Cordian — AI for Banking Data Analytics and Visualization

Data Analysis

major tech partners

years of cooperation

digital ecosystems membership

Analyze Banking Data Through AI Technology

Cordian is a dependable and intelligent AI assistant for banking analytics that specializes in data visualization. It analyzes inquiries using natural language processing and generates responses through deep learning.

Technologies:	React.js Redux-Saga Redux GraphQL PostgreSQL Node.js Express.js Show 3 more
Team:	1 Project Manager, 2 Developers
Timeline:	2.5 years
Location:	Germany

Challenge and Solution

Challenge:

The customer asked us to create a system capable of answering numerous customer requests automatically and with high quality.

Solution:

Our team implemented advanced self-learning AI algorithms to maximize the quality of output.

Result:

AI data analytics platform with strong data visualization tools.

major tech partners

years of cooperation

digital ecosystems membership

Solution 1: AI Integration

To address customer questions regarding banking facilities effectively, we integrated artificial intelligence based on natural language processing and deep learning for selecting appropriate answers. This AI can create a database of responses, solve issues, and handle simple inquiries, enhancing customer service and ongoing support. Our team also implemented a self-learning platform that accommodates text input and voice requests, significantly improving the quality of output.

Solution 2: Process Automation

We implemented Continuous Integration and Continuous Delivery (CI/CD) practices using Docker and Jenkins to automate integration, delivery, and testing processes. The tools we chose for this goal included Protractor, Jasmine, and Jest for server-side testing. Our team also implemented a system based on using i18next framework that automated much of the localization process, allowing for scalability regardless of the content’s scope.

Solution 3: Cross-Platform Integration and Optimization

We identified Cordian’s target audience and the most popular browsers, setting up virtual machines to conduct cross-browser and cross-platform testing. Moreover, our team implemented several solutions to address any scalability issues that might have stemmed from support for multiple devices. They include reductions in assembly size, data caching on both the server and client sides, and optimization of queries and server logic.

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What Does Cooperation With Us Look Like?

Step 1. Vision analysis

Learning about the client’s vision of integrating AI in banking, analyzing the feasibility of integration efforts, and outlining the main challenges we have to address during implementation.

Step 2. Requirement gathering

Analyzing the available requirements for AI integration and Continuous Integration features, choosing the appropriate tech stack for their implementation (for instance, Jasmine), and presenting a set of milestones to complete with clear deadlines.

Step 3. AI integration

Integrating artificial intelligence into the app, providing convenient tools for future localizations, delivering visualization frameworks, and showcasing those new features during meetings to maintain a common vision.

Step 4. Feedback and testing

Gathering feedback from finance and banking stakeholders to analyze AI efficiency, finding potential issues in code and UI/UX through manual and automated testing, and creating advanced integration systems for manual and automated testing.

Step 5. Post-launch support

Launching the product together with its new features, providing dedicated support to ensure its smooth operation, upgrading AI according to customer feedback, and further upgrading the testing frameworks to maximize performance.