The Evolving Value of the Democratization of AI with Citizen Developers in Financial Services

The financial services industry is undergoing a profound transformation, driven by the democratization of artificial intelligence (AI). This movement is making AI accessible to a broader audience, including citizen developers—individuals with limited formal training in AI. By leveraging citizen developers, financial institutions can unlock significant business value, reduce costs, and accelerate innovation. In this article, we will explore the market size, cost savings, challenges, top use cases, leading platforms, and the future of AI democratization in financial services.

Market Size and Growth

The global AI market is experiencing rapid growth, with the financial services sector being a major contributor. According to recent estimates, the global AI in financial services market was valued at approximately $9.45 billion in 2022 and is projected to reach $37.37 billion by 2028, growing at a compound annual growth rate (CAGR) of 25.3%. This growth is fueled by the increasing adoption of AI technologies to enhance customer experiences, improve operational efficiency, and drive innovation.

Cost Savings with Citizen Developers

One of the key advantages of democratizing AI is the potential cost savings for financial institutions. By empowering citizen developers, organizations can reduce their reliance on highly specialized programmers and data scientists, leading to significant cost reductions. Here are some ways citizen developers can drive cost savings:

1. Lower Development Costs: Citizen developers can use low-code and no-code platforms to build AI-driven applications, reducing the need for expensive software development resources.
2. Faster Time-to-Market: With citizen developers, financial institutions can rapidly prototype and deploy AI solutions, shortening development cycles and reducing time-to-market.
3. Reduced Training Costs: Training citizen developers on user-friendly AI platforms is often more cost-effective than training specialized data scientists and programmers.

Challenges of Leveraging Citizen Developers

While the benefits are substantial, there are challenges associated with leveraging citizen developers in financial services:

1. Skill Gaps: Citizen developers may lack the deep technical expertise required for complex AI projects, leading to potential skill gaps.
2. Quality and Consistency: Ensuring the quality and consistency of AI solutions developed by citizen developers can be challenging, especially in a highly regulated industry like financial services.
3. Security and Compliance: Financial institutions must ensure that AI solutions developed by citizen developers comply with stringent security and regulatory requirements.

Accelerating Adoption of Citizen Developers

To overcome these challenges and accelerate the adoption of citizen developers, financial institutions can implement the following strategies:

1. Comprehensive Training Programs: Offer extensive training and certification programs to equip citizen developers with the necessary skills and knowledge.
2. Mentorship and Support: Establish mentorship programs where experienced AI professionals guide citizen developers through the development process.
3. Robust Governance Frameworks: Implement governance frameworks to ensure the quality, security, and compliance of AI solutions developed by citizen developers.
4. Encourage Collaboration: Foster a culture of collaboration between IT and business units to align AI initiatives with business objectives.

Top AI Use Cases in Financial Services for Citizen Developers

With more business requirements knowledge, citizen developers can more affectivley drive innovation in financial services through various AI use cases:

1. Fraud Detection: Develop AI models to detect fraudulent transactions and activities in real-time.
2. Customer Service: Create AI-powered chatbots and virtual assistants to enhance customer service and support.
3. Risk Management: Build predictive models to assess and manage financial risks.
4. Personalized Marketing: Use AI to analyze customer data and deliver personalized marketing campaigns.
5. Loan Approval: Automate the loan approval process using AI-driven credit scoring models.

Leading Citizen Developer AI Platforms

There are several excellent platforms designed to empower citizen developers to create AI-driven applications without requiring deep technical expertise. Here are some of the top platforms:

1. Microsoft Power Platform

• Power Apps: Allows users to build custom apps with minimal coding.
• Power Automate: Enables automation of workflows and processes
• Power BI: Provides powerful data visualization and analytics capabilities.

AI Builder: Integrates AI functionalities into apps and workflows.

2. Google Cloud AutoML

• AutoML Vision: For image recognition.
• AutoML Natural Language: For text analysis.
• AutoML Tables: For structured data analysis.
• AutoML Translation: For language translation.

3. IBM Watson Studio

• Watson Assistant: For building conversational AI and chatbots.
• Watson Machine Learning: For deploying machine learning models.
• Watson Natural Language Understanding: For text analysis and sentiment analysis.

4. Salesforce Einstein

• Einstein Prediction Builder: Allows users to create custom AI models for predictions.
• Einstein Bots: For building chatbots.
• Einstein Vision and Language: For image recognition and natural language processing.

5. Amazon SageMaker

• SageMaker Autopilot: Automates the process of building, training, and deploying machine learning models.
• SageMaker Studio: Provides a comprehensive environment for machine learning development.
• SageMaker Ground Truth: For data labeling and preparation.

6. AppSheet by Google

• No-Code Development: Allows users to create apps without writing code.
• AI and Machine Learning Integrations: Provides built-in AI capabilities for data analysis and automation.

7. UBIX

• ChatUBIX: Simplify your relationship with Data through ChatUBIX conversational AI. Enjoy user-friendly interactions, advanced search/query, and end-end AI from source connection through productionizing solutions.
• DataSpace: Simplify streamline data storage, engines, and libraries with Kubernetes deployment. Enjoy user-friendly setup, advanced monitoring, and seamless instance management for data-driven success.
• ModelSpace: Easily create advanced solutions for every type of Data Science or Analytics pursuit you can imagine.
• SolutionSpace: Seamlessly configure a wide variety of analytics workflows to solve problems across your organization. 100% no-code an accessible to all.
• ProductionSpace: Simplify deployment and management of production models across your organization.
• CloudSpace: Simplify and streamline data storage, engines, and libraries with Kubernetes deployment. Enjoy user-friendly setup, advanced monitoring, and seamless instance management for data-driven success.

8. DataRobot

• Automated Machine Learning: Simplifies the process of building and deploying machine learning models.
• AI Cloud Platform: Offers a range of tools for data preparation, model building, and deployment.

9. H2O.ai

• Driverless AI: Automates the machine learning workflow, from data preparation to model deployment.
• H2O-3: An open-source platform for building machine learning models.

These platforms provide a range of tools and functionalities that make it easier for citizen developers to harness the power of AI. They offer intuitive interfaces, pre-built models, and automation features that lower the barrier to entry, enabling more people to contribute to AI-driven innovation.

The Future of AI Democratization and Citizen Developers

The future of AI democratization in financial services looks promising. As AI tools and platforms continue to evolve, the barriers to entry will lower, making it easier for citizen developers to contribute to AI initiatives. This democratization will lead to:

1. Increased AI Adoption: More financial institutions will adopt AI technologies, driven by the accessibility and ease of use provided by citizen developer platforms.
2. Cost Reduction: The reliance on expensive AI specialists will decrease, leading to significant cost savings.
3. Innovation Acceleration: Citizen developers will drive faster innovation cycles, enabling financial institutions to stay competitive in a rapidly changing market.

In conclusion, the democratization of AI with citizen developers is transforming the financial services industry. By leveraging the skills and creativity of citizen developers, financial institutions can unlock new business value, reduce costs, and accelerate innovation. As we move forward, the continued evolution of AI tools and platforms will further empower citizen developers, driving widespread adoption and shaping the future of financial services.