The financial and banking spheres are among the most competitive nowadays. The competition here is fierce, which enforces both new and established businesses go beyond traditional solutions. One of the best decisions for these businesses is to use modern technologies such as Machine Learning and Artificial Intelligence to gain the upper hand. Let’s take a look at how FinTech companies took advantage of these technologies by employing them for different purposes, and is it worth investing in these technologies at all.
The future of AI and ML in the Fintech sphere
Before diving into the use cases of AI and ML in fintech and banking software development, you need to make sure that your investments are going to pay off. Here are some latest stats and facts that reflect the future of AI and ML in FinTech.
The MarketsandMarkets research company estimated that by 2022 artificial intelligence in the financial sphere will be worth more than $7 million compared to $1.3 million in 2017.
According to the PwC survey of 2017, more than half of those in the financial services industry confirmed their substantial investments in AI. Almost 70 percent of respondents were going to invest in AI during the next three years heavily.
The report by Juniper company estimates that banks’ savings on AI-powered chatbots only will reach $7.3 billion by 2023.
As you can see, the statistics show that AI and ML are not buzzwords anymore. These are the path for financial establishments to become frontrunners in the sphere. If you, too, want to leverage AI and ML technologies for your business, let’s take a look at their most popular use cases that allow banks and financial companies to save money, provide better user experience, and more.
AI and ML use cases in FinTech
There are a lot of ways how you can use artificial intelligence and machine learning for your company. Here are the most promising ones.
Virtual assistance
It’s impossible to compete with other companies if your customer support is lacking. Modern customers expect the same (excellent) experience throughout the whole time of interacting with your company and services. It means that no matter how great your services are, once a customer faces an obstacle, it ruins the general experience. This is proven by the Dimensional Research and ZenDesk survey, which revealed that almost 70 percent of B2B and more than 50 percent of B2C customers would stop cooperating with a brand once they have a negative experience.
So, how can your FinTech company leverage AI and ML to provide better customer support? Here are some ideas for you:
- Chatbots. AI-powered chatbots are a powerful tool to not only improve customer experience but also save costs. The Juniper Research report claims that in 2023, chatbots will save more than 800 million hours for banks.
- Personalized experience. People tend to stay with a financial service provider for a long time if they are happy with the service quality. Modern banks use personalization to increase customer satisfaction and build loyal relationships with them. To provide users with the targeted offers, banks employ machine learning to collect data on customers and analyze it.
- Sentiment analysis. Sentiment analysis is a study of customer’s emotions via multiple news channels to reveal people’s attitudes to banking establishments and detect customer behavior patterns. AI programs allow companies to efficiently gather and analyze big data and get valuable insights to improve their services.
Processes automation
To stay offload, companies have to reconsider all their organizations’ processes to make them faster and cheaper. For instance, machine learning algorithms allow lenders to get useful insights on borrowers, better assess risks, and make information-driven decisions.
Insurance companies use the same approach to risk assessment. Such companies apply natural language processing algorithms to check all the information on a potential customer in social media and other sources to get a comprehensive picture of a person or a company.
Security enhancement
Machine learning is a much more effective option to detect suspicious transactions than manual checks. People need to be taught and trained before they can start performing checks. People are also limited in the time they can do this job. Machine learning, on the other hand, can operate 24/7 and, what’s more important, it can also improve the quality of checks due to self-learning mechanisms.
Fintech software development companies create solutions based on machine learning to check suspicious transactions, detect the software systems’ weaknesses, prevent hacks, and much more. ML programs collect data and process it to convert it into easy to digest reports that suggest decision-makers’ actions.
A parting thought
The recent changes connected with the world pandemic and lockdown has shown that the FinTech industry can’t survive without digital transition. If you want your business to stay competitive, you need to shift to the online form of operation and do it fast. So, there’s no question whether or not to use AI and ML. The only question is how fast you can adopt these technologies.
Story by Paul Belogour. He is an experienced IT entrepreneur with a laser focus on Fintech-related projects.
Share
Contributors
