At this point, one can confidently say that AI is everywhere and in everything that has some form of digital module. But the functionality and application of AI differ across various AI-equipped systems and platforms.
Today, we’ll look at how AI functions in fintech apps, the core use cases in fintech apps and remittance services, as well as the benefits.
But before we dive in, what actually counts as a fintech app?
Fundamentally, a fintech app refers to any mobile, web, or desktop application that provides financial services digitally.
Today, many fintech apps have different AI features for different purposes, from customer support and personalization to app security.
That said, let’s take a broader look at some of the core applications of AI in fintech apps.
Core Applications of AI in Fintech Apps
The core use cases of AI in fintech apps generally center on enhancing user experience. This common theme can be seen in how AI is used to streamline various processes, including customer service, fraud detection, credit assessments, and payment processing.
Customer Service
Conversational AI in virtual assistant chatbots used by fintech apps helps provide meaningful 24/7 customer service, even on bank holidays.
The best thing about the AI-powered virtual assistants is that their responses are quick and hyper-personalized, getting better with their responses every time they interact with a given customer.
For the customers, this means that, for minor disputes and queries, they don’t have to stress with back-and-forth emails, which could drag on for days.
In return, AI virtual assistants lift a huge burden off customer service teams by handling the mundane tasks while also allowing them more room to focus on more demanding customer support issues.
Fraud Detection
Ever wondered how fintech apps are able to flag and halt unusual spending on your account even when you are not close to your device?
With AI, it’s simple, really. AI models in fintech can learn and understand patterns that indicate how you use the app. Suppose you create an account on an international money transfer app to send money to BanCoppel for your folks in Mexico. You’ve been religiously sending a certain range of amount to this account, but one day, the transfer of a larger amount to some other bank in another country gets initiated on your account.
In such an instance, the AI security feature, which now understands your typical use of the app, automatically detects the transaction as an anomaly, flags the transaction, and immediately sends a notification to you about the transaction, saving you from potential financial loss.
Additionally, AI processes large datasets gathered from app users in real-time as well as from the fintech company, continuously learning how to detect and manage new account security threats.
Credit Assessments
AI-credit risk assessment tools have been proven to be superior to traditional credit risk assessment tools in terms of accuracy, cost savings, predictions, decision-making, and overall efficiency.
Credit Karma, for instance, uses AI to:
- Provide personalized recommendations to users based on their financial profile.
- Predict approval odds, shielding users from rejections that might impact their credit scores.
- Provide credit score guidance to users after considering various financial choices.
On a broader scale, credit-focused fintech providers also use AI to predict customers’ creditworthiness, minimizing default rates.
Payment Processing
The role of AI models in fintech apps is quite extensive across the various types of payment processing, from credit and debit card transactions, contactless payments, and instant payments to digital wallets, check processing, and cryptocurrency.
For the sake of brevity, let’s consider check processing and QR payments, a form of contactless payment.
Financial institutions use AI-powered Optical Character Recognition (OCR) to automatically verify checks, thereby preventing check fraud and reducing processing errors.
Within the QR payment space in fintech, we are seeing innovations built around AI to help reduce fraud incidents. In fact, CoinLaw indicates that AI-powered QR payments might reduce fraud incidents year-on-year by 15% in 2025.
From customer service and fraud detection to credit assessment and payment processing, among many other use cases, it is clear that AI is undeniably a game-changer for fintech processes and industries.
As you move across fintech industries, including neobanks, personal finance, remittance, and investment, you get to see varying degrees of AI applications in fintech apps.
For context, let’s take a quick look at the remittance industry.
AI in Fintech Remittance Services
For many low- and middle-income countries with relatively substantial migrant populations, remittances are increasingly becoming the largest avenue of foreign exchange earnings for both the residents and the governments.
Interestingly, AI has a role to play in all these.
One of the biggest barriers to fintech growth in the early years was KYC compliance, especially the attempt to include underserved and unbanked populations. As much as fintechs were hailed as necessary disruptors that brought faster and cheaper international money transfers, there was still a need for fintech firms to be compliant with long-standing global regulations.
AI-powered systems helped fintech remittance services answer the question of how to send money internationally while staying compliant.
How so?
International Finance Corporation (IFC), a member of the World Bank Group, explains it best.
According to IFC, increasing the volume of annual remittances also meant an increased exposure to fraud and cyber-attacks. Increasing threat indicates more need for remittance service providers to bolster security across their apps, in line with global standards. However, the tools necessary for this are prohibitively expensive.
Thankfully, remittance service providers today can use AI-powered compliance technology to reduce costs and meet KYC requirements while equally improving overall transaction security for both senders and receivers.
In the grand scheme of things, the integration of AI systems into remittance services underscores a huge win for global financial inclusion.
Benefits of AI in Fintech Apps
We have seen some of the major use cases of AI across fintech app processes, as well as in the fintech industry. With every instance, the benefits of AI in fintech become clearer at the end-user, company, and global levels.
For starters, AI has revolutionized how fintech providers approach customer service by guaranteeing 24/7 support, even on bank holidays, ensuring that customers get faster and more personalized responses while support teams focus on more sensitive issues.
The fact that AI is able to process large datasets in real-time and continuously learns from new data translates to well-informed and faster decision-making, as well as more proactive fraud detection and prevention than ever before.
On an even broader scale, AI helps fintech companies meet regulatory compliance, further enhancing risk reduction, affordable service delivery, and financial inclusion.
Other benefits of AI in fintech include:
- Streamlining payment processing
- Personalized user experience and financial product recommendation
- Better financial predictions, especially during credit risk assessment.
- Reduction of human errors and bias in interpreting financial data.
Generally, AI benefits result in elevated user experience for customers and enhanced scalability and productivity for fintech companies.
Conclusion
Across all the industries captured within the fintech sector, including but not limited to international money transfer, insurance, financial advisory, and credit management, AI models are already streamlining processes and systems to improve the overall efficiency and performance of mobile, web, and desktop apps offered by providers in these industries.
There’s a lot to look forward to in the marriage between these two disruptors with undeniable global impact.
Generally, we can expect that as AI models become more advanced, fintechs are going to innovate more and more around them to make their apps much better for the users
