AI use cases and challenges

You can read my idea here: 

(P1) Experience Management (XM) in the banking industry - Future of Applying Machine Learning and Deep Learning | XM Community 

(P2) Experience Management (XM) in the banking industry - Future of Applying Machine Learning and Deep Learning

I really love this topic. In some use cases like banking, I believe AI can help improve all aspects of a bank specifically, and a business in general. For example 

• AI enhances customer experience (CX): Artificial intelligence helps Personalize experiences at scale – something that manual methods cannot do. ML algorithms can analyze millions of transaction and interaction data to understand each customer's needs, from there Make suggestions for suitable products and services automatically. For example, AI chatbot and intelligent virtual assistants can provide Personalized financial advice, instant answers to customer questions anytime, anywhere . This enhances satisfaction and loyalty when customers feel understood and served promptly. AI also delivers 24/7 support service – Chatbot works continuously to help answer requests after hours, Ensuring customers are always served promptly without having to wait. In addition, ML is also used to predict customer behavior: for example, predicting the possibility of churn so that banks can proactively retain them with appropriate incentives, or forecasting credit demand to approach offers at the right time. Analyzing customer sentiment via voice calls or text responses using AI-powered NLP (natural language processing) also helps banks capture real-time satisfaction levels. Overall, AI/ML transforms CX management from reactive to reactive proactive, "understand before customers speak".
• AI helps improve user experience (UX): In UX design and optimization, AI is playing an increasingly larger role. First, AI helps Analyze user behavior on digital channels: ML tools can automatically collect click data, actions, heat maps... for detection Where users often encounter difficulties on the application/website. From there, the UX team knows where to improve (for example, a certain registration step that many users abandon -> needs to be simplified). Second, AI enables Flexible testing and interface customization: multivariate testing or AI algorithms can suggest themselves A/B testing plan and even Adjust the interface in real time based on user response. This creates UX Optimized for each customer segment. Additionally, AI delivers New UX features: for example Smart search (natural typing to find functions, powered by NLP), Automatic input suggestions, or application Vision AI to allow customers to open accounts with ID scanning and facial recognition instead of manual entry. It all helps Smoother, more seamless user experience. Another application is AI chatbot integrated right into the interface (like a guidance assistant) helps new users get acquainted with the application more easily, or quickly resolve questions during use - thereby reducing friction and increasing satisfaction when using digital products.
• AI drives employee experience (EX): For internal banks, AI and ML are also being leveraged to enhance the working environment and employee performance. One example is using an Internal virtual assistant: AI chatbot for employees, helping to quickly answer policies, procedures or professional instructions when needed, reducing time searching for information. AI also supports Personalized employee training and development – through analyzing performance data and career paths, the AI ​​system recommends suitable courses for each employee, or even plans promotions based on skills and interests. In everyday work, ML is possible Automate boring tasks (such as data entry, document reconciliation) thanks to RPA combined with AI, helping bank staff Reduce repetitive work load to focus on value creation tasks (customer care, financial consulting). More importantly, AI delivers Employee sentiment analysis tool through anonymous surveys or internal email tone analysis, helps management Catch early signs of dissatisfaction or reduce engagement to take improvement measures (for example, change working regimes, organize team-building...). Thanks to that, banks can proactively improve EX instead of just reacting after the employee quits. Even some pioneering banks have Draw the employee experience journey and use tracking technology at every touchpoint (onboarding, training, performance reviews, promotions, etc.), similar to how you do with the customer journey. In short, AI/ML is helping Personalize and optimize the experience for each employee, creating a more engaged, more efficient workforce – a prerequisite for delivering good CX.
• AI strengthens brand experience (BX): Brand management also benefits from AI, especially in the digital landscape where brand feedback data is abundant. First of all, AI enables Real-time brand reputation monitoring: social media listening tools using NLP and computer vision can scan through millions of posts and comments to Detect mentions about banking, analyzing tone of voice, positive or negative. Thanks to that, the brand team promptly grasps the media crisis or understands how customers are evaluating the bank's image. Second, AI helps Personalize marketing messages – an important part of BX. Instead of a generic message, AI can segment customers in detail and make suggestions Content suitable for each group (for example, the same loan ad but young customers will see different content than middle-aged customers). Approach Right person, right message, right time This makes the brand experience closer and more meaningful to customers. Third, AI supports assurance Brand consistency across multiple channels: smart digital content management systems can automatically check whether documents and advertisements comply with brand standards (colors, logos, tone of voice), or even suggest edits to synchronize images. Finally, AI also provides Predictive analysis of brand value – for example, using a model to predict that if the NPS score increases by X, the brand awareness index can increase by Y, helping leaders guide brand strategies based on data. All of the above applications aim at one goal: Build a trustworthy, customer-understanding and consistent banking brand, thereby improving BX. In fact, the conversational AI model (chatbot) not only for CX but also plays a role in BX - when the chatbot represents the brand chatting with customers, if well trained, it will create a positive brand impression (modern, useful). Recent research also suggests Conversational AI models have the potential to positively impact customer and brand experiences, but further research is needed to clearly understand the influencing factor

​@EdenHaha  Your points on AI's role in banking are insightful, especially regarding how it personalizes customer experiences and enhances employee engagement. With all these advancements in AI, what do you think are the biggest challenges banks face in implementing these technologies effectively?