With the advent of programs such as Siri, Google Assistant, and Microsoft Cortana, Artificial Intelligence (AI) and machine learning have come a long way. For example, the front page of Amazon or YouTube would look different to two people, unless their searches, browsing habits, and locations were 100% identical.
Machine learning allows computer programs to automatically learn without explicit programming. Thanks to machine learning, users can now access bespoke content tailored-made based on their browsing habits and buying behavior in the former’s case. Research from IDC indicates that applications that incorporate predictive analytics, such as machine learning will grow 65% faster than their non-predictive counterparts.
Forrester reports that 80% of US customers believe that the quality they value the most in customer service is the notion that the company values their time. Through machine learning, companies can learn more about their customers’ needs and tailor their responses and actions accordingly in a timely fashion. Moreover, since the opportunities to satisfy customers on an in-person basis are few and far in between, machine learning opens new possibilities to enrich customer experience and foster customer-centric interactions. The importance of customer experience cannot be understated; a Walker study indicates that, by 2020, customer experience will overtake price and product as the key brand differentiator.
What Companies are Doing with Machine Learning
Those possibilities extend to the field of mobile payments. In September, 2017, Stockholm-based mobile payments processor iZettle raised a €30 million (~$36 million) debt round to fund Research and Development (R&D) initiatives that cover AI and machine learning. The company targets small to medium businesses with hardware and software that streamlines card payments via mobile devices.
iZettle is not the only company that realizes the importance of AI and machine learning. Retailer Gilt uses deep learning to search for similar items of clothing with different features. In 2016, Etsy bought Blackbird Technologies to apply the latter’s image-recognition and Natural Language Processing (NLP) technology to its search function. UNIQLO offers its customers an AI-powered assistant with a deep knowledge about its product catalog, retail locations, and more.
Ant Financial, a subsidiary of Alibaba in 2014 created to operate its AliPay mobile payment platform, unveiled a AI-powered photo-intelligence system that allows drivers in car collisions to take a picture of the wreck and file an insurance claim with the AI system. The system can calculate a claim in as little as half a second.
According to Ant Financial Vice President and Chief Data Scientist Yuan Qi, the collision processing system is a good example of how AI and machine learning can turn existing systems on their heads. By feeding thousands of example images, the system can be trained to recognize things difficult to discern to the human eye. Their machine learning technology is also used to crunch user data to determine whether to grant a customer a loan.
What makes Ant Financial unique is that their model targets individuals and small businesses who do not have access to financial services like a bank or an insurance company. As of September, 2017, Ant Financial submitted an application for US government clearance to buy Dallas-based MoneyGram, a digital wallet and money transfer service.
More possibilities for machine learning in fintech include fraud detection and prevention, by analyzing a customer’s transaction, purchasing habits. SecurionPay states that as more data is introduced to the system, the machine will be able to detect fraud with increasing accuracy.
Rise of the Machines Chatbots
Consumers have yet to completely embrace chatbots, computer programs that conduct conversation through auditory or textual methods. Nonetheless, while chatbots have been around for some time, it is their capacity to learn and improve that underscores their potential as game-changers. As the technology improves, banks and fintech companies foresee a variety of roles and abilities for the chatbots to fulfill.
In response to a need for a seamless customer service experience, citing discombobulated “contact us” pages and forms, NeuraFlash has developed a chatbot that uses NLP to comprehend text patterns in addition to utilizing Salesforce data and history to personalize conversation. The official site states that the system will take less time, less cost, less frustration, fewer dropped leads, and higher customer satisfaction.
Customers of FedEx can track a package by simply asking their Amazon Echoes, eliminating the need to fill out forms and enter tracking numbers. Mitsuku, the winner of the Loebner Prize for most humanlike AI, has seen varied users, such as an avatar being used for educational purposes in China. The technology is still nascent, but chatbots are opening new possibilities, delivering information faster and more efficiently than their human counterparts.
The introduction of AI and machine learning has led to interesting developments in the world of finance and technology. Companies like iZettle and Ant Financial understand the importance of using machine to accomplish mundane tasks considered tedious to humans.
Moreover, while chatbots might not be seen as a serious force right now, their learning capabilities may change that notion rather quickly.