6 ways to use AI in fintech app development

13 min read
December 22, 2023

$32.76 billion – that’s how much the AI in fintech market will be worth in 2030, according to Verified Market Research.

And there’s a good reason for that.

If you’re building a fintech app, AI can help you take it to the next level.

With AI, you can improve your app’s UX, its security, and cut your costs.

Here, we’ll discuss 6 ways you can use AI when developing a fintech app to make that happen.

Let’s go!

What is AI?

AI is a branch of computer science that aims to build machines and programs that can mimic human intelligence.

In other words, AI refers to machines and programs that can perform tasks we normally would associate with human intelligence and not computers.

This includes tasks like:

  • Language recognition
  • Image recognition
  • Translation
  • Pattern recognition

And AI is a broad field with a lot of subfields like machine learning and deep learning – here’s an overview of how they relate to each other:

AI vs machine learning vs deep learning

source: Singapore Computer Society

But, how does it actually work?

In the simplest terms, AI works by processing and then learning from huge amounts of data.

It then uses what it’s learned to make informed decisions and predictions.

Of course, this is a simplified explanation, and the processes are much more complex in reality.

But, that’s how it works on a basic level.

Now, let’s discuss how you can use AI in your fintech app.

6 ways to use AI in fintech app development

Fraud detection and prevention

Fraud detection and prevention is one of the best ways you can use AI in fintech app development.

And the need for fraud detection and prevention systems is rising – fraud cost consumers $8.8 billion in 2022 alone.

But, the cost of fraud doesn’t just affect consumers.

According to LexisNexis Risk Solutions, every $1 of fraud loss costs U.S. financial services firms $4 in extra costs.

And bringing those costs down to a minimum is a very compelling reason to invest in AI fraud detection models.

But, how do AI fraud detection models work? And how can you build one for your fintech app?

To put it simply, they work by analyzing huge amounts of data, like transactions, and detecting anomalies and suspicious behavior – here’s a more detailed overview:

AI fraud detection system

source: Penta Security

AI fraud detection systems can help you detect common types of bank fraud caused by:

  • Identity theft
  • Phishing
  • Forged documents

So, how can you add an AI fraud detection system to your fintech app?

One option is training and building an in-house AI model from scratch and integrating that in your app.

But, that can get expensive, and you’ll need to build the infrastructure to support your AI model, too.

Another option is using third-party AI fraud detection tools and systems like:

But, whichever option you choose, you’ll make your fintech app more secure, reduce your losses, and increase user trust.

And that’s why AI fraud detection systems are such a good investment.

Key tips

  • Use real-time monitoring – if you monitor transactions and other user activity in real-time, you can flag suspicious activity before it causes significant damage
  • Regularly test and audit your AI model – regularly testing and auditing your fraud detection AI model will ensure it’s working as intended
  • Educate users about fraud risk – educate your users about common fraud risks and encourage them to report suspicious activity


Personalization is the name of the game in today’s world – and using AI to do it in your fintech app will help raise the bar.

And that’s not just idle talk.

Twilio’s 2023 State of Customer Engagement report showed that 66% of users will quit a brand if their experience isn’t personalized – that number rises to 75% among Gen Z users.


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Also, another study showed that 81% of users want brands to understand them better.

These numbers show just how important personalization is to users in this day and age.

So, how does AI figure into all of this?

We’ve already discussed how AI can analyze huge amounts of data – and that’s what’s key for AI-boosted personalization.

AI personalization

source: LinkedIn

With the right AI model, you can give your users a hyper-personalized experience when they’re using your app.

A good example of successful personalization in the fintech space is the chatbot Cleo.

Cleo describes their product as “the world’s first AI personal assistant dedicated to personal finance”.

They use open banking data and a deep learning AI model to give personalized financial advice to their users.

Adding a similar chatbot to your fintech app or just giving your users personalized financial tips is a great way to personalize your app.

And that’s exactly what users want.

Key tips

  • Offer personalized financial advice – use an AI model to analyze your users’ data and offer personalized financial advice like investment suggestions or budgeting tips
  • Use real-time personalization – use models that can offer personalized suggestions like warning about overspending in real-time
  • Give your users control – make sure your users can control their personalization settings and explain how your AI models will use their data

AI cybersecurity

Cybersecurity is becoming more and more important with every year – and using AI cybersecurity in your fintech app can significantly improve its security.

Of course, there’s no such thing as a fully secure system.

Like Gene Spafford, computer security expert, once said:

“The only truly secure system is one that is powered off, cast in a block of concrete and sealed in a lead-lined room with armed guards.”

But, AI cybersecurity solutions are the closest you can get to that.

That’s why they’re becoming so popular – 82% of IT decision-makers plan to invest in AI cybersecurity systems in the next 2 years.

And there’s a good reason for that.

Cybercrime is expected to surge in the coming years, as you can see below:

Cybercrime costs 2018-2027

source: Statista

The estimated cost of cybercrime worldwide will rise to $23.82 trillion by 2027 – that’s a 107% increase from the 2023 numbers.

But, that’s on a global scale.

On an individual level, the average cost of a data breach is $4.35 million.

And the average cost of a fintech data breach is likely even higher, because it deals with financial data – not to mention the reputational damage such a breach can cause.

So, how can you add AI cybersecurity to your fintech app?

Of course, you can build your own AI cybersecurity system but keep in mind that an in-house system will need a hefty investment.

And using AI cybersecurity platforms like Crowdstrike’s Falcon and Darktrace’s Enterprise Immune System is more cost-effective.

The Falcon platform integrates a large number of cybersecurity tools in a single platform and offers end-to-end protection.

Crowdstrike Falcon platform

source: Cosive

And Darktrace’s Enterprise Immune System has an AI model that can detect and counter threats in real-time.

It also learns from previous encounters with threats, becoming better over time.

And that’s why AI cybersecurity solutions are a game-changer.

Key tips

  • Use predictive analysis predictive analysis based on historical cyber attack data will help you predict and prevent future threats
  • Do user behavior analysis – use AI to analyze user behavior and detect any suspicious activity or compromised accounts
  • Use self-learning algorithms self-learning algorithms learn from previous incidents and improve protection from new attack methods

Robotic process automation (RPA)

Robotic process automation (RPA) refers to any AI technology that automates repetitive routine tasks in business processes.

And that’s why it’s one of the most useful ways to use AI in fintech.

But, let’s first talk about how RPA works.

In RPA, bots mimic the actions a human would take when completing a certain task.

Leslie Willcocks, professor at the London School of Economics, put it best: “RPA takes the robot out of the human.”

It can take care of repetitive tasks like:

  • Form filling
  • Data entry
  • Generating reports

The main point of RPA is automating and optimizing business processes.

And since the fintech industry depends on analyzing huge amounts of data, automating repetitive tasks can save you a huge amount of time.

But, you can also use it in your app.

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Your app will likely rely on users entering data or migrating large volumes of data from other platforms.

You can integrate RPA tools that will automate those processes, saving your users’ time and improving their experience.

And you can use it for quality assurance (QA), too.

RPA tools for QA can automate repetitive tests and run them with higher accuracy.

That’s why it’s a good choice if you want to use AI in your fintech app.

Key tips

  • Identify repetitive tasks your first step when implementing RPA should be identifying repetitive tasks like data entry and transaction processing that you can easily automate
  • Test and optimize RPA tools before deployment – make sure your RPA tools are working properly before you deploy them to avoid costly mistakes
  • Focus on scalability – as your app’s user base grows, make sure your RPA tools can handle the increased data volumes

AI customer service chatbots

Customer service chatbots have taken the world by storm and AI has drastically improved them.

Adding an AI chatbot to your fintech app is a great way to improve your customer service and your users’ experience.

And on top of that, it can help you cut costs.

That’s because an AI chatbot can handle most simple customer questions, so you don’t need to have a large customer support team.

Also, they’re available 24/7 and can help your users instantly.

But, AI customer service chatbots can do much more than just solve simple user problems.

They can also:

  • Help users with money transfers
  • Give personalized financial advice
  • Help with loan approvals and insurance claims
  • Assist users with tax filing

And that’s just the tip of the iceberg – there’s a number of other ways they can help your users, depending on the type of fintech app you’re building.

But, how do you build an AI customer service chatbot?

Of course, you can build one from scratch – but that’s a time-consuming and expensive process.

An easier solution is using a third-party chatbot like Tidio’s Lyro.

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source: Tidio

And it’s very simple to use – you just add a knowledge base by linking the content you want Lyro to learn and it’s ready to use.

Another option is using OpenAI’s GPT models – they’re available as APIs.

You can train them on your data and you’ll have one of the most capable and recognizable chatbots in your app.

And that’s a great way to improve your users’ experience.

Key tips

  • Use off-the-shelf chatbots – using off-the-shelf chatbots you can train on your data can be a cheaper solution than building a chatbot from scratch
  • Create a feedback loop – make sure your users can leave feedback on your chatbot’s responses and use that feedback to improve the chatbot
  • Add multilingual support – if your app is aimed at a global audience, make sure you add support for languages other than English

Algorithmic trading

Algorithmic trading, also called automated trading, uses a computer program following a clearly defined set of instructions to place a trade.

Algorithmic trades account for the majority of trades in major equity (stocks and shares) markets – about 60% to 75% of the trading volume, depending on the market.

Here’s a simple overview of how it works:

Algorithmic trading

source: Investopedia

And using AI for algorithmic trading in your fintech app can help you raise the bar and get ahead of your competition.

So, how exactly can you use AI to improve your trading algorithms?

One interesting way is using natural language processing (NLP) AI models to analyze financial news, reports, and social media feeds.

Natural language processing

source: CleverTap

Normally, algorithmic trading depends on strictly defined criteria and as such, can’t take market sentiment into account.

And AI can use NLP to analyze market sentiment and that will help you better understand it and adjust your trading algorithms.

You can also build an AI model that can make real-time adjustments to your trading strategies based on current market conditions.

This will help you stay on top of and adjust to sudden changes in the market.

And that’s why using AI for algorithmic trading is a good idea.

Key tips

  • Use high-quality data – high-quality data is essential if you want your algorithmic trading AI model to work properly and deliver results
  • Ensure compliance with regulations – make sure the AI model that you use follows the relevant financial regulations and trading laws
  • Process data in real-time – speed is key for algorithmic trading and that’s why real-time data processing is essential

6 ways to use AI in fintech app development: FAQs

What is AI?

AI is a branch of computer science that aims to build machines and programs that can mimic human intelligence.

In other words, AI refers to machines and programs that can perform tasks we normally would associate with human intelligence and not computers.

How can I use AI in my fintech app?

There are a number of use cases for AI in fintech, such as:

  • Fraud detection and prevention
  • Personalization
  • AI cybersecurity
  • Robotic process automation (RPA)
  • AI customer service chatbots
  • Algorithmic trading

How can AI help keep my fintech app secure?

To keep your fintech app secure, you can use AI for fraud detection and prevention and for improved cybersecurity.

How can I use AI to improve my fintech app’s user experience (UX)?

If you want to improve your fintech app’s UX, you can use AI to improve your app’s personalization and add AI customer service chatbots for 24/7 customer support.

Need a fintech app?

Do you have an idea for a great AI fintech app but haven’t found the right company to partner with yet?

Don’t worry, we’ve got you covered.

We can help you turn your idea into reality and build a game-changing AI fintech app.

If you want to learn more, you can read about our previous work building fintech apps and get in touch with us.

Written by

Mario Zderic

Co-founder and CTO

Mario makes every project run smoothly. A firm believer that people are DECODE’s most vital resource, he naturally grew into the role of People Operations Manager. Now, his encyclopaedic knowledge of every DECODEr’s role, and his expertise in all things tech, powers him to manage his huge range of responsibilities as COO. Part developer, and seemingly part therapist, Mario is always calm under pressure, which helps to maintain the office’s stress-free vibe. In fact, sitting and thinking is his main hobby. What’s more Zen than that?

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