How Financial Services Can Leverage AI and Machine Learning in Application Development

AI and machine learning technologies are profoundly impacting industries, including the financial services sector. Financial sector executives are applying AI-driven insights to transform how they work, think, and make decisions.

According to Statista, the financial services sector invested $35 billion in AI technology in 2023. The 2024 report on the “State of AI in Financial Services” highlights the use of Generative AI in their:

• Business operations
• Customer service
• Revenue generation
• Sales and marketing

Dan Schulman of PayPal says, “We’re not trying to reinvent the wheel, we’re trying to perfect it.” Application development is a “wheel” that can leverage AI and machine learning. With the growing demand for “intuitive” financial applications, the integration of AI and ML technologies is becoming imperative in the software development cycle.

Let’s look at 7 areas in application development that can leverage AI and machine learning.

1. Code generation and optimization

Error-free and optimized code is simply a “pipedream” for most application developers. With AI technology (read “Generative AI”), this is now a possibility. With AI tools like MS CoPilot and GitHub, developers can automatically generate code snippets in multiple languages.

Besides, AI tools also optimize the code quality by adhering to best practices and coding standards. What’s more, financial companies can leverage AI tools to reduce the time and effort involved in manual coding, thus accelerating the development workflow.

• Predictive analytics

Struggling to forecast project timelines? With machine learning models, project managers can now accurately predict timelines using historical data. This helps them allocate the right resources for on-time project completion.

What’s more, project managers can utilize AI-powered recommendations to improve the usability of their applications. For instance, AI systems can automatically provide UI-related recommendations – based on the user’s preferred navigation path.

• Software Testing

By integrating AI and ML algorithms, application developers can now expedite the software testing process. For example, with its algorithms, developers can detect and fix software bugs at an early stage of application development.

Similarly, by “training” on massive codebases, machine learning systems can detect common coding errors and vulnerabilities. This effectively reduces the debugging time, thus enabling faster software releases.

• DevOps

AI and machine learning are valuable assets for DevOps teams – driving the collaboration between the application development and operations teams. AI-driven DevOps uses AI models to automate the software deployment process.

Through automated deployment, AI ensures that financial services companies deploy new application versions – with minimum human intervention.

AI-powered monitoring tools also provide real-time insights into application performance, thus enabling faster issue resolution.

• User Requirement Analysis

Effective application development is all about converting the customer’s needs into technical use cases. As an AI capability, natural language processing (NLP) is valuable for understanding and analyzing customer requirements – and communicating the insights to developers.

AI-powered tools can also validate the user’s requirements by comparing them with previous development projects. With NLP, application developers can shorten the requirements-gathering phase, thus reducing the overall development time.

• Personalized User Experience

Along with intelligent recommendations, AI makes it possible for UX professionals to create customized user interfaces in financial apps. Based on the user’s activities on the app, developers can facilitate an AI-enabled user experience.

By analyzing user data, AI-powered tools can also inform if the application is meeting the user’s expectations. This data-driven approach is an integral part of modern application development.

• Software Architecture Design

Besides application development, AI and machine learning technologies are also improving software architecture design. For instance, Generative AI can assist software architects by recommending the right technology stack – based on business requirements.

Similarly, large language models (LLMs) like ChatGPT can be trained on the best practices in software architecture. Besides cutting down design time, AI tools can provide architects with a “blueprint” to get started with the application design.

How AI and Machine Learning Can Benefit the Development of Financial Apps

In the financial services domain, the adage “Time is money” holds even in the digital age.

With the AI adoption, application developers can save valuable time & costs in:

• Writing and testing code
• Deploying the application
• Manual or repetitive tasks

Among other benefits, AI-enabled tools can detect financial fraud by tracking “suspicious” transactions. As per industry estimates, AI systems can detect fraud with an accuracy of up to 95% – as compared to 60-70 for traditional systems. For example, AI-powered payment systems ensure faster transactions – as well as secure digital payments.

Besides time and cost benefits, AI-enabled financial systems improve scalability to process massive volumes of financial data. Over time, AI-powered applications can improve through self-learning – by analyzing real-time data. Similarly, business leaders in the financial domain can improve their decision-making powers with relevant data patterns (extracted by AI and machine learning algorithms).

Conclusion

To summarize, we’ll continue to see the adoption of AI technologies for application development in the financial services industry. With the growing competition in this sector, financial services companies are focusing on reducing the time-to-market of their applications – along with high-quality code.

As a solution provider for banks, financial service providers, and payment processors, Verinite offers application development and maintenance services to its global customers.

If you want to leverage our AI and machine learning expertise, get in touch today.