vanson Bourne study, ‘State of Artificial Intelligence for Enterprises’, 80 per cent of enterprises already have some form of AI in production today; 30 per cent are planning to expand their AI investments over the next 36 months; and, 62 per cent are expected to hire a Chief AI Officer in the future.
In the last decade alone, we have built powerful computers that can process more data and use more complex and sophisticated algorithms than ever before. In turn, the volume of data generated has increased exponentially, which can train those algorithms better. Such fast paced and innovative developments mean that there are huge opportunities.
Below are just some of the ways that this technology is taking the payments industry by storm.
Increased automation is leading to better insights
The impact of AI is felt strongly across the payments landscape, from changing the way people invest their money to automating the borrowing process; a huge development for those who’ve previously been overlooked as a result of cumbersome challenges and infrastructures.
A key benefit of AI is that it can help payments companies dramatically improve operational efficiency, examples include: reducing processing times and human error, as well as providing user insights and increased automation. In this sense, AI is helping businesses to reimagine and restructure operating models and processes. For example, it can support businesses in processing huge volumes of data to generate financial reports and satisfy regulatory and compliance requirements; processes that would typically involve large numbers of people performing repetitive data processing tasks.
In fact, AI’s transformative power is having such a monumental impact on the financial services industry that it has been predicted to replace up to 75 per cent of outsourced financial services jobs within 15 years (KPMG). This could have a huge implication on companies looking to reduce operational costs; enabling them to develop and nurture other areas of their business.
It goes without saying that it will also have an impact on workers themselves. With many debating whether AI leads to workforce augmentation or degradation. It can be argued that removing some of the more repetitive and laborious tasks gives employees the chance to either re-skill or up-skill and take on more strategic, and hopefully engaging, roles. In fact, Forbes research indicates that, by 2034, AI could boost labour productivity by up to 40 per cent.
With many companies having already invested in AI, thanks to advances in big data, open-source software, cloud computing, and faster processing speeds, it’s becoming increasingly mainstream.
Enabling more informed decisions
Both AI and Machine Learning are helping to support investing decisions which are data driven. Quantitative techniques and new methods for analysing big data have increasingly been adopted by key market players in recent years. And as the quantity and the access to data available continues to grow, it will continue to impact how investors look to leverage data analysis to make more informed decisions.
The lending industry has the potential to achieve massive operational and strategic efficiencies by implementing Machine Learning, with many key players expediting the lending process using Machine Learning and big data analytics. It’s already being used in all types of verticals from retail to healthcare, and across the financial services industry more broadly it can replace older statistical-modelling approaches with new and innovative techniques.
Broadly speaking, Machine Learning is having a huge impact on the industry as a reliable means of reducing spending and risk. Beyond this, consumer-centric tools are being used to automate the process of savings, providing a huge benefit to consumers, both in terms of the speed at which this can be processed and of course, the cost implications.
Success in action
An example of both AI and Machine Learning in action is the work one of our partners Kreditech is doing in the payments space. We partnered with a shared ambition and belief in the enormous potential of technology to unlock credit and financial services for underserved populations. Kreditech’s innovative technology uses Machine Learning and AI to improve financial freedom in the many high growth markets around the world that require better access to financial services, for example India and Brazil.
Specifically, Kreditech’s model uses data science and technology to enable companies to gain a better understanding about a customer’s credit rating. It uses Machine Learning and AI to process alternative data that subsequently enables them to develop scoring technology that takes advantage of this data to replace traditional credit models.
Services like those offered by Kreditech are important in economies where many merchants from mature markets are hesitant to lower their risk threshold based on traditional payment verification models. According to McKinsey, 2 billion people in developing nations don’t have access to financial services such as savings accounts and credit. That’s almost half of the developing world’s adult population. The opportunity is arguably huge.
AI and Machine Learning have enabled key players across the payments and fintech landscape to dramatically transform, both in terms of their back and front-end processes. From cutting costs, automating time-consuming operations and shortening the approval process, both AI and Machine Learning will continue to pave the way for the payments industry. The changes we’ve already seen are just the beginning and personally I’m excited to see what’s yet to come.
Jose Vélez is the Chief Executive Officer of PayU Latin America
Image Credit: Razum / Shutterstock