Technological advancement comes in many ways. Some trends become buzzwords and fade away with time, leaving only a sour taste. Some withstand the test of time. Some, finally, have the potential to transform the entire landscape. Artificial intelligence is a great example of the last one. Here are some of the most important applications of AI in fintech – proving, that marriage of human design and machine perfection is an interesting match.
We broke down the landscape into smaller pieces. Talking about specific use cases gives us the opportunity to highlight specific implementations. Additionally, it’s a great opportunity to show how rich the horizon for AI really is and what might come next.
Fintech is a perfect test ground for new feature releases, especially when they come in the form of microservice-powered building blocks. The list below only proves a point – we live in an age of radical transformation.
Application #1 – Market sentiment analysis
Being able to process huge amounts of data is invaluable. Fintech companies provide insights into the investors’ attitudes and ascertain if their views about market conditions are positive, neutral, or negative. Drawing data from news articles, social media, business podcasts, and other sources leads to building comprehensive market reports.
By utilizing human language processing, recognition of emotion-related words and text data, specific keywords, and other data pieces, companies build an image of investors’ mood.
This plays right into consumers’ hands. There are multiple fintech apps that allow for currency swaps and stock investment on the side. If algorithms detect mood swings towards certain stock, it might be recommended for a portfolio. Even as a portfolio builder, which is a stock that everything else is rotating around. And that’s a powerful recommendation for an average Joe who wants to try investing from time to time.
There’s also a separate topic of high-frequency trading (HFT) where algorithms execute trade orders within milliseconds, capitalizing on minimum stock price discrepancies.
Application #2 – Credit risk assessment and underwriting
The integration of AI revolutionized financial services in ways previously unthinkable. Credit risk assessment, customer’s credit history, and underwriting processes – all is either available or optimized with AI. Machine learning (ML) algorithms are used for advanced data analysis to enhance credit scoring systems.
Using AI in the credit risk assessment area enables real-time decision-making processes. It allows predictive analytics and making more informative decisions about risks. AI helps by enabling streamlined operations due to automatization, higher accuracy through algorithms, and saving money by minimizing errors made with human decisions.
Application #3 – Know Your Customer (KYC) processes
KYC verification became a standard. Nonetheless, every business and operational process can be optimized. This one is not an exception. By going through piles of data, AI can accurately confirm customer identity, minimizing the risk of fraud. By pointing out inconsistencies, human operators and decision-makers can eliminate fraudulent activities or money laundering schemes.
By minimizing human interventions, companies experience fewer errors. Plus, there is a minimized need for oversight. The same goes for assuring regulatory compliance – less work, less fuss. Machine learning can spot discrepancies, red-flagging potential mistakes in product or policy code or text. It eliminates the possibility of non-compliance penalties and increases reputations among regulators. It’s also good for branding, as it benefits customers along the way.
Application #4 – Smart payments
Fintechs can make transactions more personalized, efficient, and secure. Which factor on this list is the most important? Wait… Why should you choose at all? Personalization increases customer volume, efficiency of streamlined processes benefits companies and customers alike, and security… it speaks for itself.
Machine learning analyzes transaction data patterns, helping predict behaviors. Therefore, companies can process transactions while reducing errors.
Security measures are crucial because we live in an era of a constant war between white and black hat hackers (securing and stealing groups all over the world).
Personalization enhancements can predict if users want to pay in their local currency or, while on vacation, they would need a local currency for dinners, diving, and everything in between. This translates to increased customer satisfaction since even a little time saved equals more time for themselves, family, and joy.
Application #5 – Personal financial planning
And since we are talking about personalization… Do you know why fintechs win over traditional banking applications, not to mention brick-and-mortar facilities? Because main street banks are adopting fintechs’ vision for the product and copy functionalities.
By incorporating personal savings goal metrics, splitting spending to highlight the structure of it, and even adding personal accountant modules, banks are admitting they were previously lacking a human touch. Ironically, artificial intelligence can be seen as a powerful tool for human relations.
Let us explain. Personal financial planning gives users personalized advice tailored to individual needs, financial situation, and risk tolerance. Do you want to save more? Here are some recommendations, generated based on your spending history. Do you want to buy crypto? These are terms, conditions, and guidance based on how much you earn, how much you spend, and how AI thinks is the best level of investment, giving these patterns.
There are even apps based solely on robo-advisors, utilizing AI-powered algorithms for wealth management. No other functionalities apply. Like Betterment, which craft investment strategies, that are tailored for each user separately.
Apart from tools and robo-advisors, AI is also implemented by fintech apps for credit scoring, which is very important in personal finance decisions. Application for another credit card or a loan? Credit scoring can make or break these decisions.
Application #6 – Customer service automation
Automating tasks like answering FAQs on a chat is a standard now. By providing personalized advice, AI is enhancing the customer experience. That’s a whole other level.
AI-powered fintech chatbots like Haptik use machine learning to continuously improve customer experience based on interactions with users.
Predictive analytics used in the context of customer service, helps anticipate users’ behavior and even their feedback. That results in better customer service, not to mention enhanced software development processes and higher-quality apps in the end.
Application #7 – Data-driven customer acquisition
Can you acquire customers with AI? Why not, it’s already happening. By pulling actionable insights from customer data pools, both fintech companies and their third-party support entities can target specific consumer segments. Down to the smallest details, like spending money on a specific type of product, in specific parts of a week or month. That way, you can sell Christmas presents in an e-commerce module of a fintech app, to someone who buys Christmas gifts early, let’s say in October.
If a customer shops frequently buy waits to actually pay for products, the app can display (through a modal, for example) a buy now, pay later (BNPL) option. That drives additional customers and revenue streams to the mix.
You know what’s also important? Data-driven competitor analysis. Especially in fintech, where the market is saturated and there’s a shrinking space. You may want to use Signum AI for that. It’s an AI-powered customer tracking and acquisition platform that gathers data from multiple points of contact and accounts across the web.
Application #8 – Computer vision and surveillance
In a world where brick-and-mortar apps leverage fintech ideas and incorporate their features in their products, fintech apps get banking apps. It’s normal. So, what would you say about AI features taking over security companies and making sure that ATMs are safe?
Let’s say you have a Revolut card and you want to use it for ATM withdrawal abroad. Your card gets stolen in front of it or there’s a machine malfunction and the plastic gets swallowed. What now? AI can detect and identify the situation, prompting both ATM owner and yourself, sending the situation ID on your smart devices. At the same time, calling the local police department, having a photo of the perpetrator sent for identification purposes.
The future is now!
Application #9 – Natural language processing (NLP)
NLP algorithms can analyze customer sentiment from social media posts, customer reviews, and other textual data, providing valuable insights for companies to improve their products and services. That opens the door for multiple future collaboration and partnerships. See how many partners have Revolut in their Platinum tier? It’s not a coincidence.
NLP algorithms can extract essential information from unstructured data sources, such as financial documents or news articles, facilitating data analysis and decision-making processes. They can analyze textual data from various sources to identify market trends, consumer preferences, and competitor analysis, aiding in market research and strategic decision-making.
Application #10 – Brand management and enhancement
Keeping track of customer feedback is one thing. Leveraging consumer’s pain points and mapping areas for improvement is another. AI can identify frequently reported bugs and suggestions and match them with specific users to track problems through feature or geolocalization. That’s just two examples, the possibilities are endless.
Regular consumer sentiment monitoring, paired with bug and suggestions tracking, builds up a solid background for predictive analytics. That way, when a crisis occurs (downtime, major technical issue, hackers’ attack) the team can respond swiftly, responding to the matters at hand.
That’s invaluable for all companies, not only those with completed major investment rounds. Like CoverTree, our client with a successful 10M seed funding campaign.
Do you want an app like that?
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