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Revolutionizing CX in Banking in 2024: How Banking Apps are Leveraging AI for Enhanced Customer Engagement

In today’s digital age, banking apps have become a critical tool for customers to manage their finances. With the rise of fintech and open banking,

With Gen AI coming into the picture, banks are leveraging AI to not only streamline their back-end processes but also provide hyper-personalized experiences and enhance customer engagement. According to McKinsey Global Institute, gen AI could add $2.6 trillion to $4.4 trillion annually in value with banking predicted to have one of the largest opportunities.

In this article, we’ll explore how banking apps are leveraging AI to transform the banking industry and revolutionize CX in 2024.

The Rise of Banking Apps

Fintech app

According to a study by the Federal Reserve, 53% of smartphone users have used mobile banking in the past 12 months, and this number is expected to continue to rise. As more customers turn to banking apps for their financial needs, banks are under pressure to provide a seamless and personalized CX to stay competitive.

How AI is Revolutionizing CX in Banking Apps

Personalized Recommendations and Insights

AI in banking

One of the key ways that AI is transforming CX in banking apps is through personalized recommendations and insights. By analyzing a customer’s financial data, AI algorithms can provide personalized recommendations for financial products and services that best suit their needs. This not only helps customers make more informed decisions but also increases the likelihood of cross-selling and upselling for banks.

AI can also provide valuable insights into a customer’s spending habits, allowing banks to offer personalized budgeting and financial planning tools. This not only improves the CX but also helps customers better manage their finances.

With Gen AI’s capability to summarize and contextualize documents from ample unstructured data, those working within customer contact functions can get a more comprehensive view saving their time and effort and thus improving their efficiency. 

Chatbots for 24/7 Customer Support

Another way that AI is enhancing CX in banking apps is through the use of chatbots for customer support. Chatbots are AI-powered virtual assistants that can communicate with customers in natural language, providing quick and efficient support. They can handle a wide range of inquiries, from basic account information to more complex issues, without the need for human intervention.

By using chatbots, banks can provide 24/7 customer support, improving the overall CX for customers. This also reduces the workload for human customer service representatives, allowing them to focus on more complex inquiries.

Fraud Detection and Prevention

Fraud detection

AI is also playing a crucial role in fraud detection and prevention in banking apps. By analyzing a customer’s spending patterns and transaction history, AI algorithms can identify suspicious activity and flag it for further investigation. This not only helps banks prevent fraud but also provides customers with peace of mind knowing that their accounts are being monitored for any unusual activity.

Predictive Analytics for Better Decision-Making

AI-powered predictive analytics is another way that banking apps are leveraging AI to enhance CX. By analyzing a customer’s financial data, AI algorithms can predict future spending patterns and provide insights for better decision-making. This can help customers plan for major purchases, budget more effectively, and make informed investment decisions.

The Future of AI in Banking Apps

Voice-Activated Banking

As AI technology continues to advance, we can expect to see more voice-activated banking features in the future. Customers will be able to use their voice to check their account balance, make transfers, and even apply for loans. This will provide a more convenient and hands-free way for customers to manage their finances.

Hyper-Personalization

With the help of AI, banking apps will be able to provide hyper-personalized experiences for customers. This means that every aspect of the CX, from product recommendations to customer support, will be tailored to the individual customer’s needs and preferences. This will not only improve the CX but also increase customer loyalty and retention.

Advanced Fraud Detection and Prevention

As AI technology continues to evolve, we can expect to see more advanced fraud detection and prevention measures in banking apps. AI algorithms will be able to analyze a customer’s behavior in real-time and identify potential fraud before it happens. This will provide customers with even more security and peace of mind when using banking apps.

Conclusion

AI is revolutionizing CX in banking apps, providing customers with a more personalized, convenient, and secure banking experience. With increasing competition and changing consumer expectations, banks must embrace AI to stay competitive and meet the evolving needs of their customers. With the advancements in AI technology, we can expect to see even more innovative features and improvements in the CX of banking apps in the future.

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10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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