How Low-Code and AI Are Disrupting Traditional Banking Processes

Explore how low-code and AI are revolutionizing banking processes, enhancing efficiency, reducing costs, and improving customer experiences.

Mar 25, 2025

Low-code platforms and AI are transforming banking by reducing costs, speeding up development, and improving customer service. Banks are now using drag-and-drop tools and AI to automate workflows, detect fraud, and personalize financial services. Here's a quick summary of the key changes:

Faster Development: Low-code tools cut app development times by up to 90%. For example, a lending system project dropped from 2.5 years to just 8 months.
AI-Powered Automation: AI handles tasks like loan approvals, fraud detection, and customer interactions, saving time and improving accuracy.
Cost Savings: Automation reduces IT expenses, with some banks cutting costs by 50%.
Improved Customer Experience: AI-driven tools enable faster responses, personalized services, and better data protection.

Banks face challenges like integrating old systems, ensuring data security, and training staff. But with structured strategies, they can adapt and thrive in the digital age. Keep reading to explore specific examples, benefits, and solutions.

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Key Banking Processes Changed by Low-Code and AI

The combination of low-code platforms and AI is transforming banking operations, bringing measurable gains in efficiency and customer service. These advancements are evident in areas like loan processing, fraud detection, and personalized financial services. Here are some standout examples:

Smart Loan Processing Systems

Low-code platforms powered by AI are streamlining manual loan workflows. For instance, Direct Mortgage implemented a system that slashed loan approval times from weeks to minutes, reduced document processing costs by 80%, and enabled AI to manage 80–97% of the entire workflow. CEO Jim Beech highlighted the uniqueness of their approach:

"Nobody is doing what we're doing with Multimodal, not even close"

This system automatically handles over 200 document types - such as paystubs, bank statements, tax forms, and insurance documents - while validating applicant data with minimal human input.

Advanced Fraud Detection

AI-driven fraud detection tools are helping banks safeguard customer assets more efficiently. In 2022, U.S. consumers lost $300 million to fraudulent texts, a sharp increase from $131 million in 2021. These systems analyze large volumes of transaction data in real time, flagging suspicious activities across various channels and initiating immediate protective measures. Subhash Ramamoorthi, Director of AI Hub at IntelePeer, emphasized:

"The most beneficial aspect of AI concerning fraud detection is how much faster it is than a human"

Custom Financial Services

Low-code platforms are enabling banks to create personalized financial services at a rapid pace. Rabobank, for example, developed RaboDirect - an online banking portal managing over $18 billion for 500,000 customers - in just a few months using low-code tools. IT Architect Joost Landman shared:

"We built and released new functionality quickly with a small, business-focused team, enhancing collaboration with product owners and end users"

This ability to quickly adapt is crucial, as acquiring new customers can cost up to seven times more than retaining existing ones, with 65% of a company’s business often coming from current customers.

Main Advantages for Banks

The combination of low-code platforms and AI is changing the way banks operate, helping them stay competitive and efficient.

Faster Product Launch Times

Low-code significantly shortens development cycles - by 62% for new applications and 72% for feature updates. For instance, the Development Bank of Canada used low-code to build and launch a new core lending system in just eight months. By comparison, traditional coding would have taken 2.5 years. Similarly, ABN AMRO developed over 60 applications using low-code by the end of 2022 and scaled to 150 applications within two years. They also began migrating more than 250 sales and service forms during this time.

"If we see there is a short timeline and we need to deliver something quickly, then Mendix is the way forward."
– Benjamin Blaauw, Head of Development Automation at ABN AMRO

This speed not only accelerates time-to-market but also helps save on operational costs.

Reduced Costs Through Automation

By leveraging low-code and AI, banks can cut IT expenses and simplify workflows. Rabobank, for example, reduced its IT costs by 50% after adopting a low-code platform.

A global bank in the UK implemented a compliance automation solution that delivered impressive results:

Achieved nearly 100% accuracy in automated checks
Reviewed all cases instead of using sampling
Eliminated backlogs with real-time processing
Cut compliance process times by 80%

These savings free up resources that can be reinvested in improving customer services.

Better Customer Service

Low-code allows banks to roll out customer-focused solutions faster, improving the overall banking experience. With AI-powered tools, banks are seeing quicker response times, more personalized services, and higher customer satisfaction.

"Low-code application development gives banks access to technology they've never had before."
– Richard Eastley, Mendix

Together, these benefits show how low-code and AI are reshaping banking by boosting efficiency, cutting costs, and delivering better customer experiences.

Common Challenges and Solutions

While digital transformation offers banks numerous advantages, it also presents several challenges. Overcoming these obstacles requires thoughtful strategies to stay ahead in a competitive landscape.

Integrating Legacy Systems

Many banks still depend on older systems, which can make integration a complex task. For example, when a major European bank transitioned to a cloud-based, AI-enabled platform, they achieved notable outcomes:

50% faster transaction processing
70% less system downtime
60% reduction in manual reconciliation time

To address these integration challenges, banks can adopt:

API-first strategies to simplify connections between systems
Hybrid cloud solutions with phased rollouts to ensure critical legacy functions remain operational during the transition

Solving integration issues also helps banks focus on another pressing challenge: security.

Data Protection Requirements

Security and compliance are top priorities, especially as 14% of financial institutions reported data breaches costing over $10 million in 2024.

"Banks have to be very, very careful about the recommendations they make, or the decisions they make, being seen to be wrong. I think that would create a huge reputational problem." – Brad O'Brien, partner at Baringa's U.S. Financial Services practice

Recent concerns among U.S. banks highlight the importance of security:

Security Concern	Percentage of Banks
Data Privacy Exposure	31%
Cybersecurity Vulnerabilities	26%
AI System Usage	36%

Key security practices include:

Establishing dedicated teams to oversee AI security
Implementing strong data governance policies
Monitoring third-party vendor data handling
Ensuring proper data disposal procedures

Staff Training and Adoption

Equipping employees with the right skills is just as important as securing systems. Transitioning to low-code platforms and AI requires structured training programs and effective change management.

One bank's training initiative delivered impressive results:

Employees achieved basic proficiency within 90 days
Advanced certification was completed in 6 months
Full application development skills were acquired in one year

Key steps for success include:

Identifying internal talent for low-code development roles
Creating structured onboarding programs
Building in-house training expertise
Offering mentorship opportunities
Defining clear career progression paths

What's Next for Banking Technology

New Banking Applications

The banking industry is increasingly adopting AI-driven tools to boost efficiency. For example, JPMorgan Chase has introduced its LLM Suite to over 200,000 employees, offering tailored AI assistants for various roles.

These applications are making strides in three main areas:

Application Area	Current Implementation	Future Impact
Risk Management	Fraud detection and compliance monitoring	Real-time risk assessment with AI
Customer Service	15–20% time savings in call summaries	Automated, personalized financial advice
Process Automation	Basic task automation	Fully optimized end-to-end workflows

As these technologies evolve, they are also reshaping the responsibilities of banking professionals.

Changes in Banking Jobs

AI is driving a shift in how banking jobs are structured. According to Accenture, 75% of banking roles will be influenced by AI, with the focus being on augmentation rather than replacement.

Citi is already adopting AI to transform its operations:

Citi Stylus helps summarize reports.
Citi Assist simplifies policy searches.
GitHub Copilot supports 1,000 developers.

"As most of the repetitive administrative functions are removed from people's roles, and as AI becomes their tool, so the nature of work in the average bank will change. Bank employees will spend more time using their essentially human skills: judgement, creativity, empathy and relationship-building." - Accenture trends report

Updates to Banking Rules

As technology transforms banking, regulations are adjusting to keep pace. The EU's Digital Operations Resilience Act (DORA), set to take effect on January 17, 2025, introduces new operational standards.

Key regulatory updates include:

Data Protection and Operational Resilience: Banks need strong governance for AI systems and must manage tech risks across their supply chains.
AI Governance: Clear processes for compliance and reporting in AI applications.

A Bank of England survey found that 79% of U.K. financial services firms are already using machine learning in their operations, underlining the urgency for updated regulatory measures.

Conclusion: Steps for Success

The Development Bank of Canada managed to develop a core lending system in just 8 months using the Mendix platform, a process that would have typically taken around 2.5 years.

Here’s how organizations can follow a similar path and achieve success:

Start Small, Scale Smart Begin with a pilot project that carries minimal risk to quickly prove its value. For instance, Zurich Insurance transitioned from their outdated Lotus Notes system to a low-code platform, saving £700,000 in the process.

Focus on Strong Data Governance

Implementing a solid data governance strategy ensures security, compliance, and efficiency. Here's a breakdown:

Security Aspect	Implementation Focus	Expected Outcome
Data Protection	Automated monitoring, encryption	Maintain compliance
Risk Management	AI-powered threat detection	Speed up breach identification
Compliance	Automated checks	Minimize manual efforts

Encourage Collaboration
"The most effective low-code AI solutions are born from collaboration between business users and IT professionals. Encourage open communication and knowledge sharing to ensure that AI applications are tailored to meet specific business needs and integrate seamlessly with existing systems".
This collaborative mindset allows low-code and AI solutions to seamlessly integrate into operations, positioning them as key tools for the future of banking.

By prioritizing quick pilot projects, ensuring robust security measures, and fostering teamwork between departments, banks can break free from outdated systems and gain a strong competitive edge in the digital era.

Peter Wannemacher emphasizes, "Your bank's ongoing relevance depends on its ability to differentiate in an increasingly crowded market and that in turn depends on how rapid and effective your changes, improvement, and innovation can be".