The technological advancements in the finance sector are at an all-time high. However, the true impact of new-gen technologies like Blockchain, AI, and IoT in the finance domain is taking its own sweet time to become visible in the sector. The fact that the industry is one of the most expansive domains and filled with unstructured data makes the process of technological integration all the more difficult for a tech entrepreneur to disrupt it.
There is one technology, though, which has taken the whole and sole responsibility of making the sector that touches the world an effective one – Artificial Intelligence. The extent to which AI is being explored to better the fintech industry is too massive to contain in this one article. So, we will be focusing on the impact of technology on one of the most used sub-set of the sector – Payments.
Top 3 Ways in Which AI is Making Payments Better
1. Bettering Detection Of Fraud
Around two decades ago, the internet saw a massive boom. It changed how people bought clothes and groceries, how they ordered food, and even how they saw content. In a world where people were no longer confined to the real world boundaries to transact, real-time payments were born – but little did we know that it would be conjoined with online frauds and security breaches. Going by the huge number of transactions that happen every second, it is difficult for a human to be on top of security.
Financial institutions continue to produce multiple data that can be studied by machine learning. The data comes in both unstructured and structured data, which in a number of cases, are not rightly valued or connected. This vastness of data poses a key challenge in AI adoption.
According to a Capgemini report, the universal digital payment transaction is expected to grow to up to 726 billion by the end of the year 2020.
AI software can be used for sending alerts to the users if an activity not normal for the user is detected. The software will be able to understand the users’ behavior over time – an insight that will help them identify a discrepancy in their behavior and will alert them in real-time to save them from becoming a hacking victim.
2. Vision-based Payment Transactions
An AI-based retail store is the newest kind of concept that the industry has to offer. It plays around with recognition software that is placed all across the stores to save the traditional checkout process time by detecting which items are picking and charging them directly from their store app. While deemed fiction a few years ago, Amazon Go is making it a reality. Its new AI-powered store is designed in a way that the customers do not have to wait in queues for checking out. All they have to do is pick the product and the amount will get deducted from their store wallet or bank account.
Businesses have now begun experimenting using facial recognition systems, for authorizing payments and using cards and payment systems as its replacement. The biggest pro of facial recognition, gesture detection, and voice recognition system is that it’s almost impossible to fool them. In the future, we can expect businesses to use voice and face for authorizing payment transactions and transfers.
3. Payments Through Chatbots
Businesses are looking to bring Artificial Intelligent services into multiple areas of their processes. After ruling the customer service domain for a long time, the technology is now getting adopted by finance and payment firms for answering users’ questions, interacting with the customers, making online purchases, and navigating the users through the app or website.
Chatbots are one of the most advanced and promising expressions of interaction between machines and humans. It’s an artificial-intelligence powered system that simulates a conversation with the users in a natural language. It can be initiated on a plethora of devices – phone, messages, apps, and websites.
Capital One, a bank holding company, has created chatbot Eno for helping customers check their account balances, perform basic account queries, and perform fund transfer between accounts. Another example of this can be seen in Ingenico, the smart payment technology system has linked with IBM Watson to develop an AI-powered chatbot to help the merchants belonging to the hospitality, retail, and entertainment sectors conduct payment.
Through the mode of natural language processing, chatbots are able to have a conversation with users in multiple languages to find customers’ interests and help users make payments through the app’s secure API. Machine Learning and AI algorithms also enable the lenders to look at multiple data sources for developing personalized profiles which help predict the users’ credit risks that then guide them towards approval or rejection of the loans.
Although payments are a key area getting impacted by AI, its true adoption depends on the customers overcoming some key challenges.
Challenges Standing Ahead of AI Adoption in Payments Domain
Data.
Financial institutions continue to produce multiple data that can be studied by machine learning. The data comes in both unstructured and structured data, which in a number of cases, are not rightly valued or connected. This vastness of data poses a key challenge in AI adoption.
Moreover, businesses continue to produce data in vertical silos compared to end-to-end horizontal processes that act as AI adoption barriers.
Culture.
Artificial Intelligence comes attached with awe and fear, mostly because of the image that they carry. People in banks and financial who are scared of losing their job to AI will be a lot more reluctant to embracing AI in a way that their potential is realized. Additionally, there are employees, especially ones belonging to the 50+ age group who feel uncomfortable about talking to a chatbot because they think they might expose their business data to security hacks and breaches.
Technical limitations.
While AI has made massive strides in the payment sector, technology still has several limitations to overcome. For example, chatbots presently lack the cognitive ability that is needed for wrestling with human emotions and complex challenges.
Secondly, an AI system is only as efficient as the data which goes into the development of the system. In case the data is biased or incorrect the results created by AI will also be incorrect.
Now that we have looked into the applications of artificial intelligence in the payment sector and the challenges associated with the adoption of the technology in the domain, the next step should be to read into the practical applications of companies using AI.
In what other ways do you think is AI bettering the payments sector, share with us in the comments section.
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Smith is a very creative writer and active contributor who loves to share informative news or updates on various topics and brings excellent information to readers. His priority is to cover up new technologies and techniques for his audience. Smith has come out with many interesting cases and information that attracts readers to unravel his write-up.