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Here’s how Neobanks are Changing the CX game in Banking

4 minutes read

“To change behavior, products must ensure the user feels in control. People must want to use the service, not feel they have to.”― Nir Eyal

Our life revolves around Swiggy, Uber, Dunzo, Urban Clap, and hundreds of applications that give us instant service, usage insights (for example, Swiggy shows the amount of money saved per month through the application usage), rewards, and personalized notifications. Customers are now addicted to this kind of habit and want similar experiences everywhere. What if they can have a banking experience in the same way they order food over Zomato or book an Ola cab via a mobile app. Neobanks are the Swiggy, Zomato of the banking world. Neo is a Greek word that means new. Neobanks are the modern version of traditional banks. Let’s look at how neobanks are changing the CX game in banking industry.

NEO Banks and the Gen Z

Gen Z’s (Generation Z) are the newest addition to the banking world. This generation has a deeply embedded expectation that everything they search for or buy online will be tailored and delivered right away. 

Additionally, Gen Z is a value-driven generation that seeks more value for their money. Their expectations are hyper-personalized experience, prompt deliveries, and on-demand services, higher user engagement, and value for money. And neo banks have been the first movers in decoding these expectations. They are positioning their brand as an online platform for millennials and Gen Z, offering financial services at a touch of a button. Their USP is convenient and simple user experience

For example, Jupiter money- a 100% digital banking company designed to target Gen Z and Millennials- helps users open an account within 3 minutes. “Jupiter has 3 main areas of focus at the moment — increasing user engagement on the platform, investment options, and introducing consumer lending services, which will help them monetize the platform”, says the company’s founder and chief executive officer (CEO) Jitendra Gupta. 

Neobanks are making it easy for users to keep a track of their expenses, and save and plan their investments wisely. But what else is different about them? Why are Gen Z and millennials hooked on this modern banking platform? Well, it’s all about the first impression. Neobanks have built the mobile app keeping the new Generation’s daily routines, actions, and habits in mind. They studied user behavior patterns to determine what compels and ticks these newer customer segments. Here’s how neobanks are changing the CX game in banking to win customers: 

  1. Real-time financial insights at the tip of a button: Customers can track their spending, saving status, and every financial activity on the app. 
  2. Interactive & Conversational App Design: Neobank apps do not have any physical branch yet they are appealing because of their amazing UI and application design. The look and feel of the application is more youthful and vibrant with a minimalistic design. Their focus is on user experience design and functionality, both.
Here's How Neobanks are Changing the CX Game in Banking
Here's How Neobanks are Changing the CX Game in Banking

Source: Jupiter

  1. Rewards & Benefits: Neobanks offer attractive offers and rewards to bring back users repeatedly on the app and retain them. For instance, customers get a 1% reward on all UPI and debit card purchases using Jupiter Money. They can also track their reward earnings in real-time. 

Where are the Traditional Banks heading towards?

Conventional banks focus more on the functionality of the application. Earlier, customers had to visit the branch physically to avail of banking services. Now they focus on bringing the banking service to the user’s ecosystem. Data and AI-driven personalization have been helping banking institutions to create seamless customer journeys for the users. They are leveraging technologies like metaverse, Virtual Reality (VR), and Augmented Reality (AR), to create offerings in the virtual world. Their USP (Unique Selling Proposition) is Customer Engagement. Gen Z is spending most of the time in this virtual space. Banks are leaving no stones unturned to mark their presence in the customer’s ecosystem. How? By creating an immersive experience for these users in the virtual space. IndusInd Bank launched a video branch, which allows customers to communicate with their bank executive in real-time. 

JP Morgan opened a lounge- Onyx in Decentraland. Bank of America launched VR training in over 4,300 financial centers. Lynx is working on introducing 1) A cryptocurrency-based game that allows players to create, earn and sell digital items with financial value and 2) An“enhanced remittance experience”:  A digital meeting space that allows those sending money to loved ones to visit and communicate with them in a “streamlined, entertaining, economical, and secure” manner.

The Road Ahead:

The Reserve Bank of India hasn’t allowed banks to become fully digital. This is one of the major challenges for Neobanks. Having a completely digital presence, they do not have a license. But they do have the technical expertise and Gen Z’s attention.

Neobanks with their technological expertise & Conventional banks with years of experience can together bridge the existing customer experience gap in the banking industry. Niyo, Jupiter, Razorpay have partnered with the traditional banks to deliver a seamless digital banking experience for their customers. According to the Redseer Strategy Consulting report, partnership profits both, giving neobanks a strong position and traditional banks access to young, tech-savvy customers. Recently, Visa and AI-driven neo bank OneBanc Technologies teamed up to launch the first magnetic-strip-free debit and credit cards in India. More than 300,000 new accounts with neo-banking partners have been launched by Federal Bank. 

In the end, it’s all about creating the best customer experience. And working in silos might turn out to be a disaster for both parties. Healthy cooperation may definitely help win customers. 

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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