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Personal finance is finally catching up to the rest of the digital world. Gone are the days of juggling spreadsheets, manual tracking, or trying to remember how much you spent last weekend. AI finance assistance apps like Plum are changing the game—automating savings, analyzing spending, and offering smart investment tips, all through your phone.
What sets apps like Plum apart isn’t just automation it’s intelligence. These tools quietly study a user’s income, habits, and goals and then work behind the scenes to optimize their financial life. They act like a personal finance coach who never sleeps, always one step ahead with recommendations tailored to you.
In this blog, we’ll take you behind the scenes of building a smart finance app like Plum. From essential features and step-by-step development to the tech stack and cost considerations, this guide is here to help you turn your app idea into a powerful financial tool that users can actually trust and enjoy using.
Market Insights of AI in Finance Market
The AI in Finance market is projected to experience significant growth, expanding from USD 38.36 billion in 2024 to USD 190.33 billion by 2030. This represents a compound annual growth rate (CAGR) of 30.6%. This growth reflects the increasing adoption of AI and automation across the financial services sector as businesses look to streamline operations, improve customer service, and optimize costs.
Among the key AI technologies in demand within the finance sector are chatbots and virtual finance assistants. These tools have become integral to modern financial platforms, primarily because they automate repetitive tasks while significantly reducing operational costs. Additionally, they enhance user experience by offering real-time responses and personalized interactions, adapting to individual user behaviors.
Why Are AI Finance Apps Gaining Popularity?
AI finance apps are gaining popularity due to their ability to streamline financial management, offering real-time insights and automation for better decision-making. These apps help users save time, reduce errors, and improve overall financial planning.
1. Automated Savings and Budgeting
AI finance assistance apps make managing money simpler by automatically tracking expenses and directing funds toward savings goals. This eliminates the need for manual budgeting, helping users stay organized with less effort.
2. Actionable Insights Through Data Analysis
By reviewing spending habits and analyzing financial data, these apps offer smart, personalized advice. Users can better understand their financial behavior, adjust spending, and make more confident decisions about saving or investing.
3. Tailored Financial Management
These apps adapt to each user’s unique habits and financial goals. Instead of a one-size-fits-all approach, users get recommendations and tools that reflect their real-world needs, making day-to-day money management more effective.
4. Simplified Financial Control
With built-in automation and minimal user input required, AI-powered finance apps allow users to stay in control of their money without the stress. They provide a clear view of finances and take the guesswork out of planning and decision-making.
What is an AI Finance Assistance App?
An AI Finance Assistance app is a digital tool designed to help individuals manage their finances by using artificial intelligence. These apps rely on machine learning and data analytics to understand users’ financial behavior and provide personalized insights. The main goal of an AI finance assistant is to automate crucial financial tasks, such as budgeting, saving, and tracking investments. This allows users to stay on top of their finances without needing to manually handle every detail.
Examples of Some Market Leaders in The Market
There are several popular AI finance assistance apps making a significant impact in the market, including Plum, Cleo, and Chip. Each of these apps offers unique features that cater to different aspects of financial management.
- Plum is known for its automated savings feature. It analyzes users’ spending patterns and automatically transfers small amounts of money into a savings account, making saving effortless.
- Cleo focuses on budgeting and offers real-time financial insights. It provides a conversational interface, allowing users to interact with the app via a chatbot to track spending and set savings goals.
- Chip uses AI to automate savings by analyzing users’ spending habits. The app moves money into savings automatically, ensuring users save without having to think about it.
Why Investors Should Pay Attention to the AI Finance Assistant App Market?
The rise of AI finance assistance apps is more than just a passing trend. It represents a shift towards smarter and more efficient financial management tools, making it an attractive space for investors and entrepreneurs. The demand for such solutions is clear, and there is significant room for innovation.
Startups like Plum have already proven the potential of AI finance assistance apps. Despite launching during a challenging economic period, Plum raised $10 million in 2020 and now serves over a million users across the UK and Europe. It offers smart savings, automated investments, and bill-reduction tools, showcasing steady growth even in uncertain conditions.
Cleo, another leader in the space, raised $80 million in 2023, achieving a valuation of $500 million. The app has over 7 million users, primarily young adults, and helps them manage budgets, avoid debt and build financial habits. Cleo’s strong U.S. revenue, exceeding £51.6 million, highlights its effective monetization strategy.
In the business sector, Planful has proven that AI tools can support enterprise-level financial planning. The company secured over $91 million in funding and surpassed $100 million in annual revenue, indicating the broader applicability of AI in finance beyond individual users.
Emerging startups like FinChat, Hebbia, and Numeric are targeting niche opportunities, including AI-generated equity research, document analysis, and automated financial closing. These players are helping shape the future of finance tools and project impressive growth, with FinChat expecting to hit $10 million in recurring revenue by 2025.
With strong market validation and user demand, AI finance assistance apps present significant investment opportunities. Investors have the chance to fund the next wave of innovation and fill gaps that will benefit both individuals and businesses alike.
Market Gaps AI Finance Apps Can Still Address
Despite strong momentum in the AI finance app space, there are still several gaps in the market. Targeting these areas can give new apps a competitive edge and help them stand out.
1. Better Customer Support and Faster Response Times
Some existing apps, like Plum, have faced criticism for slow transaction processing and poor customer service. Offering fast, reliable support across multiple channels can build user trust and improve retention.
2. All-in-One Financial Planning Tools
Most apps focus on basic budgeting and saving. There is an opportunity to provide users with more comprehensive tools, including tax optimization, retirement planning, and goal tracking based on different life scenarios.
3. Ethical Monetization Practices
Some platforms have drawn negative feedback for aggressive upselling or promoting high-interest financial products. A more ethical model, focused on paid premium features that offer clear value, can help establish long-term user loyalty.
4. Smarter Personalization and Predictive Tools
Many apps still deliver generic financial tips. A more advanced AI system that adapts to individual habits, forecasts future expenses, and identifies smart investment opportunities can make a real difference.
5. Support for Financial Literacy and Inclusion
A lot of apps assume users already have financial knowledge or a credit history. A truly inclusive product would educate users, help them build credit, and give access to core tools regardless of their background or income level.
6. Full Integration with Existing Financial Tools
Users often rely on multiple platforms to manage their money. A finance assistant app that connects easily with bank accounts, credit cards, payroll systems, and investment services can provide a seamless experience.
7. Continuous Updates Based on Market and User Behavior
AI systems that stay static lose relevance over time. Building a model that evolves based on user input, market trends, and economic shifts will keep the app accurate and helpful in the long run.
Must-Have Features of an AI Finance Assistance App
AI finance apps like Plum revolutionize personal finance management by automating budgeting, saving, and investment tracking. Essential core features keep users engaged, while advanced functionalities offer a competitive edge in a crowded market.
A. Core Features
Core features of an AI finance assistance app include automated budgeting, expense tracking, and real-time financial insights that streamline financial management and improve decision-making.
1. User Authentication & Security
Security is paramount in any financial application. Robust user authentication, such as biometric verification (face or fingerprint recognition), two-factor authentication (2FA), and encryption, is essential. These measures protect users’ financial information and prevent unauthorized access, ensuring trust and safety.
2. AI-Powered Budgeting & Expense Tracking
AI can automate budgeting by categorizing expenses and tracking spending patterns in real time. The app learns from the user’s financial behavior and provides insightful suggestions for optimizing spending. This feature helps users stay within their budget while understanding their financial habits more clearly.
3. Automated Savings & Round-Ups
One of the most convenient ways to save is through automation. With features like automated savings and round-ups, users can effortlessly build savings by automatically transferring small amounts into a savings account. Round-ups round up purchases to the nearest dollar and save the difference, making it easier to accumulate savings over time without extra effort.
4. Smart Notifications & Alerts
Smart notifications help users stay on top of their finances by sending timely reminders for bill payments, low account balances, or overspending. These alerts help users avoid missed payments and stay disciplined with their financial goals.
5. Investment & Wealth Management
Integrating a robo-advisor into the app provides users with personalized investment recommendations based on their risk preferences and financial goals. The app can also automatically adjust portfolios based on market trends and user progress, simplifying wealth management.
6. Chatbot & Voice Assistance
AI-driven chatbots and voice assistants offer users real-time support, guiding them through app features, answering queries, and providing financial advice. This feature makes the app more interactive, creating a seamless and user-friendly experience.
7. Multi-Bank Account Aggregation
Integrating with multiple bank accounts through open banking APIs allows users to view and manage all their accounts in one place. This feature gives a comprehensive view of their financial situation, making it easier to track spending, savings, and investments across different platforms.
8. Personalized Financial Insights
Machine learning algorithms can analyze users’ spending patterns to offer personalized financial insights. This helps users understand their financial behavior and make smarter decisions regarding budgeting, saving, and investing.
B. Advanced Features for Competitive Edge
Advanced features such as predictive analytics, personalized recommendations, and natural language processing for queries provide a competitive edge by offering tailored solutions and enhancing user experience.
1. Predictive Cash Flow Analysis
AI can predict future cash flow based on past spending data, helping users plan for upcoming expenses or changes in income. This proactive approach allows users to adjust their savings or investments, ensuring they are financially prepared for future fluctuations.
2. Credit Score Monitoring
Credit score monitoring helps users stay informed about their credit health by providing real-time updates. The app can also offer tips on how to improve their credit score, helping users make more informed decisions when applying for loans or credit cards.
3. Subscription Management & Cancellation
Managing recurring subscriptions can be challenging, but AI-powered apps can track them and notify users before renewal dates. The app can also recommend canceling unnecessary subscriptions, helping users reduce wasteful spending and save money.
4. AI-Driven Debt Payoff Strategies
Debt management tools powered by AI can help users create personalized strategies to pay off outstanding debts, such as credit cards or loans. By using methods like the avalanche or snowball technique, the app can recommend the most efficient repayment approach based on the user’s financial situation.
Development Process for an AI Finance Assistance App
The development steps of this app involve steps like market research, defining key features, choosing the right tech stack, and integrating AI models. The goal is to create an intuitive platform that effectively addresses user needs while ensuring scalability and security.
1. Consult & Market Research
First, consult a reputable development agency like IdeaUsher to get help with detailed market research and business planning are essential. This involves analyzing competitors like Plum, Cleo, and Chip to identify gaps and opportunities, defining your target audience such as young professionals or freelancers, and ensuring full compliance with regulations like GDPR, PSD2, and national banking laws. A clear monetization strategy should be in place early, whether through subscriptions, freemium models, or financial partnerships. Finally, outline your Minimum Viable Product (MVP) by separating must-have features from advanced AI capabilities to reduce time-to-market and cost.
2. Frontend Development
The front end of the app is where users engage daily, so it must be seamless, intuitive, and responsive. For mobile development, React Native is ideal for cross-platform apps, while Swift (iOS) or Kotlin (Android) is best for native builds. Web dashboards can be built using React.js or Angular. UI/UX design tools like Figma or Adobe XD help prototype clean, user-friendly interfaces. Prioritize features like secure login with biometrics or 2FA, real-time transaction updates, and interactive dashboards for budgeting and savings to keep users engaged and informed.
3. Backend Development
The backend forms the operational core of your app, handling data, logic, and security. For scalability and speed, use Node.js or Python with Django or Flask frameworks. A robust data stack includes PostgreSQL for structured data and Firebase for real-time syncing. Third-party integrations such as Plaid or Yodlee enable secure bank data access, while Stripe handles in-app payments. Your backend should include encrypted user authentication, real-time transaction processing, and financial analytics to support AI and reporting features effectively.
4. AI/ML Frameworks
To make your finance app intelligent, you’ll need to implement AI and machine learning frameworks. TensorFlow and PyTorch are commonly used for model training, while Dialogflow or GPT-4 can power natural language chat interfaces. For analysis, Pandas and NumPy are key. These tools allow you to create spending behavior models, cash flow predictions, and personalized savings recommendations, giving your app a strategic edge over static budgeting tools.
5. AI Model Development & Integration
Effective AI development starts with gathering and preparing quality data. This includes collecting anonymized user transaction data, cleaning it for accuracy, and then using it to train models that detect savings opportunities or risky spending patterns. Once trained, deploy these models using platforms like AWS SageMaker or Google Cloud AI, then link them with your backend for real-time insights and financial guidance that feels personalized to each user.
6. Full Platform Integration & Development
After individual modules are built, it’s time to connect the frontend, backend, and AI models into one synchronized system. This integration must support real-time transaction syncing, instant notification delivery, and low-latency AI responses, especially critical in finance apps where delays can erode user trust. Ensuring seamless cross-module communication will result in a consistent, reliable experience across mobile and web platforms.
7. Testing & Launch
Before public release, your app must go through rigorous testing. Conduct security tests, including penetration testing and GDPR compliance checks, to ensure user data is fully protected. Perform beta testing with a controlled group to identify any bugs or performance issues. Finally, prepare for launch by optimizing for visibility on app stores using ASO (App Store Optimization) techniques, helping your app stand out from competitors from day one.
Cost of Developing an AI Finance Assistance App
Creating an AI-driven finance assistance app involves multiple development stages, each influencing the overall cost based on the time, technical complexity, and resources required.
Development Cost Breakdown Table
| Development Step | Task | Description | Estimated Cost |
| Market Research & Business Planning | Strategy and Compliance Setup | Involves analyzing competitors, understanding regulations, and defining revenue models | $1,500 – $3,000 |
| Frontend Development | Mobile and Web UI | Designing and developing interfaces for both mobile and web platforms | $2,000 – $15,000 |
| Backend Development & Database | Server Logic and Data Handling | Building server-side logic, authentication, data encryption, and API connections | $3,000 – $20,000 |
| AI/ML Frameworks | AI Logic Architecture | Choosing suitable AI frameworks and building logic for budgeting and insights | $3,000 – $18,000 |
| AI Model Development & Integration | Data Training and Model Deployment | Collecting data, training the AI, and integrating models with the backend | $2,500 – $15,000 |
| Full Platform Integration | System Optimization and Testing | Ensuring seamless connection between frontend, backend, and AI modules | $1,500 – $10,000 |
| Testing & Launch | Quality Assurance and Go-Live | Performing GDPR checks, user testing, and publishing on app stores | $1,500 – $9,000 |
this range indicates the expenses involved in creating a functional, secure, and scalable application. The ultimate budget is influenced by the degree of customization, the expertise of the team, and the timeline.
What Affects the Development Cost?
Several factors can influence the final cost of building an AI finance assistance app:
- Feature Complexity: The more advanced the features, the more development hours are needed. Tools like predictive analytics, real-time cash flow analysis, or AI-driven insights require more engineering effort and customization.
- Third-Party Integrations: Integrating services like Plaid, Yodlee, or Stripe involves licensing or usage fees. These integrations may also require additional backend configuration, increasing both time and cost.
- Data Security and Compliance: Finance apps must comply with regulations such as GDPR or PSD2. Implementing secure encryption protocols, access control, and compliance checks adds to development time and cost.
- AI Model Quality: More accurate predictions demand better-trained AI models. This can involve working with large datasets, longer training cycles, and advanced infrastructure, especially if using cloud-based tools or APIs.
- Platform Choice: Developing for iOS, Android, and web simultaneously means more work on design, development, and testing. Supporting multiple platforms raises costs but also expands user reach.
- Design Quality: A sleek interface with custom animations, responsive layouts, and smooth user interactions enhances the user experience. However, higher design quality also increases design time and cost.
How to Monetize Your AI Finance Assistance App?
Monetization options for an AI finance assistant app include subscription models, freemium plans with premium features, or enterprise licensing. Choosing the right model depends on your target audience and the unique value your app offers.
1. Subscription Model (Premium Features)
A subscription model ensures a steady revenue stream by charging users a recurring fee for premium features. This approach helps retain users over the long term by offering advanced tools such as financial planning, budgeting analytics, and investment tracking. You can introduce different pricing tiers to cater to various user needs, allowing the app to appeal to a broad audience.
Example: Monarch Money is an all-in-one personal finance platform offering budgeting, investment tracking, and financial planning tools. The app follows a subscription model, charging users $14.99 per month or $99.99 annually for premium access. This strategy has contributed to its growth as a top budgeting app.
2. Freemium Upselling
With the freemium model, you can attract users by offering essential features for free, then upsell them on more advanced tools or benefits. This strategy encourages user acquisition and allows them to experience the value your app provides. Once users see the value of premium features, they are more likely to convert to paying customers.
Example: Plum offers a free Basic plan with automated savings and access to 1,200 US stocks. For advanced features, Plum has three paid tiers: Pro (£2.99/month), Ultra (£4.99/month), and Premium (£9.99/month). These tiers provide higher savings interest rates, diversified investment funds, advanced budgeting tools, and more stock options. This has helped Plum grow its user base and increase paid subscriptions.
3. Affiliate Partnerships
Affiliate partnerships allow your app to earn revenue by recommending third-party financial products or services. When users sign up or complete transactions based on your recommendations, your app earns a referral fee. This strategy generates income without directly charging users.
Example: ET Money partners with financial institutions to offer products like mutual funds, insurance, and fixed deposits through its platform. The app earns a commission for every transaction made through its recommendations, making its revenue closely tied to user engagement.
4. Data Insights (Anonymized) for Financial Institutions
By aggregating and anonymizing user data, your app can generate revenue by selling insights to financial institutions. These insights help institutions with market research, customer behavior analysis, and the development of targeted financial products. It is crucial to comply with data privacy regulations and maintain transparency with users regarding how their data is used.
Example: FinChat provides an AI-powered platform that generates investment research and financial documents. By analyzing anonymized user data, it offers valuable insights to financial institutions, aiding in their decision-making processes.
Conclusion
Building an AI finance assistance app like Plum means striking the right balance between smart automation and a user-friendly experience. Features like budgeting, automated savings, personalized insights, and regulatory compliance all play a crucial role in gaining user trust and delivering lasting value. The goal is to let the technology do the heavy lifting in the background while keeping the experience simple and helpful on the surface. As more people lean toward financial tools that offer convenience and personalization, developing an app in this space can put you ahead in the evolving world of digital finance.
Ready to Build an AI Finance Assistance App With IdeaUsher?
At Idea Usher, we bring deep expertise with over 500,000 hours of hands-on development, led by a team of seasoned engineers from MAANG/FAANG companies. We specialize in creating smart finance apps that simplify budgeting, automate savings, and deliver real-time financial insights.
Our platforms are built to support personalized money management, from tracking expenses to setting custom savings goals and integrating directly with banking systems.
We focus on combining secure architecture with a seamless user experience to help people take control of their financial lives.
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FAQs
An AI finance assistance app like Plum typically includes features such as automated savings, budgeting tools, spending insights, and investment recommendations. The AI works by analyzing user spending patterns and providing personalized suggestions to improve financial habits. Essential integrations with bank accounts allow the app to pull transaction data and offer real-time insights, helping users manage their money more effectively.
AI enhances finance apps by automating financial tasks and providing tailored insights. It can track spending, categorize transactions, and offer suggestions on how to save money based on user behavior. Additionally, AI-powered chatbots and voice assistants help users interact with the app easily, offering advice and simplifying complex financial concepts for those without a financial background.
The cost of developing an AI finance app varies widely based on the features, level of AI integration, platform compatibility, and security requirements. A basic app may cost around $10,000, but more complex versions with advanced AI features can exceed $100,000. This budget should also account for design, backend infrastructure, and ongoing support after launch.
Yes, adhering to financial regulations is essential when developing an AI finance app. These apps handle sensitive data, and compliance with standards like GDPR, PSD2, and local financial regulations is required to ensure user data is protected. By following these regulations, the app can maintain user trust and avoid potential legal or security issues.
