Fintech and artificial intelligence (AI) have always stood at the forefront of the technological revolution. The technology offered us an unparalleled level of efficiency, personalization, and security. Now, it’s time to take a step forward. Fintech has become even more user-centered, and with design focusing on behaviors rather than mere features, user experience (UX) and user interface (UI) must reach new heights. This is how AI-powered features and interfaces change banking experiences.

The pillars of fintech

Modern digital banking, including the fintech industry, is based on a few indisputable pillars. Fintech goes along with it. These foundations are:

  • security and compliance
  • transparent access to processed financial data
  • the product’s reliability
  • personalization

These four points impact digital products’ design. Each of them has its own set of challenges.

Security. 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.

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.

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.

Fintech powered by AI does a lot more than that. It simplifies complex financial information, gets the advantage of automated processes to actively aid users, anticipates intentions, visualizes data, offers educational tools for saving and investing, and a lot more.

The role of AI-powered fintech UX in simplifying data and processes, while offering user-centered features

The introduction of AI triggered a serious paradigm shift. Many processes were simplified, and the creation of applications was suddenly based on a paradigm shift. AI redefined how financial services operate and interact with their customers. Here are some examples.

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.

Streamlined 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.

Intuitive design. 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.

Accessibility. Ensuring the app is accessible to all users, including those with disabilities, is not only a regulatory requirement but also a key factor in widening user adoption.

Customer-centered educational tools. 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, push notifications, alerts, etc. 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.

Voice recognition features. 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.

Unlike traditional AI, which primarily analyzes and interprets data, generative AI goes a step further – it creates and generates new content. In the context of banking UX, this means the ability to craft personalized interfaces, dynamic content, and intuitive user interactions based on individual user data.

The relevance of generative AI in banking UX lies in its capacity to transform static and generic interfaces into dynamic, personalized user journeys. By leveraging data such as user behavior patterns, transaction history, and personal preferences, Generative AI Services can tailor the banking experience to meet the unique needs of each user.

All this requires software engineers specialized in utilizing AI. Centaur developers, that combine knowledge of traditional development with the skill of using artificial intelligence for speed, efficiency, and control.

AI in fintech – some statistics

These few words will give you an inside into the current state of the industry and the level of AI adoption. It’s a relationship that elevates projects and serves the customer.

According to Fortune Business Insights, and Statista, the global fintech market is currently worth $340.1 billion. At the same time, the market value of AI in fintech is estimated at $44.08 billion. With a compound annual growth rate (CAGR) of 2.9%, the share of artificial intelligence in financial technology is expected to reach $50 billion over the next five years.

According to Mordor Intelligence, the fintech AI market size is expected to take flight by 2028. The AI In Fintech Market size is estimated at USD 14.79 billion in 2024, and is expected to reach USD 43.04 billion by 2029, growing at a CAGR of 23.82% during the forecast period (2024-2029).

According to Juniper Research, AI implementation in identity verification is set to save banks $900 million in operational costs and cut 29 million hours from digital onboarding processes. The average time spent per digital onboarding check is expected to decrease by 30%, from over 11 minutes in 2023 to under 8 minutes in 2028, highlighting AI’s potential to streamline financial operations and enhance efficiency.

According to Accenture, 67% of organizations plan to increase spending on technology, especially in data and AI fields. This widespread commitment to technological investment signals a future where data-driven insights and AI-powered solutions will be fundamental to business success.

According to McKinsey, approximately 72% of companies utilize AI in at least one business function. Such widespread adoption is not surprising – businesses that harness AI see a lot of improvement in data processing, security management, cost reduction, and customer service.

According to IBM’s report, AI-powered chatbots and virtual assistants can handle many customer interactions in real time. These solutions have demonstrated impressive results, helping reduce the cost of dealing with user inquiries by up to 80%. Furthermore, recent advancements have led to a 25% improvement in conversational AI accuracy, enabling these systems to better understand customer sentiments, recognize their intent, and respond to their needs.

10 examples of AI-powered features for next-level fintech UX

According to a study by Forrester Consulting for Blend, consumers expect efficient and personalized interactions with their banking institutions. Here’s how that can be achieved.

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.

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 to achieve them. 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 bottlenecks in achieving saving goals. The AI identifies areas where a user is likely to overspend, providing solutions and alternatives to help moderate expenses and stay within budget.

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.

Predictive payment method offering. 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 purchase in that category.

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.

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

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.

Smart investment recommendations. 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.

Summary

It’s already happening, the future is here. Fintech personalization is the key. That’s why Code & Pepper bets on AI. We have a talent identification methodology that allows us to hire only the top 1.6% of the market pool. 

Fintech is still about breakthroughs, but a lot has been done. Now, it’s less about technology and ease of use, and more about user and tailored solutions. It’s about flavor. The missing ingredient that competition doesn’t have.

We know all about it. We named the company after that idea. Create your next project with us, spice up your value proposition.