Information architecture of digital online banking: Putting things in order in the chaos of useful services

The entire year of 2018 saw online banks for small businesses actively implementing various non-banking services designed to address diverse business needs throughout their entire lifecycle. However, the explosive growth in the functionality of online banks took a toll on their information architecture: the addition of new services significantly burdened the online banking systems, both structurally and navigationally, creating numerous challenges for users.

In 2019, banks face a significant challenge: optimizing and organizing the infrastructure they’ve created to transform a collection of disparate services into cohesive business platforms. What should they focus on, and what principles should they follow?

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Structural Revolution

Finances, which were once the core element of business-oriented online banking, are now becoming just one of many components. This transition, which began recently, will continue to evolve. At Markswebb, we identify six key components that play a central role in the worldview of an entrepreneur or a small business manager and must be integrated into business-oriented online banking platforms: counterparties, employees, finances, document management, reporting, and products (goods and services).

In the context of business online banking, we focus on two aspects of its information architecture, which now integrates numerous non-banking services:

1. Structure – the logical hierarchy of elements represented within the system.
2. Navigation – interface elements that help users locate the necessary information and functions.

The proliferation of non-banking services in online banking systems has exponentially increased the number of user scenarios, which were already diverse. What elements should be closely monitored when building the structure of business-oriented online banking platforms? Here’s a list:

• Logical Connections – For instance, linking an issued invoice with its corresponding payment while offering the creation of an associated act, which will also be connected to the invoice and payment, etc.
• Contextual Functions – Nearly every component (counterparty, product, employee, payment) presented in online banking is accompanied by a set of actions that can be performed on it. These actions should consistently follow their "parent" across all sections of the online bank, creating predictable habits for users.
• Integration of Directories – For example, an employee directory is used in payroll services, access management for online banking, issuing corporate cards, and managing them. However, in many systems, these directories exist independently. They should be unified and shared across all services since employees remain the same.
• Proximity of Related Components – For example, a unified events calendar should reflect all important client deadlines, with configurable access for different accountants if needed. Currently, users often have to navigate several calendars—for tax reporting deadlines, external economic activity documents, and personal tasks and meetings.

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Key Principles of Organization

1. Do not fear duplicating paths to essential functions to cover the maximum range of user scenarios.
2. Use simple and clear terminology in navigation and interfaces. Business-oriented online banking often relies on accounting jargon ("debit," "credit," "conversion"), which may confuse users, particularly newcomers to business. For example, repeating a payment is sometimes labeled as "copying," which is often misinterpreted as exporting the payment document.
3. Ensure consistency in contextual functions, so each component has the same set of actions wherever it appears.
4. Implement a unified transaction feed. Multiple feeds confuse users, especially inexperienced entrepreneurs. A single feed combining incoming and outgoing transactions, including statuses like "Pending Signature," "Processing," or "Rejected," is far more intuitive.
5. Enable effective filtering—avoid automatic activation, preserve active filters from the previous session, and minimize unnecessary actions like pressing an "Apply" button. Active filters should be clearly displayed and remain visible during page scrolling.
6. Provide morphological search across the entire system, not just for payments, with context-specific actions for search results and the ability to prioritize manual results for popular queries. This feature has already been implemented by Tochka Bank and BSS (Digital2Go), and we hope it gains wider adoption.
7. Ensure structural flexibility:
   • Services should adapt to the user’s context. For instance, a sole proprietor under a simplified tax regime should receive reminders and calculated taxes tailored to their specific business type.
   • Adjust navigation to reflect the user’s current "worldview." For example, users frequently handling foreign currency transactions should see relevant functions prioritized, while others remain out of the way.
   • Use shortcuts and pattern detection—identify recurring payments and suggest automation options to streamline the user experience.

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Conclusion

A well-designed information architecture can significantly enhance the user experience. When everything needed is readily accessible and distractions are minimized, efficiency improves, and the entrepreneur's overall satisfaction increases. Achieving this outcome requires meticulous work by designers on the structure and navigation of online banking systems, alongside the use of modern data analysis technologies to tailor interfaces dynamically to the context and needs of each of hundreds of thousands of users.

Smart digital interfaces are the new horizons of UX

 

Online banking platforms are increasingly incorporating built-in accounting tools and other products tailored for entrepreneurs. Mobile banking applications, once perceived as supplementary, have evolved into full-fledged services, enabling business owners to manage key tasks like account operations directly from their devices.

The Next Frontier in User Experience

What’s the next step in enhancing the user experience? How can remote banking interactions become even more seamless? What breakthrough will redefine banking interfaces? My prediction: we are rapidly approaching the era of intelligent interfaces. In the near future, the work of analysts and designers will be complemented by artificial intelligence capable of analyzing and customizing banking interfaces for each individual among hundreds of thousands of clients.

Evolution of Banking Apps

By the end of 2017, the capabilities of mobile banking applications had grown significantly. Previously, they seemed like incomplete extensions of online banking. By then, users could execute payments, download statements, and manage cards. Today, leading mobile business banks allow payroll management, foreign currency control, and other complex tasks. This progress has led to a growing number of mobile-only users among business clients.

Similarly, online banking for businesses has evolved, introducing tools like document workflow management, built-in accounting for small businesses, and ecosystem services tailored to entrepreneurs.

Challenges in Expanding Functionality

While the functionality of banking apps continues to grow, integrating a comprehensive accounting system for general taxation or a robust inventory management system remains challenging. These require solutions tailored to various business needs and operations, which can complicate integration.

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Personalized Interfaces: A Game Changer

Imagine a personal banking consultant who is fully competent in financial products, aware of your unique context, and adept at anticipating your needs. This is the promise of intelligent interfaces—a system that learns from the user’s behavior and adapts accordingly.

Key features of such interfaces:

1. Shortcut Creation: Routines like recurring payments can be completed in just a click. For instance, the system can automatically prepare a payment for June if the last one was for May—no templates or manual input required.
2. Dynamic Navigation: Frequent tasks like payroll management appear prominently, while rarely used features like currency control are tucked away but accessible.
3. Proactive Support: Interfaces will preemptively generate filtered reports or highlight upcoming deadlines based on user behavior.

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Current Examples and Future Directions

The concept of intelligent interfaces isn't limited to banking. Early examples can be seen across industries:

• Text prediction in messaging apps suggests frequently used phrases.
• E-commerce platforms like RichRelevance personalize searches and recommendations using machine learning.
• Chatbots in banking offer guidance on financial products, with potential expansion into areas like credit advisory and currency control.

The next step involves combining text-based and voice interfaces to create assistants capable of tasks like opening accounts, issuing cards, or suggesting better plans—all tailored to the user’s context.

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Overcoming Implementation Challenges

For intelligent interfaces to thrive, banks must unify all client interaction data—online sessions, branch visits, ATM transactions, call center records, and more. However, this poses significant challenges due to:

1. Data Fragmentation: Consolidating information from disparate storage systems.
2. Infrastructure Overhaul: Upgrading legacy systems to accommodate AI-driven solutions.
3. Legal and Security Compliance: Ensuring privacy and security across integrated platforms.

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Technology Underpinning Intelligent Interfaces

Two key technologies driving intelligent banking interfaces are:

1. Recurrent Neural Networks (RNNs): Useful for analyzing sequential data like user behavior logs. Advanced architectures like LSTM (Long Short-Term Memory) can identify long-term dependencies and suggest shortcuts for recurring tasks.
2. Reinforcement Learning: This approach enables systems to learn through interaction and feedback. For example, interfaces can balance between offering frequently used features while keeping less-used options accessible for potential future needs.

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Balancing Exploration and Usability

A key challenge in interface design is the balance between exploration and exploitation—providing reliable solutions while experimenting with potential improvements. For example, while payroll management might dominate a user's activity, interfaces should still offer visibility to lesser-used features like currency control or new services.

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Conclusion: Towards a User-Centric Future

Intelligent interfaces can significantly enhance user experience, offering a level of personalization previously unattainable. For banks, this presents a complex but achievable goal. Overcoming legacy system constraints and fragmented data is critical. Once addressed, the potential of skilled analysts, developers, and data scientists can be unleashed to deliver user-focused, adaptive banking solutions.

By trusting in talent and embracing innovation, banks can transform client interactions, ensuring every user feels as though their banking platform is uniquely tailored to their needs.

My recent publication in Banking Review

Here is my article in the Banking Review magazine. It's about current state and future trends of mobile banking for business.
The main focus is on UX of banking applications. Here we discuss some new features such as document recognition via phone camera, fingerprint authentication, account managing through messenger.
Other topics are new banking services available on mobile platforms, such as payroll, accounting, etc.

Product list optimization project, part 1.3. GA tuning: Pagetype dimension

This post is a little diversion from the main course of this project.

Earlier I’ve been working on another UX research project which goal was to discover behavioral patterns in users’ sessions, in order to improve information architecture of the website. My approach was to apply hierarchical clustering to a number of sessions. But it’s hard to find any patterns when you parametrize session via URLs of each page in this session. Simply, it’s too much of diversity.

So I came up with idea to make a new dimension, Pagetype. Roughly speaking, it’s a place (coordinate) of this page in the website’s information architecture. It looks like this:

“<Senior section of the website> <level X> <particular type of the page>”

For example, “catalog level 4 product list”, “catalog level 5 product page”, “news level 2”, etc.

Performing some magic on distance measure and using Ward's minimum variance method while clustering, it gives very interesting outcomes for the information architecture analysis.

But let’s get back to our project!

I’ve mentioned Pagetype dimension because it helped here in this product list project too. Website I’ve been working on has product lists on different levels of catalog, from 2 to 5. So I used Pagetype custom dimension to filter product list pages from pages of other types.

This website happened to have a little bit weird CMS, and only javascript after page is loaded can decide whether it is a product list or not. So I’ve made a dedicated “Window loaded” tag in Google Tag Manager to track the Pagetype custom dimension.

I recommend to have such custom dimension if you work with behavioral patterns.

Product list optimization project, part 1.2. GA tuning: Pageview ID

One important addition to custom dimensions mentioned earlier, which I come up with, is Pageview ID. In webanalytics universe, there is an entity hierarchy which looks like this:
Client => Session => Pageview =>Hit (Event).
Due to already assigned Client ID and Session ID, we can distinguish one entity of that level from another.

But what about Pageview?
On the one hand, we have page URL. On the other hand, user’s trajectory over the website may be as complex as an analyst couldn't even imagine, including simultaneously opening lots of pages in browser tabs, switching back and forth between different windows (which affects visibility status), multiple hits on Back and Forward buttons, etc.
That’s why we need Pageview ID to which we can assign all these events and gather them later in reports under one Pageview entity.

I track Pageview ID through these steps:

1. Make Custom Dimension “Pageview ID” in Google Analytics and remember its index.
2. Make Custom (User-Defined) Data Layer variable “Pageview ID” and assign it to gtm.start. This is an automatically generated system variable which appears with each Pageview Tag (i.e. when pageview starts) and embeds itself in each Data Layer sending from this page (i.e. escort every event on the page). It’s value is a system time of GTM block start in the number of milliseconds format, like “1470900439977”

3. (See the picture) Under every Pageview and Event Tag, connect the Custom Dimension (recall its index) and the value of Custom Variable “Pageview ID” (like you probably did for Hit timestamp previously).

So that must be it. Now each event falls into the right Pageview set (entity), with other events happened during the same pageview.

Product list optimization project, part 1.1. Google Analytics tuning: Custom dimensions

First of all, there is a common approach to manage Google Analytics implementation - through Google Tag Manager. This method has many advantages, but discussion of those is beyond the scope of this series of posts. From now on I assume this approach by default.

There is a great article of Simo Ahava “Improve Data Collection With Four Custom Dimensions”. It’s about 4 parameters which aren’t among dimensions and metrics of Google Analytics API by default, but are crucial for many web-analytics tasks. I didn’t use User ID in this project, but Client ID, Session ID and Hit timestamp were very helpful.

For those of you who decided to implement these custom dimensions too, I want to warn you about the subtle mistake in the Hit timestamp setting. I’ve written detailed comment about this under Simo’s article.

So in brief, when you configure Custom JavaScript Variable, you can’t treat milliseconds as other parts of the time (Hour, Minute, Seconds), because it’s a three-digit variable. Otherwise, “0.089” becomes “0.89” and exceeds “0.123”, which leads to awkward results in data when some page events precede their predecessors.

To fix this, you should add another function and apply it to milliseconds:

var pad00 = function(num) {

var norm = Math.abs(Math.floor(num));

return (norm < 10 ? '00' : (norm < 100 ? '0' : '')) + norm;

};

Products list optimization project, intro

The next series of blog posts is dedicated to the UX research project I’ve accomplished recently. The described approach could be useful for e-commerce websites and online stores, especially those with large product catalog.

Motivation and Main idea.

Products in the catalog subcategories get unequal shares of user attention. Those in the top of the list are seen by almost all users. Bottom of the list gets multiple times less attention (twice as a minimum). Most users don’t scroll all the list throughout.

But as statistics tells us, often the products from the top of the list don’t attract users’ interest. They don’t click on these products, they don’t put them to the shopping cart. At the same time, bottom list products get much more interest.

So the main idea is to analyze ratio of each product’s visibility (attention) and users interest, and put most popular items to the top of the list in order to sell more.

Summary of this series.

First, we make some tunings in our Google Analytics data collection process. We should track the page scroll to measure each product’s visibility. Also we need to gather information about clicks on the links to the product’s page and ‘Buy’ buttons for each product.

Second, we connect to the Google Analytics API from statistical environment (Rstudio in our case), retrieve necessary information, make some exploratory data analysis and get the final report. This report gives us directions as to what permutations should be done in subcategories of our product catalog.

So next time, we will start off with GA tuning.