When MyBucks, a Luxembourg based Fintech firm, started scaling up their business in other countries. They faced a daunting challenge of reducing the timeline for processing credit requests from over a week’s time to just under few minutes. Any financial institution dealing with lending could very well relate to the nature of challenges associated with giving credit – checking credit history, tracking past fraudulent activities, and so on. This automatically makes the lending process tedious and time consuming. To add to this, MyBucks also aimed to make their entire lending process extremely simple and attractive to customers. MyBucks’ promise to its customers: No more visiting branches and seeking approvals. Simply login from your mobile phone and apply for a loan – we will handle the rest in a matter of minutes.
What might appear transformational with Machine Learning in MyBucks’ case is just one of the many examples of how Machine Learning is empowering a large number of finance based companies to deliver disruptive products and services. So what makes Machine Learning so attractive to Fintech and how has Machine Learning fuel this entire industry’s phenomenal growth? Read on.
Quicker and efficient credit approvals
Long before Machine Learning was established in large industries unlike today, it was quite commonly used to solve fraud detection problems. This primarily involved building a self-learning model that used a training dataset to begin with and further expanding its learning based on incoming data. This way the system could distinguish a fraudulent activity from a non-fraudulent one. Modern day Machine Learning systems are no different. They use the very same predictive models that rely on segmentation algorithms and methods. Fintech companies are investing in big data analytics and machine learning algorithms to make credit approvals quicker and efficient. These systems are designed in such a way that they pull data from several sources online, develop a good understanding of transactional behaviours, purchasing patterns, and social media behavior and accordingly decide creditworthiness.
Robust fraud prevention and error detection methods
Machine Learning is empowering banking institutions and finance service providers to embrace artificial intelligence and combat what they fear the most– fraudulent activities. Faster and accurate processing of transactions has always been the fundamental requirement in the finance industry. An increasing number of startups are now developing Machine Learning and Artificial Intelligence systems to combat the challenges around fraudulent transactions or even instances of incorrectly reported transactions. Billguard is one such company that uses big data analytics and makes sense of millions of consumers who report billing complaints. The AI system then builds its intelligence by using this crowd-sourced data and reports incorrect charges back to consumers thereby helping get their money back.
Reinventing banking solutions with the powerful combination of APIs and Machine Learning
Innovation is key to survival in the finance industry. The 2017 PwC global fintech report suggests that the incumbent finance players are worried about the advances in the Fintech industry that poses direct competition to banks. But the way ahead for banks definitely goes through Fintech that is evolving everyday. In addition to Machine Learning, ‘API’ is the other strong pillar driving innovation in Fintech. Developments in Machine Learning and AI are reinventing the traditional lending industry and APIs are acting as the bridge between classic banking problems and the future possibilities. Established banks are now taking the API (Application Programming Interface) route to tie up with innovative Fintech players in their endeavor to deliver modern solutions to customers. Fintech players are also able to reap the benefits of working with the old guard, banks, in a world where APIs have suddenly become the new common language. So what is this new equation all about?
API solutions are helping bridge the gap between the old and the new – by helping collaborate in newer ways to solve traditional banking problems. This impact can be seen far and wide within this industry and Fintech as an industry isn’t just limited to lending tech and everyday banking alone. There are several verticals within the industry that now find increased impact of Machine Learning -payments, wealth management, capital markets, insurance, blockchain and now even chatbots for customer service to name a few.
So where do you think this partnership is headed? Please leave your comments below and let us know.