Organizing Data Chaos: How to Implement Reporting into a Fintech Product

What data reporting is

Data reporting is much like the detective work of the digital age put on automation. It's the process of gathering raw data from various sources and then skillfully arranging it into bite-sized, eye-catching nuggets of insight that unveil the secrets of your business performance.

These days, the real magic happens when you wield data visualization tools to create reports that practically leap off the page, like pop-up books for business. But apart from looking swell, data reports perform a crucial role for your company and clients.

By implementing data reporting, you wire your Fintech solution to back its users with the following:

• Fraud detection (monitoring transaction patterns and flagging unusual behavior);
• Compliance (creating accurate reports for regulatory authorities);
• Portfolio and personal finance management (tracking financial health and  the performance of investment portfolios);
• Market insights (offer real-time market data, news, and analysis).  

For your company's benefit, leverage automated data reporting to enhance risk assessment (assessing the creditworthiness of applicants), customer support (analyzing customer inquiries and complaints), marketing and customer acquisition (tailor marketing campaigns), and product development (analyzing user feedback, feature usage, and market trends).

Kay data reporting concepts

Let’s focus on the three key concepts behind data reporting: data analysis, data visualization, and UI/UX.

Often, people get confused about data reporting and data analysis, using these terms interchangeably. But they aren’t synonymous: data reporting is about gathering, organizing, and presenting information, while data analysis involves interpreting data in a specific context. Data reporting is the step you need to complete before starting data analysis. Once your data mosaic is pieced together, it’s ready for data analysis, where you zoom in on your organization's current state to make new plans and recommendations for the future based on what the data reveals — or the system does it for you. 

Next comes data visualization, a crucial component of data reporting. Visual means like charts or tables transform raw data into meaningful, easily understandable insights that can be effectively communicated to stakeholders through reports or dashboards. Here’s the difference between the two:

• Reports are static and detailed documents well-suited for on-demand historical analysis and documentation; for instance, a report on the state of the Wealthtech market in a specific quarter.
• Dashboards are concise, focused, more visual and personalized for real-time monitoring of the relevant data. Say, a financial advisor’s dashboard would group clients’ portfolios and track market activity live. I’ve got an excellent example here.
  
  See the samples of key visualization approaches below (enter recursion).

UI/UX in Fintech data reporting 

User interface (UI) and user experience (UX) design principles guide the creation of impactful data reports and dashboards. Below, I mention fundamental UI/UX design principles contributing to this process.

Clarity and simplicity

Create straightforward, uncluttered layouts that let users easily understand the information presented. To make data reports user-friendly, avoid unnecessary visual distractions, use clean and consistent typography, and ensure that data visualizations are easy to interpret.

User-centered design

Prioritize understanding the needs and goals of users. Your designers must conduct user research to ensure that the data reports and dashboards your solution creates meet user expectations. Tailor the content and layout to the specific needs of your audience, whether they are executives, advisors, or their customers. Also, remember to make digital products accessible to all users. To assist individuals with disabilities, use alt text for images, add keyboard navigation options, and choose inclusive color schemes.

Information hierarchy

Effective UI/UX design organizes information hierarchically, keeping the crucial and most frequently accessed data in the spotlight. So, key metrics and insights should go on top of the dashboard or report. Use visual cues like color, size, and positioning to guide users' attention to critical information.

Consistency

A cohesive and familiar user experience requires consistency in design elements, such as colors, fonts, and icons. Maintain a consistent design language throughout your reports and dashboards. This helps users navigate and interpret the information more easily.

Interactivity

Interactivity enhances user engagement and understanding. UX design principles encourage thoughtful use of interactive elements.

What: Incorporate interactive features like drill-down options, tooltips, and filters that allow users to explore data and gain deeper insights.

Mobile responsiveness

Adapting visualized data to various screen sizes is a must, as most financial app users want to access insights on the go. Ensure your data reports and dashboards are responsive so users can access and interact with them on smartphones without friction.

Testing and iteration

While reports are static, it doesn’t mean one report template is good to go for years. Your team should do continuous testing and refinement based on user feedback. This ensures that the design aligns with user needs and expectations.

Incorporating these UI/UX design principles into your data reporting process can dramatically boost the usability, effectiveness, and overall impact of your reports and dashboards, making them more user-friendly and valuable for decision-makers.

Pros and cons of main data reporting approaches

Let’s look at the main visualization approaches in the illustration above. Below is a checklist for selecting data reporting approaches to help you navigate them and pick the right ones for your Fintech offering.

Tabular reports

A structured, table-like format is simple to create and understand, often resembling spreadsheets. It’s suitable for displaying structured data, such as financial statements or transaction logs, and can be generated quickly and efficiently. 

On the other hand, it’s somewhat limited in terms of visual appeal and interactivity (not really cool). Also, it lacks the potential for conveying complex relationships or trends and may become overwhelming with large datasets.

Charts and graphs

These are visually engaging and effective for visually styling up trends and patterns. Charts and graphs are a decent fit for displaying data distributions, comparisons, and changes over time. Another superpower of theirs is enhancing data interpretation and decision-making.

The tricky part is that both require careful design to avoid misinterpretation or distortion of data. Overly complex charts can confuse users and are not always suitable for displaying detailed or granular information.

Dashboards

Dashboards can rock a comprehensive view of key performance indicators (KPIs) and metrics. These interactive visualizations enable users to monitor data in real or near-real time, drilling down into details.

While looking cool, effective dashboards can be complex and time-consuming to build. Information overload is a risk if too many elements are included. On top of that, users may need training to use dashboards effectively.

Heatmaps

These clearly visualize data density and patterns. They effectively identify trends and outliers in large datasets and enhance data exploration and decision support.

The limitation of heatmaps lies in displaying precise numerical values. Also, they may only be suitable for some data types, which is not really helpful when individual data points are critical. There’s a risk of subjective interpretation when data is presented as a heatmap, too.

Geographic Information Systems (GIS) maps

GIS maps are ideal for spatial data analysis and visualization. They support location-based decision-making and are handy for identifying geographic trends and correlations. 

However, it’s worth knowing that heatmaps require specialized tools and expertise for development. In addition, data accuracy and geocoding issues can affect results. Also, remember that heatmaps may not be relevant for non-geographic data.

Drill-through reports

This kind of report lets users unlock detailed information by clicking on specific data points. By keeping it interactive, drill-through reports balance high-level summaries and complex data and drive up user engagement and data exploration.

The cons include complex design and implementation processes and careful planning of navigation paths to prevent confusion. Also, these reports may frustrate users if they don’t find the information they seek.

Interactive reports

These reports enable users to customize report views based on their preferences through filtering, sorting, and data manipulation. These receive kudos from me for increasing user engagement and data exploration.

Again, prepare for a time-consuming development here, compared with static reports (complexity increases with the level of interactivity). Besides, users may require training to use advanced interactive features.

Natural Language Generation (NLG) reports

NLG reports automatically generate human-readable narratives from data. The major pros of these reports are simplifying complex data for non-technical users and reducing the time required to create reports.

On the other hand, NLG reports have limited capacity to handle highly specialized or technical data. They may lack the context and insights human analysts provide, and the quality of generated narratives depends on the NLG algorithm.

One thing I can add is the choice of data reporting concept should align with the specific needs of your audience and the nature of the data being presented. A well-rounded reporting strategy may involve a combination of these concepts to communicate data insights effectively.

Reporting and visualization tools: How to implement 

You can build all the visualizations in your solution from scratch. But if you don’t have several months and their equivalent in dollars to spare, the best option would be to integrate a reliable solution.

I’m sure you don’t want just any tool, and picking the right one for yourself can be challenging. That’s why I’ve prepared a quick overview of our team’s top three recommendations below.

Fragment of one of Power BI  templates.

Power BI

Microsoft's Power BI empowers fintech entities to analyze data effectively, gaining insights into evolving customer needs. This powerful tool leverages front-running technology for every business activity, from identifying potential customers in specific regions to facilitating cross-selling. Moreover, it's adaptable to detecting anomalies in user behavior, sending alerts for non-compliant transactions, and analyzing security breach patterns.

Pros: 

• Offers access to 180+ data sources (connectors)
• Has monthly updates
• Offers multiple dashboards and AI-driven personalized reports
• Collaboration is seamless

Challenges: 

• While Power BI is user-friendly, it may require a seasoned BI consultant to leverage its capabilities fully. 
• Drill-down functionality from summary-level reporting is limited, and integration with unlisted systems might pose challenges. 
• Complex tasks like combining multiple reports, handling non-financial data, and creating clear reporting hierarchies may be daunting for beginners in Power BI. 
• The platform's dashboard creation is limited to Power BI Service.
• In the Power BI service, you’ll need a mediator between the cloud and your on-premises network. Direct connection to your on-premises database is available on Power BI Desktop.

Implementation tips:

• Tap the potential of Power BI to integrate diverse institutional systems seamlessly.
• Establish clear implementation parameters while maintaining flexibility for adjustments. This adaptable approach empowers your team to respond promptly to evolving requirements, optimizing the tool's effectiveness.
• Prioritize efficiency by streamlining data extraction processes and adeptly spotting emerging trends. Utilize these insights as a driving force to propel your institution toward its predefined goals.
• Forge collaborative partnerships with trusted solution providers or experienced Power BI partners. While the tool is user-friendly, if you are a beginner, you'll likely need their expertise and guidance during the implementation process to maximize the tool's potential.
• Develop a comprehensive implementation roadmap that clearly defines your objectives. 
• Design custom dashboards that cater not only to your institution's specific needs but also to those of your customers. 
• Utilize Power BI's real-time analytics capabilities for ongoing performance monitoring. To gain deeper insights, refine the dashboard as needed.

Screenshot of a report Ernesto Ongaro, a Jaspersoft community member, made using JasperReports. 

JasperReports

JasperReports from Jaspersoft is an open-source Java reporting engine. It operates as a Java class library that efficiently generates and delivers reports to users. This platform employs a straightforward XML file format with the ".jrxml" extension for report templates.

Pros:

• JasperReports offers connectivity to 35 data sources (fewer than the other two solutions).
• The platform lets users manage multiple report designs simultaneously and switch between them effortlessly.
• JasperReports Server has extensive public interfaces, supporting integration with various applications and customizable functionalities.
• Like Power BI and Tableau, Jaspersoft provides a wealth of how-to articles for users getting started with the tool.

Challenges:

• Editing reports demands a steeper learning curve for users.
• Creating cross tabs within the platform can require much effort, too.
• The report preview can sometimes differ from the final report's appearance, so watch out for discrepancies.
• One of the critical challenges with JasperReports lies in the need to overhaul the report's core logic whenever there are changes in the business logic.
• Modifying parts of a package can be tricky, making it challenging to revert to default package settings.

Implementation tips:

• Ensure you download the appropriate version tailored to your intended platform for report creation.
• For compatibility when selecting example reports, align the chosen version with your specific database server.
• While crafting a new report, keep an example report open to serve as a guide for following the steps accurately.
• Note that the configuration for a Data Adapter (establishing a connection to external databases like MySQL or Oracle) differs depending on the database type.
• Before integrating queries into Jaspersoft tools, rigorously test and verify them against your database using a Database Management System.
• Assess and validate the outcomes of report customization. Repeat with any tools you plan to employ, not limited to Jaspersoft products.
  
  

Urban Audit city factsheets made with the help of Tableau by European Environment Agency

Tableau

Tableau gained wide prominence as a Business Intelligence (BI) tool within the Fintech industry. With its top-league data analysis and visualization, this platform allows users to uncover data trends and conduct precise data forecasting through customizable data slicing.

Pros:

• Tableau offers access to a vast array of connectors, more than 200.
• Its user-friendly interface rocks intuitive dashboards equipped with self-explanatory wizards, making data visualization accessible even to non-technical users.
• Collaboration is a breeze with Tableau, facilitating user teamwork and review processes. The cloud-sharing feature ensures dashboard accessibility from anywhere.
• The platform's visualization capabilities encompass various formats, including charts, tables, graphs, and maps.
• Users can tap into many learning resources, easing the learning curve.
• Tableau equips users with a comprehensive suite of tools and functionalities, empowering them to analyze data comprehensively and craft visually appealing reports. It handles granular data sets more effectively than traditional spreadsheet reports and legacy reporting tools.
• Direct database connectivity is a notable advantage.

Challenges:

• Visual configuration capabilities are limited.
• Tableau has perfected many areas but not the design of critical reports to replace highly detailed Excel reports and tables, particularly in managing and expanding retail advisory businesses.
• Overall, Tableau is so user-friendly that it's difficult to identify substantial limitations or challenges beyond the two mentioned.

Implementation tips:

• To maximize Tableau Desktop's potential, connect to well-formatted data for analysis. Make data as granular as possible, with no extraneous information.
• Tableau simplifies the process by running basic calculations for R models, so your team won’t need to perform extensive manual calculations.

Akkio: Next-gen reporting

Akkio is designed with simplicity in mind. Its visual interface empowers users of all backgrounds to harness the capabilities of AI in their business operations. With Akkio, you can swiftly train and deploy AI models, unlocking the potential for data visualization and analysis at your fingertips.

Pros:

• Akkio boosts the AI model preparation process to under 5 minutes.
• It's so user-friendly that you won't need external consultants to explore it fully.
• Akkio operates entirely in the cloud, sparing users from the hassle of software installations.
• No prior AI experience is required to make the most of Akkio's features.
• The platform automates the generation of reports and insights within minutes.
• Akkio is a rapid and efficient avenue for experimenting with machine learning models, giving you a competitive edge.

Challenges:

• Achieving high accuracy in predictive models can be challenging at times.
• A feature suggesting ways to enhance accuracy, such as modifying data structures or selecting predictive variables, would be valuable to the platform.
  
  

Akkio report screenshot.

How AI boosts reporting

The mid-2010s became a turning point for AI development: human performance, as the benchmark, was set to zero. AI systems increasingly started outdoing humans that did the tests for them.

These improvements can be attributed to the following changes:

• Larger and more diverse training datasets;
• More sophisticated AI algorithms and architectures;
• GPUs (Graphics Processing Units) and specialized AI hardware that accelerated AI model training;
• Techniques like transfer learning enable AI models to leverage pre-trained knowledge and adapt it to specific tasks;
• Continuous feedback loops involving human reviewers help fine-tune AI models and improve their accuracy.

Integrating AI into reporting tools is a game-changer, enabling fintech companies to stay ahead in the competitive market. With AI-driven automation, data reporting becomes more efficient and adaptable, ensuring that decisions are based on the most up-to-date and accurate information.

Developing and integrating data reporting solutions for Fintech companies from the ground up requires years of expertise and constant learning. If you’re looking for expertise in leveraging tools like PowerBI, Jasper, and Tableau, coupled with our forward-looking approach to next-gen reporting with Akkio, let’s create stunning and comprehensible data reports tailored to your unique business needs. Let’s chat, see if our visions match, and embark on a journey of data-driven success together.