Scaling FinTech Innovation Through Low-Code and AI Technologies

Explore how low-code and AI technologies are revolutionizing FinTech by enhancing efficiency, reducing costs, and improving customer experience.

Mar 25, 2025

Low-code platforms reduce app development time by up to 90% and cut costs by 70%. ABN AMRO saved 20–40% on resources across 60+ apps.
AI tools like Klarna’s chatbot handle 66% of customer inquiries, saving on staffing while improving efficiency.
AI-driven fraud detection analyzes massive datasets in real time, identifying suspicious transactions faster than manual methods.
Customer personalization through AI ensures tailored services, with 78% of customers staying loyal for personalized experiences.

These tools allow financial institutions to launch products faster, enhance security, and improve customer experiences. For example, Rabobank’s mobile app, built with low-code, serves 500,000+ users with a 4.7-star rating. Together, low-code and AI are reshaping the future of FinTech.

Key Benefits at a Glance:

Technology	Benefit	Example
Low-code	90% faster development	ABN AMRO’s 60+ apps
AI	40% productivity boost	Klarna’s chatbot
Fraud Detection	Real-time risk management	PayPal’s global transaction scans
Personalization	78% customer loyalty boost	JPMorgan’s AI-driven services

Low-code and AI are no longer optional - they’re essential for scaling FinTech operations efficiently.

Low-Code and AI Basics for FinTech

Low-Code Platform Fundamentals

Low-code platforms let financial institutions create digital applications using simple drag-and-drop interfaces, cutting development time by as much as 90%.

Richard Eastley from Mendix explains:

"Low-code dev platforms... can help banks develop applications, web portals, user interfaces, and other digital customer experiences at speeds and costs traditional development can't dream of touching."

Here’s how low-code platforms benefit FinTech:

Benefit	Impact
Development Speed	Up to 7x faster development time
Cost Reduction	Up to 70% lower development costs
Team Efficiency	90% more employees involved in app creation
Market Response	70% faster time-to-market

Low-code provides the foundation for creating apps quickly, while AI adds intelligence to these applications.

How AI Works in FinTech

While low-code speeds up development, AI takes things further by introducing automation and smarter decision-making.

A great example is Klarna’s February 2024 rollout of its AI Assist chatbot, built with OpenAI. This chatbot now manages two-thirds of customer service inquiries, performing the workload of 700 service agents, and has also slashed marketing agency costs by 25%.

Scott Hofmann, Chief Revenue Officer at GFT US, highlights AI’s role in security:

"Banks will scale their ability to scan transactions for suspicious activity in real time, at a rate that would be nearly impossible to accomplish manually."

AI improves financial operations in several ways:

Boosting productivity: Automating processes with tools like large language models can increase efficiency by up to 40%.
Modernizing legacy systems: AI can cut the time and cost of modernizing outdated systems by over 50%, as shown in IBM’s work with a UK building society.
Enhancing risk management: By analyzing large datasets, AI can detect fraudulent activity in real time.

Prashant Jajodia, Managing Partner at IBM, explains AI’s role in fraud prevention:

"By analysing massive amounts of transaction data, AI can identify unusual activity and flag potential fraud before it becomes a bigger problem."

Together, low-code and AI create a powerful combination, enabling faster development and smarter financial solutions.

Building FinTech Products Faster with Low-Code

Low-Code Benefits in FinTech

Low-code platforms are changing the way financial institutions create and launch new solutions. Gartner predicts that by 2025, 70% of new enterprise applications will rely on low-code or no-code technologies. This is especially important in the FinTech sector, where getting products to market quickly is a top priority. The advantages of low-code are clear in the following examples.

Benefit	Impact	Example
Development Speed	Cuts development time by up to 90%	Development Bank of Canada reduced lending system rollout from 2.5 years to just 8 months
Cost Efficiency	Lowers operational costs by 40%	Aviva improved response times 10× while cutting costs
Resource Optimization	Reduces development resources by 20–40%	Major banks report significant savings on multiple projects
Scalability	Supports rapid user growth	Banco de la Provincia's digital wallet gained 1M users in its first month

Success Story: Cutting Development Time by Half

Rabobank offers a great example of how low-code can speed up development and boost efficiency. Their online banking platform, which manages $19.6 billion in assets, serves half a million clients with major improvements.

Here’s what they achieved:

Mobile App Success

Rabobank's app earned a 4.7 rating on iOS and Android, with over 500,000 users. Its user-friendly design allowed for quick updates based on customer feedback.
Cost Savings

By adopting low-code, Rabobank cut IT costs by 50%.

Another standout example is Collin Crowdfund. In just seven months, this Netherlands-based firm built its entire crowdfunding platform using Mendix. Launched in February 2021, the platform now serves nearly 30,000 users, enabling $315 million in investments across 1,200 loans.

"There are just not enough developers to go around, so by going low-code, you can get a lot done with ordinary developers that you can afford."

Low-code has made it possible for financial institutions to move faster while maintaining quality and compliance. These examples highlight how speed and adaptability are shaping the future of FinTech.

Making Better Customer Services with AI

Custom Financial Services Through AI

AI is transforming how financial institutions deliver personalized financial services. By analyzing massive datasets, AI systems can create experiences tailored to individual preferences and needs.

The results speak volumes - 78% of customers say they would remain loyal to their bank if they receive personalized content. This level of customization spans multiple financial services:

Service Area	AI Capability	Customer Impact
Wealth Management	Goals-based planning	Tailored investment strategies
Portfolio Management	Tax-smart rebalancing	Improved returns and tax efficiency
Retirement Planning	Predictive modeling	Personalized savings targets
Customer Communication	Channel optimization	Preferred contact methods

One standout example is JPMorgan Chase's Contract Intelligence (COIN) platform. This machine learning-powered tool extracts valuable data from legal documents, significantly reducing loan-servicing errors and speeding up processing times. These advancements highlight how AI is reshaping customer interactions in financial services.

AI Support Systems

AI is not just about personalization - it’s also revolutionizing customer support. Recent statistics reveal that 37% of U.S. adults are open to using AI tools to manage their finances, indicating growing trust in AI-driven solutions.

This shift is already happening. For instance, Jaja Finance introduced its AI assistant, "Airi", in September 2024. Airi has cut response times by 90%, independently handled over 9,000 general inquiries, and freed up human agents to focus on more complex issues.

"Airi continues to massively exceed our expectations in terms of delivering the superior experience our customers expect. Our colleagues are also seeing the benefits of its advanced capabilities by being able to spend more time dealing with customers' complex enquiries." - Dave Chan, CEO at Jaja Finance

This evolution is critical, especially as 70% of millennials and Gen Zers are willing to switch financial institutions for a better digital experience. AI support systems offer several key advantages:

Round-the-clock availability with instant responses
Consistent service across various communication channels
Personalized recommendations based on customer history
Quick resolution of routine requests

AI’s value goes beyond speed. These systems also help fill knowledge gaps. For example, 38.4% of Gen Z respondents feel they lack enough knowledge about achieving homeownership goals. AI-powered tools can offer targeted guidance and resources to address these gaps effectively.

Making FinTech Work Better with Low-Code and AI

Simplifying Office Tasks

Low-code platforms paired with AI are reshaping how financial institutions handle daily operations. Take ABN AMRO, for instance - they’ve implemented over 60 low-code applications, cutting development efforts by 20% to 40% while automating internal processes more efficiently.

ING's Document & Content Services department provides a clear example of how this plays out. Their low-code application for managing outbound customer communication in the Netherlands runs over 2,200 automated tests every four hours. As Ed Spitteler, Expertise Lead Outbound Communication at ING, puts it:

"The app runs automated test scripts every four hours to ensure functional performance".

With low-code automation, key areas of financial operations see noticeable improvements:

Task Area	Efficiency Gain	Business Impact
Document Processing	7x faster development	Smoother workflow automation
Customer Onboarding	500,000+ active users	Fewer drop-offs during sign-ups
Internal Communications	2,200+ automated tests	Enhanced service reliability
Resource Management	20%-40% reduction	Lower operational costs

Mark Bus, Product Owner of Rapid Application Development at ABN AMRO, highlights the importance of speed in software delivery:

"We are also an organization which, like every enterprise, needs a capability to deliver software fast".

This efficiency not only optimizes everyday tasks but also strengthens the foundation for better risk management, which we’ll dive into next.

Better Risk and Fraud Detection

While low-code simplifies operations, AI is revolutionizing risk management by identifying fraud in real time. These systems are capable of analyzing thousands of transactions per second to detect suspicious activity.

PayPal offers a great example of this in action, utilizing machine learning algorithms to monitor transactions across its global payments network.

When combined, low-code platforms and AI deliver a powerful toolkit for enhancing risk management:

Real-Time Monitoring: Financial institutions can quickly launch fraud detection tools that analyze transaction patterns and user behavior.
Adaptive Security: AI systems evolve continuously to counter new fraud tactics.
Comprehensive Data Analysis: The speed of low-code development paired with AI’s analytical power enables thorough risk assessments and fraud prevention measures.

Steps to Add Low-Code and AI to Your FinTech Business

Getting Started with Low-Code

Start your low-code journey with a clear plan in place. ABN AMRO's experience highlights the importance of strategy. Mark Bus, Product Owner of Rapid Application Development at ABN AMRO, shared:

"The second mode was where we didn't have an explicit strategy yet, which was leading to this huge portfolio of shadow IT applications. We decided to expedite our search, and it quickly became clear that low-code was the answer we were looking for".

Here’s a practical framework to guide your efforts:

Phase	Actions	Results
Assessment	Audit existing systems with IT team	Identify gaps and prioritize needs
Pilot Project	Choose a small, non-critical project	Achieve quick wins and gain insights
Solution Templates	Use pre-built financial templates	Speed up development
Scale Up	Expand to larger applications	Build a comprehensive system

This approach is based on the successful strategies of top financial institutions.

Adding AI to Your Services

Integrating AI into your FinTech operations can drive innovation and efficiency. The banking sector is projected to invest $84.99 billion in generative AI by 2030. Mastercard's example shows how AI can deliver real results, like detecting merchant fraud 300% faster and doubling the identification of compromised cards.

Key areas to focus on include:

Customer Service: Morgan Stanley's deployment of AI for its 900 advisors demonstrates how AI can handle large data sets securely.
Risk Management: AI platforms help banks reduce investigation times by 80%. Key capabilities include:
- Real-time transaction monitoring
- Fraud pattern detection
- Automated risk assessments
Process Automation: ING's Document & Content Services department illustrates how automating repetitive tasks can boost efficiency.

Addressing challenges upfront will ensure these solutions integrate smoothly into your operations.

Solving Common Problems

Challenge	Solution	Impact
Data Quality	Use rigorous testing protocols	Improve accuracy and reliability
Security Concerns	Leverage cloud-based test suites	Strengthen compliance and mitigate risks
Team Adoption	Start with self-organizing teams	Boost autonomy and productivity
Integration Issues	Prioritize composability	Accelerate development cycles

What's Next for FinTech

New Tools and Methods

Low-code platforms and AI are reshaping FinTech by speeding up app development. For instance, MendixChat uses advanced learning models to provide smart assistance, while Logic Bot suggests predictive steps during app creation. Here's a quick look at some key AI service integrations:

AI Service Integration	Primary Function	Business Impact
AWS Bedrock	Foundation Models	Better decision-making
AWS SageMaker	Machine Learning	More accurate predictions
AWS Textract	Document Processing	Faster document handling
AWS Rekognition	Image Analysis	Improved security verification

In healthcare, low-code platforms powered by AI have made it easier to develop telemedicine and diagnostic tools. This success hints at similar possibilities for financial services, where these technologies can drive major changes.

Looking Ahead to 2030

By 2030, FinTech is expected to evolve dramatically as technology continues to advance. Here are some areas likely to see major impacts:

Focus Area	Projected Impact
Regulatory Compliance	Automated monitoring and reporting
Cybersecurity	Better threat detection
Data Governance	Stronger privacy protection
Customer Experience	Tailored financial services

These shifts build on current progress, offering financial institutions new ways to improve operations and strategies. For example, Zurich Insurance saved approximately $875,000 by adopting low-code platforms early, showcasing the potential benefits.

To prepare for these changes, financial institutions should focus on:

Setting up cross-functional governance for AI technologies
Updating risk management strategies to tackle AI-related challenges
Establishing strong data governance policies

The Mendix Machine Learning Kit is a glimpse into the future, where AI-driven decisions become an integral part of applications.

Digital Innovation In Financial Services With Low-Code No ...

Conclusion

Low-code platforms and AI technologies are reshaping FinTech by significantly cutting development costs - up to 70% - while boosting employee involvement in development by 90%. For instance, ABN AMRO successfully reduced development resource needs by 20-40% across 60 low-code applications.

Industry leaders back these results. Paul Kammerer, Head of Business Development & Strategy at Rabobank IDC, shared:

"We decided to use Mendix in all areas where speed and agility are essential. We initially focused on three customer-facing areas: the mobile app, the client onboarding process, and the entire online banking platform."

These platforms don’t just speed up development; they also strengthen security and compliance. Data breach incidents have dropped by 40%, and compliance penalties have decreased by 50%, thanks to low-code solutions. A great example is the Development Bank of Canada, which completed its core lending system in just eight months instead of the expected 2.5 years.

Leveraging these technologies can help drive innovation, lower costs, and create secure applications that keep up with customer demands.