Using AI and Low-Code to Deliver Personalized Financial Services

AI and low-code platforms are transforming financial services by enabling faster development of personalized solutions. Here's what you need to know:

• Faster Development: Low-code tools speed up app creation by up to 7x, helping financial institutions quickly meet customer demands.

• AI-Powered Insights: AI analyzes customer data to predict needs, improve risk management, and enhance decision-making.

• Automation: Tasks like KYC, AML checks, and credit assessments are streamlined, reducing costs and errors.

• Personalization: AI tailors financial products, offering real-time, customized experiences for users.

• Real-World Examples: Companies like ABN AMRO and Rabobank are already leveraging these technologies to improve efficiency and customer satisfaction.

This shift is reshaping the financial sector, combining faster development, smarter automation, and better customer experiences. Read on to learn how AI and low-code tools are driving this transformation.

Using AI for Customer Data Analysis

Getting Insights from Customer Data

AI is reshaping how financial institutions analyze customer data. By leveraging advanced techniques, AI systems can process massive datasets to uncover behavioral trends and deliver actionable insights.

One of AI's key strengths in financial data analysis is its ability to connect data across different areas. By integrating information from customer service interactions, transaction records, and more, AI provides a complete view of a customer's financial activities. This connected approach helps institutions spot spending habits, predict customer needs, uncover service opportunities, and evaluate risks with greater accuracy.

AI systems are also capable of processing billions of global data points, offering insights that traditional methods simply can't match. As Piyush Bothra, AWS Cloud Technologist, puts it:

"Think about the billions of requests coming through financial systems from various sources and places across the globe. It's just not possible to analyze all the requests and data flows and still get insights using traditional analytics. That's where AI can help."

These capabilities enable financial institutions to make impactful changes in their operations and services.

Examples in Banking and Finance

AI-powered data analysis is already driving improvements in the financial sector. Research shows that generative AI could boost front-office productivity at major investment banks by 27% to 35%. Additionally, McKinsey & Company estimates that AI could contribute $200 billion to $340 billion in yearly value to the banking industry.

One real-world example is the use of AI for automated identity document processing. This technology streamlines anti-money laundering compliance by significantly reducing the time required for manual reviews.

"It's easy to envision a time when the traditional practice of picking up the phone to converse with a broker or retirement advisor about investment strategies will undergo a digital transformation. Clients will be able to engage in rich, informative dialogues online with chatbots powered by advanced large language models and bespoke content generation capabilities."

To fully benefit from AI-driven analysis, financial institutions must prioritize high-quality data. Recent studies reveal that 24% of insurers are concerned about their data quality for tasks like risk evaluation and pricing. Addressing these concerns involves:

• Establishing robust data governance frameworks

• Using automated data validation tools

• Ensuring compliance with data security and privacy standards

• Standardizing data formats across systems

These steps are essential to maximize the potential of AI in customer data analysis.

Building AI Prediction Models with Low-Code

Simplifying Prediction Models

Low-code platforms are changing how financial institutions develop AI models. These tools let teams create AI applications without needing extensive programming skills, cutting development time from weeks to just hours. By speeding up model deployment, this approach builds on earlier advancements in customer data analysis. Features like pre-built components and drag-and-drop tools make it easy for developers to integrate pre-trained models, such as those from the ONNX Model Zoo, and customize them for specific financial tasks.

Hans de Visser, Chief Product Officer at Mendix, highlights the benefits:

"Enterprises using low-code will be able to extract more value from AI in an efficient way using the new features of the Mendix 10 platform."

Modern low-code platforms streamline integration. Developers can deploy models directly within the application runtime, keep data processing local to preserve privacy, optimize for faster response times, and use AI-assisted tools to follow best practices.

Applications in the Financial Sector

Financial institutions are using low-code AI prediction models in key areas. For example, PostNL built a machine learning model with low-code tools to handle 1.5 million daily delivery requests. Their system achieves sub-millisecond response times while ensuring data privacy by avoiding third-party services. This demonstrates how low-code AI models can transform financial operations.

Guy Mettrick, Industry Vice President for Financial Services at Appian, explains the growing challenges:

"Changing regulations, such as Basel III, will continue to impact banks' appetite for risk and drive changes in the way they manage risk processes across their organization. Banks will experience heightened scrutiny of the processes behind their risk management and credit scoring decisions. There's also going to be a strong focus on auditability across the enterprise, and that's precisely where data fabric and AI automation platforms can make a difference."

To make the most of low-code AI prediction models, financial institutions should focus on the following:

• Establishing Data Quality Standards: Develop a robust data fabric to connect systems and ensure accurate input for AI models.

• Incorporating Human Oversight: Combine AI-driven insights with human expertise to improve decision-making and manage risks effectively.

• Ensuring Model Transparency: Use user-friendly tools to explain AI decisions, which is especially important for meeting regulatory requirements.

AI-Driven Process Automation in Low-Code

Improving Financial Workflows

Low-code platforms powered by AI are transforming operations in financial institutions by streamlining processes without sacrificing security or compliance. According to McKinsey, generative AI could save the global financial services industry between $200 billion and $340 billion annually.

By combining AI with low-code platforms, financial institutions can automate tasks like:

• Know Your Customer (KYC) verification

• Anti-money laundering (AML) checks

• Credit assessments

• Collateral management

• Risk analysis

AI-driven testing has also shortened development cycles, enabling faster rollouts of financial solutions while maintaining high-quality standards. These advancements are paving the way for more engaging customer experiences.

Smart Chatbots for Customer Service

AI-powered chatbots are taking digital customer interactions to the next level. These tools handle routine inquiries, process transactions, and even offer personalized financial advice, making them a critical asset for responsive customer service.

John Trapani, Industry Leader for Financial Services at Appian, envisions the future of chatbot technology:

"It's easy to envision a time when the traditional practice of picking up the phone to converse with a broker or retirement advisor about investment strategies will undergo a digital transformation. Clients will be able to engage in rich, informative dialogues online with chatbots powered by advanced large language models and bespoke content generation capabilities."

For financial institutions adopting AI chatbots through low-code platforms, key priorities include:

• Data Security: Ensure customer data is protected during interactions.

• Seamless Integration: Link chatbots with existing banking systems and databases.

• Human Handoff: Create clear protocols for escalating complex issues to human agents.

To fully harness the benefits of AI automation, financial institutions should invest in training employees to collaborate effectively with these systems. Combining human expertise with AI ensures efficient operations while maintaining the personal touch customers expect.

Better Customer Experience Through AI and Low-Code

Custom Financial Products and Services

AI-powered low-code platforms are changing how financial institutions develop tailored solutions for their customers. These tools allow organizations to create personalized financial products, with 44% of businesses now using AI to predict customer needs and refine their offerings. This approach has led to notable improvements in both revenue and customer satisfaction.

A great example of this is First Horizon Bank. Erin Pryor, their Chief Marketing Officer, shares how AI helps them connect with customers more effectively:

"Using AI-powered algorithms, we can observe individual behaviors, interests and demographics to identify those actively seeking financial products or services. This helps us reach users at the best times, resulting in more efficient ad spend... Our bankers can use this information to enhance their conversations and provide better solutions for clients."

AI-driven personalization is making a difference across various financial services:

Financial institutions are also embracing real-time personalization to make these offerings even more impactful.

Instant Personalization Features

Generative AI takes personalization to the next level by creating experiences that feel uniquely tailored to each customer. Ashvin Parmar, global head of Insights and Data for Financial Services at Capgemini, explains:

"You can go beyond personalization and towards more individualism... Generative AI not only personalizes existing content but helps you create entirely new and unique experiences tailored to individual preferences."

By combining AI with low-code platforms, financial institutions can quickly roll out features that adapt in real time to customer preferences. First Horizon Bank exemplifies this approach, as Erin Pryor highlights:

"With the financial sector being highly regulated, we make sure to intentionally use and test AI to understand our clients better while simultaneously respecting their boundaries."

Key features enabled by this integration include:

• Predictive Analytics: Tools that forecast customer needs.

• Dynamic Content: Dashboards that adjust to individual user preferences.

• Smart Notifications: Alerts triggered by customer activity.

• Automated Decision Support: Real-time advice for financial decisions.

Nearly half of companies using AI-driven personalization report clear gains in revenue, productivity, and profit margins. Additionally, 26% see exceptionally strong and consistent benefits. This approach is reshaping how financial institutions interact with their clients, fostering deeper connections and driving profitability.

How AI Agents Are Transforming Financial Services, One Call ...

Implementation Guide: AI and Low-Code in Finance

To make the most of AI and low-code technologies in finance, a clear and organized implementation plan is crucial.

Data Quality Standards

Good data is the backbone of successful AI systems in finance. As Andrew Ng puts it:

"If 80 percent of our work is data preparation, then ensuring data quality is the most critical task for a machine learning team".

Here are the key elements of maintaining data quality:

For example, Airbnb's "Data University" initiative boosted engagement with their data science tools from 30% to 45% of weekly active users.

Human and AI Collaboration

Balancing AI automation with human expertise is key. Deloitte estimates that the world's top 14 investment banks could increase front-office productivity by 27% to 35% using generative AI. This highlights the importance of combining AI-driven platforms with human oversight.

By designing workflows where AI handles data-heavy tasks and humans concentrate on strategic decisions, financial institutions can better manage risks while staying compliant with regulations.

Company-Wide Implementation Steps

Turning strategy into action requires clear, organization-wide steps. According to McKinsey & Company, AI could add $200 billion to $340 billion in value to the banking industry annually. Here’s how organizations can tap into that potential:

1. Strategic Planning

   • Define clear objectives.

   • Gain leadership buy-in.

   • Develop a roadmap for AI integration.

2. Infrastructure Development

   • Build a data fabric architecture.

   • Introduce process automation platforms.

   • Set up monitoring systems for AI performance.

3. Team Development

   • Offer upskilling programs.

   • Create dedicated data quality teams.

   • Provide training in AI and analytics.

4. Continuous Improvement

   • Regularly audit AI systems.

   • Safeguard against data poisoning.

   • Monitor key metrics for implementation success.

John Trapani, Industry Leader at Appian, envisions a shift in how clients interact with financial services:

"It's easy to envision a time when the traditional practice of picking up the phone to converse with a broker or retirement advisor about investment strategies will undergo a digital transformation. Clients will be able to engage in rich, informative dialogues online with chatbots powered by advanced large language models and bespoke content generation capabilities."

Conclusion: Next Steps with AI and Low-Code

The financial services industry is undergoing a major digital shift. With the global low-code market currently valued at nearly $15 billion and projected to grow significantly within the next five years, the potential for change is immense.

The path forward combines careful planning with measurable results. Here’s what the data reveals about AI and low-code adoption in the finance sector:

These numbers highlight how AI and low-code can boost efficiency and increase revenue. As Jeff Pedowitz, President and CEO of The Pedowitz Group, points out:

"Effective regulation in the AI era also means balancing innovation with consumer protection. It's about creating an environment where financial institutions can leverage AI for growth and efficiency while ensuring that these advancements do not compromise consumer rights or market stability."

To build on earlier insights into faster development and personalization, focus on these three critical factors for successful implementation:

• Data Quality: Establish strong validation and standardization processes to ensure accuracy and reliability.

• Team Enablement: By 2024, it’s expected that 80% of non-IT professionals will contribute to IT product development, with over 65% using low-code tools. Comprehensive training programs are essential.

• Measured Implementation: Track metrics like processing time and cost reductions. Some sectors have already achieved up to an 80% decrease in processing time for automated workflows.

These steps emphasize the importance of high-quality data, empowered teams, and measurable outcomes. As you navigate this transformation, prioritize security and compliance to ensure sustainable growth.

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