According to Crunchbase, global venture investments in the first quarter of 2023 fell by 53% compared to the same period last year, amounting to $76 billion. These are frankly poor results. That said, all stages were affected without exception, including the early-stage fundraising.

Yet, the picture seems a bit brighter when looking at the fintech market. No matter what's happening in the startup world, global investments in Fintech are growing, according to CB Insights. Generative AI integration is a core factor in attracting investors’ attention. And simply making fintech better. So how do you properly integrate generative AI into your fintech apps to kill two birds with one stone?

How Has Fintech Done in Q1 in 2023?

Frankly, fintech is one of the aspiring industries growing despite the downsizing venture capital market.

Thus, investors have made 983 deals totalling $15 billion (+55% QoQ), according to CB Insight. A whopping 72% of these deals were made with early-stage startups, marking a new record in the observation period.

Such domains as wealth tech have suffered a significant outflow of investments recorded, while payments, digital lending, insurance, and capital market technologies received the most funding.

It is hard to overestimate the power of generative AI in attracting this success. If, in 2018, investors invested $408 million in AI products, then in 2022, this figure will reach $4.5 billion. Therefore, founders should consider integrating AI into their products, either as an innovative development or as a tool that optimises processes within the team.

As for fintech, artificial intelligence is indispensable when collecting and processing large amounts of data, assessing risks, and optimising processes. In the case of fintech companies, it dramatically simplifies tracking clients' financial status, analysing information about their investments, loans, and accounts.

According to Grand View Research, the AI tools market for the fintech sector went beyond $9.45 billion in 2021 and it's still on the rise. Experts predict that from 2022 to 2030, this market will experience incredible growth at an average annual rate of 16.5%. Big players like Microsoft, Google, Salesforce, Intel, Amazon, and others have jumped into the race for this market.

How to Enhance Your Fintech App’s Customer Experience With AI?

As AI is rapidly transforming global asset management, investors are eager to harness the power of AI technology to deliver market-leading services. The majority of fintech companies, for example, use AI-powered chatbots to handle aspects such as customer service representatives, salespersons, and more.

But besides chatbots, there are plenty of ways AI can help boost your fintech app.

Enhanced Security

Fraud has been one of the major and costly issues in the financial sector. In 2020, personal data theft alone cost approximately $26 billion, with each victim experiencing an average loss of $1,100. Most fintech companies utilise AI-based solutions to enhance security. However, there is a need for additional updates as criminals are becoming increasingly sophisticated in their cybercrimes.

AI can analyse large volumes of data through machine learning and provide fintech companies with the ability to offer unique solutions. By detecting suspicious behaviour, AI is used to identify fraudulent activities and process financial documentation.

In fintech you can hardly overestimate the value of user’s trust – customers will stay with the service that they rely on. Overlooking the factor of trustworthiness in your relationships with customers is a mistake that can cost you new investments from fundraisers.

Solid security is a core in building trust with users and consequently gaining and retaining customers.

User acquisitions and retention rates are the key metrics investors look at when considering investing into a startup. By providing users with security, trust, personalization, and customer support, co-founders gain a competitive advantage among other candidates for fundraising.

Customer Management

Previously, clients had to establish relationships with the staff of a local bank who would personally assist them with their needs. While this method of customer service may still work locally, it becomes challenging to maintain in the modern, more globalised market.

This is where artificial intelligence, especially generative AI, has proven effective, thanks to online chatbots. They can interact with customers round-the-clock providing highly personalised assistance.

The global economy expects chatbots to hit $7 billion by 2023, and financial institutions have compelling reasons to continue using virtual assistants and artificial intelligence to interact with customers.

Advanced payment systems

Historically, there has been a growing demand for a more reliable payment system, and AI has all the potential to bring change here. Soon enough, we may witness a new world of seamless payments that could replace point-of-sale (POS) systems.

Currently, FinTech payment systems serve two functions: payment storage and transfer. Thus, customers use mobile fintech apps to either pay for goods and services or to make direct peer-to-peer transactions.

For instance, Zelle, a platform created by US banks, links payments directly to the customer's account, allowing for payments between different financial services (unlike Paypal, where users have to use a single payment service). This model allows large banks to be part of the digital market. In 2020, Zelle’s payment volume was nearly double that of the Venmo and PayPal payment apps.

Yet, AI can make the payments’ world even more seamless. Thanks to Machine Learning (ML), the transactions will speed up, while the processing time will be accelerated. Here’re some more ways in how AI can disrupt the payments industry:

• Increased automation: accelerating processing time;
• Security and authentication: AI can simplify the authentication process and enhance the security level;
• Fraud detection: faster data analyses for fraud detection.

Reliable Credit Ratings

Applying for a loan without a credit rating can be challenging, and traditional financial institutions often overlook most potential clients. However, many fintech companies offer alternative ways for loan application without a credit history review.

For example, ZestFinance, an AI-driven lending software, leverages AI to assess the creditworthiness of potential lenders by extracting and analysing such data as employment profiles, web search history, and social media activity to create a soft credit rating.

Efficient contract management solutions

Contracts are an integral part of the financial industry, and tracking these contractual agreements requires a lot of time. AI can streamline the contract drafting process using optical character recognition (OCR), machine learning (ML), and natural language processing (NLP). The COIN project serves as a prominent example. Launched by JP Morgan in 2017, COIN, also known as Contract Intelligence, completed around 360,000 working hours' worth of manual work in a matter of seconds.

Financial market forecasts

In recent years, data-driven investment decisions have been solid. In 2018, for example, thanks to computer treating strategies the quant hedge fund industry closed at $1 trillion.

Thus, the popular take on algorithmic approaches to investment has shifted from scepticism to interest. Indeed, AI can provide accurate financial markets’ forecasts, and many investors are starting to use it in trading.

For example, Wall Street has leveraged AI in market analysis, and cutting-edge AI research is even being used to power automated crypto trading.

Personalisation in Financial Management

Last but not least is the use of AI in personal management. And it is not about chatbots this time.

Personalisation in neobanks has been a thing even before the AI/ML hustle. Banks not only try to analyse users’ past actions to provide recommendations, they try to assess customers’ direction of movement and aspirations – in other words get into users’ hearts and souls. 

AI can help users make better financial decisions, set up saving goals, long-term investment plans and other calculations. One may argue that non banks already do that – like Acorns. Yes, but AI can take it on a whole other level, giving users tailored recommendations based on their own spending data.

So data is the key. To teach a machine learning algorithm the customer data, neobanks need to build data ingestion pipelines. The data extracted from these pipelines should include traditional banking customer information and transaction data, as well as less traditional clickstream data from a bank’s platforms (app or website) or data from third-party partnerships with various platforms and aggregators.

But besides collecting, storing and analysing the data, neobanks should constantly deploy, test and iterate on their models.

Drawing Up

AI has been here for some time, and it is not another passing hype wagon. It will significantly change how we treat finances and do business in this sphere.

AI integration is no longer a choice for your fintech startup. This is a claiming necessity that fintech startups need to harness and employ to the fullest extent.