For a long time, the lack of human-machine interfaces was the key limit of many tech advances. But things changed with the spread of AI and ML technologies.
In this post, we are going to look at how the advances changed the fintech industry, with Emerline — a software development company focused on the development of innovations.
One of the most prominent examples of AI in fintech is conversational technology. Before it had developed to its current state, it was represented by script-based chatbots that provided users with answers to frequently asked questions and eased their access to the common features of applications. Due to the limits of script-based scenarios, the necessity of something more intelligent and flexible had arised. And that paved the way for for NLP (natural language processing).
With the introduction of NLP to fintech, chatbots became more intelligent as they were able to communicate with users dynamically — the machine got the ability to learn from conversation and discover the actual needs of each customer.
Today, the mix of AI and NLP is used by many leading fintech providers. Some of the examples include the Bank of America with Erica, HDFC with EVA, SEB with Aida, and others.
Now, let’s look at the current and close-coming benefits offered by the application of AI in fintech. These are:
According to Business Insider, AI in fintech will save banks $447 billion by 2023.
Savings on Labour Costs
The same source states that by 2023, AI will also save banks about 862 million of working hours.
Facilitation of Banking Processes
One of the brightest examples of this is risk management procedures. The use of AI in fintech opens up space for dealing with such issues as credit underwriting and fraud prevention efficiently and with a high level of security.
To get a better taste of how technology can be applied to fintech, let’s take a look at some of its use cases.
Having a conversational AI at hand, companies can receive valuable insights from each conversation with a customer, meanwhile avoiding a manual stage of information entry and updates. The thing is that AI can work together with a CRM system, and after logging, parsing, and evaluating the received information, it accurately puts it into the corresponding groups.
But what are the insights and how can they be used? For example, these could be identification of competitors, regions that lack products or services the company offers and where they will be in demand, information about the most profitable sectors to consider for investment, useful insights about customers, their preferences, expectations, and their level of satisfaction with products and services offered.
Furthermore, the biggest bunch of advantages offered by conversational AI are just coming. Soon, the technology will develop to the point when customers will be able to discuss and find out all the things they are interested in with its help. What’s more important, the information provided will be so personalized that customers will be able to hear the answer to their questions in any language or even dialect they speak.
Considering all these, there’s no wonder why by the end of 2024, the technology market value is expected to reach $15 billion, compared to $3.2 billion fixed in 2019. So if there’s a good time to invest in conversational AI, it is now.
While the number of interactions with chatbots is growing with remarkable speed and is expected to see a 150% growth in fintech by 2023, experts predict that in the coming decade, only 5% of all conversations in the industry will be left to humans.
Have some doubts? Even today chatbots show their value by processing insurance claims and providing customers with the ability to generate a simple claim approval within a few minutes. Furthermore, these are no longer chatbots that follow scripts but act as a tool for a personalized experience, suggesting customers the best policies to meet their needs.
And, as we’ve already mentioned earlier, the ability to get valuable insights without market research and qualitative and quantitative studies — all offered by chatbots — should not be underestimated. So, are you ready to implement a chatbot technology into your business to reap the benefits? Then do it with no worries, it will pay for itself.
Natural Language Processing
Personalized interactions with chatbots are due to the use of NLP. Sentiment analysis that allows receiving valuable information on the level of customer satisfaction and paves the way for machines to understand the mood of the caller to respond accordingly — all this is also thanks to NLP. The ability to understand different accents, dialects and phrasing to create clear and expected answers — once again, it’s NLP. Need to convert voice to text, analyze scanned and handwritten documents in seconds? You know the answer.
NLP is probably one of the major ingredients that now makes chatbot technology so alive, and in the future, with further growth and enhancement, it can work miracles to the fintech industry.
To conclude, it’s important to say that while AI and ML are still on their way to flourish in fintech, the time when the first flowers bloom is very close. Pioneer to chatbots companies have already seen how the value of this tech has increased just in a couple of years. And the future is even more promising and bright. So, once again, if you consider this tech as an option, it’s worthy of your attention and investments.
NLP for fintech affects the industry positively in two primary ways. The first is in front-office customer interactions. Rather than relying upon a fixed, script-based response, NLP uses efficient voice-to-text and text-to-voice conversions, which allow a computer to understand dialects, accents, and phrasing. This creates a natural, intuitive experience for the customer: needs are met, expectations are exceeded, and targeted service is delivered rapidly.
The other area where NLP for finance is a natural fit is in a back-office application. NLP-enabled computers can review a vast database of information and go far beyond searching for keywords or phrases. It can also search for inferences, implications, and connected material by analyzing the language’s structure, returning comprehensive results. When paired with optical character recognition technology, the system can also analyze scanned and handwritten documents, including those in its analysis.