17 AI-powered Features That Will Revolutionize Banking UX

Understanding Banking UX Design Distinctive Nature

At its core, banking user experience (UX) is about how customers interact with and experience a bank's services and products, primarily through digital channels like banking apps and online platforms. It encapsulates every touchpoint a customer has with their bank, from navigating the website to completing a transaction on their banking app to seeking customer support.

The essence of the banking user experience lies in its focus on user-centered design that considers the specific needs, limitations, and behaviors of banking customers. This focus is critical because banking involves complex transactions and sensitive information, requiring an interface that is not only user-friendly but also secure, reliable, and trustworthy.

How Banking UX Design Differs from General UX Design

Complexity of Financial Data

Banking interfaces deal with complex financial data and processes, such as loan applications, investment portfolios, and account management. Unlike other industries, where UX might revolve around simpler interactions, banking UX must simplify and make these intricate processes accessible.

Security and Compliance Considerations

Due to the sensitive nature of financial data, banking UX must incorporate robust security measures without compromising on user-friendliness. This balance is less prevalent in non-financial digital experiences.

Trust and Reliability

In banking, establishing trust through UX is crucial. Users must feel confident in conducting transactions and managing their finances through a banking app or a digital platform, something that might be less critical in other industries.

Personalization

Banking UX requires a higher level of personalization due to the diverse financial needs of users. Personalization in banking UX might include customized financial advice, tailored product offerings, and individualized account management features.

The Role of UX in Simplifying Complex Financial Information and Processes

Intuitive Design for Complex Processes

Banking UX aims to make complex financial processes intuitive and easy to navigate. This includes offering information and services based on the user’s previous actions, anticipating their needs and goals, breaking down lengthy procedures into manageable steps, using clear language, and providing guidance throughout the process.

Visualization of Financial Data

Good banking UX often employs data visualization techniques to help users understand their financial health and activities. This could mean presenting spending reports in charts, illustrating investment growth in graphs, or displaying account balances in an easily digestible format.

Educational Tools for Financial Literacy

Part of simplifying finance through UX includes providing educational resources within the banking app. This could involve tips on managing finances, explanations of banking terms, or interactive tools to help users make informed financial decisions.

Personalized Dashboards and Alerts

If provided with personalized dashboards inside a banking app, users can quickly access the most relevant information about their financial situation. Alerts and notifications can be used to prompt users about important account updates or actions required, simplifying their banking UX.

Responsive and Accessible Design

Ensuring that banking apps are accessible to all users, including those with disabilities, is a critical aspect of banking UX. This includes designing for various screen sizes and providing features like screen readers and voice navigation.

Streamlining Customer Support

Incorporating elements like AI chatbots or easy-to-navigate FAQ sections in the banking app can streamline customer support, making it easier for users to find solutions to their issues.

Feedback and Continuous Improvement

Effective banking UX involves continuous iteration based on user feedback. By regularly updating and refining the interface based on real user experiences, designers can ensure that the banking app remains user-centric and relevant.

Common AI-powered Features in Banking UX Design

The integration of Artificial Intelligence (AI) into the banking sector marked a significant paradigm shift, redefining how financial services operate and interact with their customers. Many features within regular banking apps have been leveraging AI technologies for quite some time, enhancing various aspects of customer service and operational efficiency.

Customer Service Enhancement

AI-powered chatbots and virtual assistants have become commonplace, providing customers with 24/7 support. These tools use NLP to understand and respond to customer

Operational Efficiency

Automation of routine tasks: AI has automated numerous back-office functions in banking, such as data entry and report generation, leading to increased efficiency and reduced human error.

Voice Recognition

Many banks have integrated voice recognition technology into their apps, allowing customers to perform banking tasks using voice commands. This technology uses AI to understand and process spoken language, making banking more accessible, especially for users with visual impairments or mobility issues.

What Else Could AI Bring to Banking UX? 17 Next-Gen Features

While the current applications of AI in banking apps have been transformative, they merely scratch the surface of what's possible. For UX/UI designers, the opportunity to innovate is immense, particularly when speculating about AI capabilities in a banking app setting.

According to a recent study by Forrester Consulting for Blend, consumers demand efficient, personalized interactions with their banks, which sets the stage for our thought experiment. Let's explore the AI-powered features a next-generation banking app can offer today.

1. Automated Expense Management, Guidance and Budgeting

Finance management used to be cumbersome, but not anymore. The AI enhances banking UX by offering various levels of automated support to help users navigate their spending wisely and save effectively, all within a banking app.

How It Works

• Expense Categorization

  As the user engages in financial transactions, the AI algorithm categorizes and tracks each expense, creating an overall financial behavior report for the user.

• Income Allocation

  The app's AI goes a step further by allocating users' income into various spending categories. This not only ensures that essential costs are covered, but also helps prevent overspending by asking for user confirmation for expenses that exceed allocated budgets.

• Financial Behavior Analysis

  Beyond just categorizing, the AI delves deeper into understanding spending habits and patterns, using them as the basis for the practical advice it provides.

• Personalized Goals and Tips

  The AI uses this data to help users set clear and realistic budgeting goals. Unlike a basic budgeting app, it also offers tailored financial tips to encourage responsible spending, making financial management feel less like a chore.

• Identifying Problematic Spending Areas

  The AI identifies areas where a user is likely to overspend, providing solutions and alternatives to help moderate expenses and stay within budget.

• User Customization

  While the AI provides automated guidance, users have the flexibility to modify or override any of the app's suggestions, ensuring that the advice remains relevant to their unique financial situations.

• Continual Learning

  As the user continues to interact with the app, the AI learns and fine-tunes its recommendations, making the guidance more precise over time.

2. Seamless Payment Experience

In addition to providing financial guidance, the AI simplifies the payment process, making it as seamless as possible.

How It Works

• Learning User Preferences

  From the moment you start using the app, AI algorithms work to identify your preferred payment methods, whether it's your debit card for daily expenses or a credit card for larger purchases.

• Predictive Payment Method Optimization

  Utilizing AI algorithms, the app analyzes your spending patterns and contexts to recommend the most beneficial payment method for each transaction. This could factor in aspects like credit card rewards, bank offers, or minimizing transaction fees. For instance, if a particular credit card offers cashback at a specific type of retailer, the AI could suggest using this card when you make a purchase in that category.

• Smart Merchant Recognition and Offers

  The AI recognizes the merchants where you frequently shop and highlights any available discounts, cashback opportunities, or special promotions linked to your payment options. For example, if there's a discount for using a specific credit card at a certain store, the AI would notify you at the point of payment.

• Contextual Queueing

  Leveraging past transaction data and situational context, the AI anticipates your payment needs. It pre-populates payment fields for recurring payments or commonly visited merchants, simplifying the checkout process.

• User Authentication

  For the final layer of security, users are prompted to confirm transactions using established secure methods, such as Face ID or fingerprint recognition.

3. AI-Enhanced Cost-Efficient Shopping

In a bustling consumer marketplace where prices fluctuate frequently, keeping track of the best deals can be overwhelming. With AI algorithms, it is possible not only to monitor real-time prices but also to analyze collective user purchasing data, keeping users informed about cost-effective deals.

How It Works

• AI-Powered Price Monitoring

  The banking app uses AI to continually scan and analyze the prices of items users have shown interest in or have already purchased. It also factors in shipping fees, seasonal discounts, and more to provide a comprehensive price comparison.

• Intelligent Community-Driven Alerts

  Using machine learning algorithms, the app aggregates and analyzes data from various consumers to identify better deals on similar items. Users will get an alert if someone has found the same item cheaper, complete with AI-suggested actions on how to take advantage of this better deal.

• Predictive Deal Forecasting

  The AI can predict upcoming price drops based on historical data and market trends, alerting users to wait a bit longer for a potential better deal, thus saving them money in the long run.

• Personalized Recommendations

  Aside from alerting users about cheaper options, the AI analyzes their shopping history and preferences to suggest alternative products or brands where they are likely to find cost savings, refining its suggestions over time as it learns more about users' habits.

• Secure Data Usage with AI

  Advanced algorithms ensure the secure and anonymized use of collective user data, so individual privacy is maintained while still offering community-driven, money-saving insights.

4. Streamlined Subscriptions and Recurring Payments

Around 70% of the digital services we use are subscription-based, and looking after each separately can be a hassle. Imagine an intelligent feature within a banking app that streamlines the management of various recurring payments, from streaming services to gym memberships.

How It Works

• Payment Identification

  Upon the processing of any transaction, the AI categorizes it and flags it as a recurring payment if it matches predefined criteria, thus separating them from one-time purchases.

• Usage Analysis

  The AI can sync with digital services to gauge the frequency and nature of usage. This information is valuable in determining the real worth of a subscription to the user.

• Cost-saving Alerts

  By evaluating usage and payment data, the AI identifies redundancies or inactive subscriptions. It then notifies the user with suggestions for potential cancellations or downgrades.

• User Feedback

  A built-in mechanism for feedback helps the AI to refine its recommendations, which results in better alignment with user needs and preferences over time.

• Subscription Organizer

  All identified subscriptions are gathered into a dedicated 'Subscriptions' folder within the app, allowing users to view and manage them from a centralized interface.

• Custom Notifications

  The user can set up reminders for upcoming renewals or price changes, which gives them ample time to decide whether they want to renew or cancel the subscription.

5. Health and Lifestyle

Traditionally, the worlds of personal finance and healthcare rarely intersected in a meaningful way. However, with advancements in AI technology, the lines are starting to blur. AI can analyze and leverage health and lifestyle data to influence financial decisions and opportunities, specifically in the realm of insurance.

Health and Lifestyle Data Aggregation

• User Onboarding

  On the first visit to the insurance section, the AI could prompt the user to either sync data from health apps or input data manually. This forms the foundational dataset for AI analysis.

• Data Verification

  The AI would validate the information to ensure accuracy and relevancy. For instance, sleep pattern data should be consistent over a certain time frame to be considered reliable.

Intelligent Recommendations for Insurance Discounts

• Pattern Recognition

  Machine learning algorithms analyze the aggregated data to discern patterns. For example, consistent exercise routines, sleep patterns, and even shopping habits for health products.

• Discount Calculation

  Based on the recognized patterns, the AI calculates possible discounts on life insurance, creating a score that correlates with lower-risk factors.

• Real-time Notifications

  Whenever the AI identifies a discount opportunity, it can alert the user through notifications.

Tailored Insurance Plans Across Categories

• User Profile Analysis

  The AI takes into account multiple factors such as age, occupation, and even past insurance claims to tailor recommendations for other types of insurance like travel or shopping.

• Dynamic Recommendations

  The AI algorithms continually update to offer the most relevant insurance options. For example, if the AI notices frequent international transactions, it might suggest global health insurance plans.

Predictive Algorithms for Future Needs

• Contextual Awareness

  AI can analyze user behavior like travel bookings, significant purchases, or changes in family size to proactively suggest insurance plans.

• Just-In-Time Recommendations

  If the AI recognizes signs that a specific insurance need may arise — like a booked flight or a newly purchased vehicle — it can offer suitable plans beforehand.

User Customization

• Preference Settings

  Users can manually set preferences for what kinds of recommendations they wish to see, thus training the AI to better match their needs.

• Feedback Loop

  The system would include a way for users to provide feedback on recommendations, which the AI would use to improve future suggestions.

6. Family Financial Management

Family finance management requires a solution that goes beyond the basics of joint accounts and children's allowances — it needs AI-powered features aimed at automating and optimizing the financial responsibilities that come with family life.

How It Works

• Predictive Budget Allocation

  Employing predictive analytics, the AI scans past and present data to anticipate recurring family-related expenses, such as bills, groceries, and school fees. It then automatically sets aside the necessary funds to cover these expenditures.

• Intelligent Spending Division

  The AI takes a nuanced approach to dividing discretionary funds among family members. It analyzes past spending behavior and current needs and aims to distribute resources equitably.

• Spending Trend Alerts

  The AI closely observes the spending habits of each family member. For instance, if it detects an uptick in a child's in-app purchases, it will alert the parents and suggest setting a spending limit. Additionally, it can flag potentially inappropriate or age-restricted purchases, offering an extra layer of oversight for concerned parents.

• Budget-Friendly Recommendations

  By analyzing collective spending behavior, the AI could identify areas of overspending, like frequent dining out, and recommend more budget-conscious alternatives within the family’s geographic location.

7. Localized Cost-of-Living Calculators

When it comes to financial planning, individual needs vary greatly — especially considering the dramatic differences in the cost of living across various regions. This inconsistency can be easily mitigated with an AI-powered feature that offers localized budget suggestions tailored to the cost of living in a specific region or city.

How It Works

• Automated Location Identification

  Upon user consent, the banking app pinpoints the current geographical location. Alternatively, users could manually input locations they're interested in.

• Real-Time Data Sourcing

  The AI then correlates this location data with up-to-date information on local cost of living and inflation rates. This information could be gathered from a variety of credible sources, such as government databases or financial institutions.

• Interactive Design Elements

  To visualize these insights, the UI could feature interactive heat maps or graphs, providing a comparative view of how quickly financial goals could be met in different locations.

• Dynamic Budget Adjustments

  Armed with this localized data, the AI adjusts its budget recommendations. For instance, residents in high-cost areas might see a larger allocation for essentials like rent and groceries, while those in lower-cost areas might find they have more leeway for savings or discretionary spending.

• Goal Re-calibration

  Financial milestones, such as saving for a home or planning a vacation, are recalibrated based on localized cost expectations. Users would get a more accurate timeline for achieving their financial goals, contextualized by their geographic location.

• Actionable Insights

  To take it a step further, the AI could offer precise, location-based suggestions to optimize spending. For example, if the user resides in a city where certain services or products are more affordable, the app could direct them to these outlets.

• Periodic Re-assessment

  As economic conditions fluctuate, the feature would update its recommendations, ensuring that advice remains relevant.

8. Collaborative Financial Tools

Collaboration between users is easier than ever with an integrated AI. It allows users to split bills, make group payments, and share expenses all within one banking app. Here are some examples of how It can simplify financial interactions within social circles or business networks, almost eliminating the need for manual intervention as a result.

How It Works

• Expense Splitting

  AI analyzes transaction data, receipts, and user input to accurately split bills among the group members. It can identify the items, calculate individual shares, and suggest fair splits based on predefined rules or user preferences.

• Group Payment Recommendations

  AI analyzes users' spending patterns and take into account their preferences set within a group. If the group makes a large purchase, the app can suggest optimized payment plans based on this analysis, such as recommending the best person to make a payment while taking into account things like available funds and the financial situation of each individual within the group.

• Smart Notifications and Reminders

  By constantly analyzing the financial conditions of each group member, AI can send timely notifications and reminders related to shared expenses. It can alert users when it's their turn to make a payment or notify them of pending contributions, reducing the chances of missed payments or confusion.

• Intelligent Coordination

  AI can help the group create optimal financial plans, budget allocations, or payment schedules depending on the group's goals.

• Data Analytics and Insights

  AI algorithms can analyze and pinpoint the spending patterns within a group and generate reports, charts, and graphs to visualize users’ activity. It can also take individual spending habits into account and help the group better organize their finances.

9. Seamless Integration with IoT Devices

As smart devices continue to permeate every aspect of our lives, there is untapped potential for integrating financial management directly into the Internet of Things (IoT). By employing AI, it's possible to bridge this gap, allowing interaction with smart home systems and connected vehicles to provide context-driven financial guidance inside the app in real-time.

How It Works

• Context Detection

  Utilizing AI algorithms, the banking app identifies the user's context based on their interaction with various IoT devices. For example, when the user is near their smart refrigerator, the AI activates features related to grocery shopping and meal planning.

• Emergency Fund Management

  In case of detected emergencies like a home break-in or a car accident through IoT sensors, the AI could advise on how to allocate your emergency fund for immediate expenses, coordinating with your insurance where applicable.

• Intelligent Transportation Advice

  When the AI detects that it's connected to a smart car, it analyzes current fuel levels and local gas prices, along with real-time traffic conditions. Based on this analysis, the AI advises whether to refuel immediately or wait for a more economical option.

10. Debt Management and Loan Options

Debt management is a daunting task that can become the reason for one’s financial downfall. However, with the aid of AI algorithms, banking apps can now integrate features that assess debt structures, forecast the impact on credit scores, and suggest customized loan options tailored to anticipated financial needs.

How It Works

• Strategic Debt Management

  If a user is juggling multiple debts, the app's AI can consolidate this information and suggest a tailored debt management strategy, such as the snowball or avalanche method, suited to the user's unique financial situation.

• Credit Score Projections

  For users curious about how financial decisions could affect their credit scores, the AI can simulate the impact of various actions, like paying off debt or opening a new credit line, to provide a clearer roadmap for financial planning.

• Customized Loan Suggestions

  If the AI detects a significant upcoming expense, it presents loan options specifically tailored to meet that need. These options are laid out in an easy-to-understand format, allowing users to quickly compare interest rates, repayment terms, and other criteria.

• Expense Anticipation

  The app's machine learning algorithms can identify patterns suggesting a significant upcoming expense. For example, frequent visits to home improvement stores may signal a future home renovation project.

AI-driven Opportunities in Investment

11. Investment Decision Support through AI

AI algorithms already have the capabilities to analyze user data and predict future financial patterns and behaviors, making it only a matter of time before they can make close-to-perfect predictions.

How It Works

• Pattern Recognition in Financial Habits

  The AI within the app constantly scrutinizes user behavior and financial transactions. Over time, it discerns patterns and identifies idle funds that could be more productively invested.

• Intelligent Investment Suggestions

  As patterns emerge, the app's AI offers investment opportunities that align with the user's financial habits. It goes a step further by showing potential returns that could have been earned if these idle funds were invested earlier.

• User-Friendly Investment Education

  To encourage users to engage in investing, the app presents all necessary information about the investment opportunity in a straightforward and accessible format.

12. Virtual Financial Simulation

As the world of investing grows more complex, the barriers to entry can seem increasingly daunting, especially for those new to trading. By leveraging AI, we can bridge this gap and create a virtual financial sandbox — an environment that allows users to explore various investment scenarios and build confidence.

How It Works

• Simulated Market Conditions

  Utilizing AI, we can provide a risk-free simulated environment, where users can test different trading approaches and market scenarios, offering a hands-on learning experience.

• Personalized Investment Simulation

  Based on an analysis of the user's account balance and spending, the AI can suggest an affordable amount to invest. Users can then simulate what investing this amount would look like and observe the results over time, enjoying an additional layer of personalized financial education.

• Adjustable Market Variables

  Within this sandbox, users can tweak different market variables, simulating real-world conditions like historical market crashes or year-end tax implications.

• Gradual Engagement

  The virtual financial sandbox serves as an inviting gateway to real-world trading, providing a stepping stone for users to become comfortable with the platform before committing to actual financial transactions.

• Cloud-Enabled Scalability

  To avoid computational constraints, the app offloads these data-heavy simulations to cloud servers to ensure that the feature remains both innovative and practical for widespread use.

13. Fully Personalized Financial Strategies

A lot of users struggle to create a strategy that truly aligns with their personal needs and goals. The AI simplifies this by offering personalized investment strategies tailored to each user's unique profile.

How It Works

• Multiangular User Profiling

  The AI collects data on various aspects of a user's financial life, including their risk tolerance, investment goals, and even demographic information. For instance, a 30-year-old user with a moderate risk tolerance and a goal to buy a home within the next five years would have these details collected by the AI.

• Custom Investment Strategies

  Using this rich profile, the AI suggests personalized investment options tailored to fit the user's specific financial situation and preferences. In the case of the 30-year-old aiming for home ownership, the AI might recommend a diversified portfolio that includes a mix of stable bonds and some growth-oriented equities.

• Integration with Financial Goals

  These personalized investment strategies are integrated into the user’s overall financial planning, ensuring a coordinated approach to achieving their financial objectives. For instance, the AI could automatically allocate a percentage of the user’s monthly savings to the suggested investments, helping them stay on track to meet their home-buying goal.

• Detailed Financial Guidance

  Alongside its suggestions, the AI offers insightful advice on why certain investment options might be better suited for the individual, providing context and clarity. For example, it would explain why the suggested mix of bonds and equities is beneficial for the user's risk tolerance and time horizon.

14. Advanced Risk Assessment

Correctly gauging the risk associated with various investment opportunities is a persistent challenge for investors. Banking apps can simplify this by incorporating an advanced risk assessment feature that uses AI to meticulously analyze and rate the risk level of various financial products.

How It Works

• Comprehensive Risk Ratings

  The AI evaluates a range of financial products, from investment funds to cryptocurrencies, assigning each a risk rating. For example, a government bond might receive a 'low-risk' rating, while a particular cryptocurrency could be labeled 'high-risk.'

• Transparent Decision-Making

  The assigned risk ratings provide users with exhaustive information about investment opportunities that aligns with their preferences. For instance, someone with a low tolerance for risk can easily identify and opt for 'low-risk' products, while those willing to take more risk can explore 'medium' or 'high-risk' options.

• In-Depth Analysis

  The risk rating isn't based solely on historical performance but incorporates multiple factors, such as the product's reputation, user reviews, and projected future performance. So, if an investment fund has a strong history but poor user reviews, it may still receive a moderate risk rating.

• Future Projection Insights

  Beyond current ratings, the AI also offers insights into how the risk levels might change in the future, aiding users in their long-term financial planning. For example, a 'medium-risk' stock might be flagged for becoming 'high-risk' in the next quarter based on projected market conditions, enabling users to reassess their investment strategy in advance.

15. Real-time Investment Updates

In an industry where conditions can change rapidly, real-time updates are crucial. We can leverage AI technology to constantly monitor users’ investments and deliver personalized recommendations that adapt to market fluctuations in real-time, all within one banking app.

How It Works

• Constant Market Surveillance

  The AI continuously analyzes stock market trends and performance metrics to keep an eye on the user's investments. For example, if a user's stock shows a sudden spike in volatility, the AI could send an immediate alert.

• Dynamic Strategy Adaptation

  Depending on market conditions, the AI can suggest new investment strategies or adjustments to existing ones. If a significant news event causes a sector-wide dip, the AI might recommend diversifying the portfolio.

• Urgent Action Notifications

  In extreme cases, such as a sharp market downturn, the AI may advise users to make drastic changes, like cutting certain investments. Users would receive these recommendations via their preferred method — be it push notifications or SMS.

16. Fraud Detection

With online fraud rates for financial services experiencing a staggering 149% increase between Q4 of 2020 and Q1 of 2021 alone, according to a report by TransUnion, the urgency for enhanced security measures has never been greater. But now AI technology is capable of providing vigilant, around-the-clock fraud detection capabilities.

How It Works

• Anomaly Detection

  The AI is trained to continuously monitor a user's financial activities, allowing for the quick identification of suspicious transactions. For example, if you usually make small, local purchases but suddenly engage in a large international transaction, the AI would flag it for review.

• Real-time Notifications

  If an irregular transaction is detected, the banking app sends an immediate alert to the user via their preferred notification method, either through a push notification or SMS. This means a user could receive a text message alert within seconds of a questionable transaction, enabling instant action.

• AI as Mediator

  In cases of severe fraud, the AI can act as a facilitator between the user and the bank's security team, offering detailed analytics and evidence to speed up resolution. If fraudulent activity is confirmed, the AI can swiftly compile a history of related transactions and behaviors to assist the bank in its investigation.

17. Visualization with Augmented Reality

As augmented reality (AR) becomes increasingly sophisticated, the possibilities for its application in financial services are abundant. Paired with AI, AR could bring a new layer of intuitiveness and engagement to finance management, while AI algorithms could analyze user behavior and preferences to personalize the AR experience even further.

How It Works

• AI-Powered Portfolio Insights

  By analyzing historical user data and current market conditions, AI algorithms can highlight specific parts of your 3D portfolio in AR, suggesting areas that require your attention. For example, if a particular stock is underperforming, the AI could flag it, making its 3D representation glow red in your AR view.

• Contextual Investment Recommendations

  In the AR environment, AI-generated bubbles could pop up next to investment opportunities, providing real-time insights and suggestions based on your investment history and financial goals. Imagine looking at a 3D representation of the S&P 500 and seeing a bubble suggesting you consider diversifying into international stocks based on recent market trends and your current portfolio balance.

• Seamless AR/AI Integration

  By blending AI-driven insights directly into the AR landscape, users can enjoy an interactive experience that's both visually rich and informationally robust. For instance, if you reach out to 'touch' a 3D representation of a high-risk asset, the AI could immediately display its risk metrics and historical performance.

• Tailored User Experience

  AI algorithms adapt the AR visuals and interactivity options based on user behavior. If you often overlook certain types of financial information, the AI might make those elements more prominent or visually engaging in your AR view.

What Do Researchers Tell Us?

According to a study by El Bachir Boukheroua, the investment management industry has experienced profound transformations in recent years due to advancements in Artificial Intelligence and Machine Learning (AI/ML). While similar technology has long been utilized in trading tools, client services, and back-office operations to manage extensive volumes of trading data, the ongoing development of AI/ML and related technologies is destined to revolutionize the industry. This includes the emergence of new market participants through product customization, enhanced client interfaces through chatbots, superior analytics and decision-making methodologies, and cost reduction through automated processes.

The same study provided sample use cases of artificial intelligence in investment management:

• Augmented Market Liquidity: Leveraging AI-powered high-frequency algorithmic trading to amplify market liquidity provision and optimize market price formation

• Democratized Wealth Advisory: Expanding wealth advisory services by offering personalized and targeted investment advice to mass-market customers, including low-income populations, in a cost-effective manner through the application of AI

• Enhanced Efficiency: Harnessing the power of artificial intelligence and machine learning (AI/ML) to assume an increasing share of investment management responsibilities, resulting in improved operational efficiency

• Tailored Investment Portfolios: Creating more customized investment portfolios by leveraging AI/ML to deliver targeted customer experiences based on individual preferences and requirements

• Unveiling New Return Profiles: Exploring novel return profiles through AI/ML techniques instead of relying solely on conventional investment strategies leads to innovative investment opportunities.

Is It Possible for Traditional Banks to Compete with Digital Banks Using AI?

Traditional banks face various challenges, including intense competition, ever-changing customer needs, and tightening regulatory requirements. In attempts to keep the upper hand in competition against neobanks, traditional banks are adopting innovative technologies such as AI, which can help them improve customer service, decision-making, and operational efficiency.

The use of AI in traditional banking is not new. Many banks have already deployed AI-powered solutions to automate tasks, reduce operational costs, and improve customer service. According to a report by Accenture, AI is expected to increase the profitability of traditional banks by 40% by 2025. The report also suggests that AI can help traditional banks reduce their operating costs by 25%.

Embracing AI Technology

A report from the Cambridge Centre for Alternative Finance and the World Economic Forum indicates that AI adoption in banking has become widespread, especially in key domains like risk management and the generation of revenue through innovative products and streamlined processes.

Financial institutions are leveraging their existing AI solutions to tackle increasingly complex challenges as AI gains popularity in the banking sector. A significant majority (80%) of banks, according to a survey by OpenText, are aware of the potential advantages that AI and machine learning may provide, and many are planning to deploy AI-powered solutions. In fact, 75% of respondents from banks with over $100 billion in assets are currently implementing AI strategies, compared to 46% of banks with less than $100 billion in assets.

Overall, the application of AI-powered technologies in traditional banking has already become an integral component of their operational frameworks. These include the integration of chatbots in customer-facing roles and the implementation of anti-payments fraud measures in intermediary functions.

At the 2022 MIT Technology Review EmTech Digital Conference, Sameena Shah from JPMorgan Chase delivers a presentation titled "The Future of Financial Services Empowered by AI". When she was asked about use cases of AI in financial services, she mentioned:

• Creating safe networks, which can include problems that arise with fraud, sanctions, or anti-money laundering,

• In many cases where there is no access to data (no proxy or synthetic data, for example) or if you have data but are not allowed to use it, AI can become a data provider.

• In the market area, AI can improve trading or multi-agent simulations.

For more details, we recommend watching the whole conference. However, we are here to find out how exactly AI can improve the relationship between a customer and the financial services they are using.

Enhancing the Banking Customer Experience in Traditional Banks with AI

Traditional banks have a unique edge thanks to their physical presence and established community relations. Integrating AI into these aspects can significantly uplift the banking customer experience.

Tailoring In-Branch Interactions

AI elevates the banking customer experience during in-branch visits by equipping staff with immediate insights about customer preferences and history. Just like with digital banking UX, this information allows for more personalized financial guidance and relevant product suggestions. Imagine a scenario where, as soon as a customer enters a branch, the staff is already briefed about their recent digital interactions, preferences, and potential needs, providing a highly personalized service.

Streamlining Services with Predictive Analytics

AI's predictive capabilities can optimize resource management in branches by anticipating busy periods and specific customer service needs. This predictive management reduces wait times and enhances service efficiency, contributing to the enhancement of the banking customer experience.

Harmonizing Digital and Physical Channels

AI can help merge digital convenience with the personal touch of physical branches, creating a seamless omnichannel banking customer experience. For instance, issues or queries initiated on digital platforms can be effortlessly transitioned to in-branch discussions, ensuring a fluid and consistent customer journey.

Local Needs Analysis

Utilizing AI to dissect community-specific data helps banks understand and cater to the distinctive financial needs of various locales. This might lead to targeted services, such as small business support, in areas where AI identifies a growing entrepreneurial trend.

Enhanced Personalization for Trust Building

Personalizing community engagement with the help of AI deepens the trust and relationship between banks and their customers. This could involve customizing communications, events, and financial services to reflect local cultures and interests, thus fostering stronger community ties.

Specialized In-Person Consultations

Another significant advantage of traditional banks with physical branches is the opportunity to offer specialized, in-person consultation services. Here, AI plays a critical role by providing deep insights into the complex financial needs of customers. Staff equipped with AI-powered tools can offer advice on intricate matters such as estate planning, wealth management, and retirement planning with a level of depth and personalization that is challenging to replicate online. The human-AI synergy in branches provides customers with a blend of technological efficiency and the irreplaceable value of human interaction, thus enhancing the overall banking customer experience.

Transforming Bank Branches into Smart Environments

The concept of smart branch bank design involves a strategic makeover of the physical banking space, incorporating AI technology to create a futuristic and efficient banking customer experience. It integrates advanced technology to redefine how customers interact with financial services within a branch.

AI-driven Information Kiosks

One key feature of smart bank branch design is the use of AI-driven information kiosks — interactive stations that provide customers with quick, personalized banking information, from account details to product recommendations. By leveraging AI, these kiosks can offer a custom experience, understanding and predicting customer needs based on their banking history and preferences. The convenience and efficiency of these kiosks enhance the overall banking customer experience, making routine inquiries faster and more user-friendly.

AI-Powered Predictive Models in VR

Imagine customers using VR headsets to step into a 3D financial world, far surpassing the limits of traditional 2D graphics. In this interactive environment, AI evaluates their financial data, spending patterns, and investment habits. Then the AI creates a realistic future projection based on the analysis. Customers can witness their retirement savings evolve in a visually rich environment where each element dynamically adjusts to market trends and personal risk profiles. Recreating financial projections in 3D space not only adds interactivity to financial planning but also simplifies complex information, making it more digestible through immersive visualization.

Interactive Scenario Exploration

In these virtual environments, customers can interact with different financial scenarios. AI enhances this experience by generating personalized 'what-if' situations. Customers can explore how different investment strategies might pan out or understand the long-term impact of various saving plans. With AI's predictive analytics at work, VR presentations can help simplify financial concepts and make them more accessible for customers.

Visualization of Financial Advice

To further elevate the banking customer experience, AI can provide tailored financial advice within the VR session. By analyzing vast amounts of financial data and trends, AI can offer customized recommendations that align with the customer's unique financial goals and risk tolerance, thus significantly enhancing the customer’s understanding and comfort with financial planning.

The Future Is Near

At NetPro International LLC, we believe that incorporating AI-powered technologies will become a necessity for almost every digital product in the near future. Every platform or app without these features will be completely outpaced by their AI counterparts.

As we expect AI to become widespread in the near future, we stand ready to deliver exceptional UX/UI design for any upcoming AI solutions. Every interface you see in this article was meticulously designed by our team, who is anxious to delve into the possibilities AI development will present. AI is destined to reinvent the user experience, and we are ready to spearhead the design revolution, ensuring that the design of your AI-powered digital products is on par with their capabilities.