From manufacturing and agriculture to healthcare and education, artificial intelligence is changing how industries operate. It forecasts earthquakes, detects diseases, prevents cyberattacks, and enables real-time translation across hundreds of languages. But finance stands out as an industry especially ready for transformation.
Finance is all about numbers and patterns—things that AI is extremely good at. Artificial intelligence in fintech can help with numerous tasks, from evaluating investment risks and credit scoring to detecting financial crime and sending personalized recommendations. AI is making finance smarter, safer, and faster. If you want to keep up with the times, continue reading and get familiar with the stages of AI financial app development and its most successful use cases.
The rise of AI in finance
Artificial intelligence has been around for a while but has experienced a revival over the past years. Hundreds of companies across the world come up with new solutions that help us make data-driven finance decisions, get personalized assistance, and live more comfortably. The main trends driving AI adoption are automation of routine tasks, high-end personalization, and predictive analytics. AI frees humans for more complex assignments and helps create content and services tailored to every customer. By analyzing huge volumes of information, AI can also forecast demand and industry trends.
This technology is driving fintech app trends, transforming how financial services are delivered and used. A successful example of AI use is chatbots. For example, Erica, an AI-driven chatbot introduced by Bank of America, provides financial advisor services and manages daily transactions. Apps like Wealthfront and Betterment use artificial intelligence to deliver tailored investment advice based on individual financial goals. Another great example is Zest, which helps lenders make accurate credit decisions by analyzing payment history and transaction patterns.
Core use cases of AI in financial apps
AI is revolutionizing the financial industry, making it safer, faster, and more efficient. The core AI use cases are:
- Personalized financial insights. Fintech applications can analyze income and expenses to provide personal recommendations on budgeting, saving, and achieving other financial goals. AI money management has already proven to be more convenient and efficient.
- Credit scoring & risk modeling. Artificial intelligence can analyze a broader set of information compared to traditional models, offering more accurate assessments and insights.
- Fraud detection & security. Fraud detection with AI is extremely effective, as algorithms can process huge volumes of information and detect unusual patterns at the earliest stages.
- Automated customer service. By using AI financial assistants and chatbots, companies can significantly improve customer service, speeding up their response times, increasing the issue resolution rate, and providing 24/7 assistance in multiple languages.
- Algorithmic trading & investment management. AI uses advanced algorithms and helps traders and investors make data-driven decisions based on a wide range of market data and trends.
A Step-by-Step Guide to Building Your App
As you see, leveraging artificial intelligence helps financial companies achieve various goals and surpass their competitors. If you are ready to implement this technology or create a product from scratch, let us discuss how to build a personal finance app in just 8 steps.
1. Define your problem & AI use case
On the first stage, define the problem your financial app is going to solve. The market is extremely competitive, so search for the gaps that your AI-powered application can fill in. Study demand, explore customers’ pain points, and find out what competitors lack. When developing AI-powered finance apps, companies should also decide which monetization strategy to use: free tier, pay-as-you-go, premium alternative, etc. The strategy should align with your goals and expected results.
2. Collect and clean financial data
To build a financial app, you should identify the types of data the application needs (transaction histories, investment portfolios, etc.) and gather it, keeping regulatory compliance and encryption in mind. To provide accurate analysis and insights, it is crucial to clean the collected information of inconsistencies, errors, and duplicates. For convenience and accurate results, normalize data by converting it into a single format.
3. Choose the right tech stack
Selecting a proper tech stack is crucial for leveraging AI in banking apps. Start with AI frameworks to create and train models more efficiently. The most popular frameworks are PyTorch, Keras, and TensorFlow. To build the server-side, you will need backend environments like Node.js, which handle a variety of processes from database communication to authentication. You will also require full frameworks like Django and Ruby on Rails for scalability, quick development, and handling large volumes of data. Finally, you should take care of encryption and safety to protect sensitive information and follow international laws.
4.Train & test your model
When the tech stack is selected and the first model is developed, it should be trained on the cleaned data to check how accurate its predictions are. Test it under different conditions to make sure the future app operates correctly. If you are working with an external team, make sure they tune algorithms and validate performance so that nothing is missed.
5. Build your frontend experience
Your future application should not only be functional but also convenient and user-friendly. That is why the next step is to work on UI/UX. It should be easy to navigate the application, view financial data, and interact with AI-driven features. Creating seamless and responsive interfaces is crucial for minimizing the learning curve, reducing bounce rates, and building loyalty.
6. Integrate AI features
When you build a fintech app with AI, think of the features users might benefit from. For example, the app may send weekly reports on smarter investing and saving based on the user’s spending habits. It may also categorize transactions to make spending tracking easier. AI can also send personalized alerts when users are close to exceeding the budget. If your resources are limited, start with several core features and gradually expand functionality as you grow.
7.Ensure compliance and security
Financial information is very sensitive, so keeping data privacy laws in mind is crucial. The application should also use top-tier authentication and fraud detection protocols to identify suspicious activity in real time. Other helpful measures to implement are secure APIs, if the app connects to third-party services, regular security audits, user data anonymization, and role-based access control. By integrating these measures, your customers will get a secure experience, and you won’t have to worry about failing to meet legal requirements.
8. Launch, monitor, iterate
When the application is developed, it is the right time to let it see the world. The market is extremely demanding, so to keep customers satisfied and meet the competition, you should constantly improve and refine the app. To provide quality AI-driven financial services, you need to gather feedback, fix bugs, and optimize the app, making it more user-friendly and responsive.
Artificial Intelligence in FinTech: What Lies Ahead
Artificial intelligence is changing the financial sphere at a huge speed. It makes investment management, fraud detection, and personal finances more convenient and secure. As AI gets better, it will create advanced algorithms and models to make the industry more accessible and secure. And if you want to remain at the forefront, investing in financial app development is a necessity. Good luck!
