Top 7 Advanced Strategies for Fintech App Testing in 2024

The FinTech industry has had an unparalleled upswing in 2024, with a noteworthy annual growth rate of 20%. This boom is driven by the adoption of blockchain, AI, and IoT, which have revolutionized how consumers interact with mobile payments, peer-to-peer lending, robo-advisors, and cryptocurrency exchanges.

The demand for creative testing techniques is greater than ever as FinTech apps are becoming increasingly complex and essential for financial operations. In an environment that is always changing and combining modern innovations like voice payments, blockchain, and open banking, traditional testing techniques might not be adequate.

What is fintech app testing?

Fintech App testing is a quality assurance process for applications in the financial technology sector. It involves strict verification and validation of the app’s functionality, security, usability, and compliance with financial regulations. The goal is to deliver a reliable and user-friendly app that people of various age groups can use without the fear of security attacks.

Core fintech app testing types

• Security testing: Checks for weaknesses in the app to prevent unwanted access or data breaches, protecting users' sensitive financial information.
• Functional testing: Ensures each feature of the FinTech app functions as intended, such as transferring money or checking account balances.
• Performance testing: Assesses how well the FinTech app performs under different conditions, ensuring it remains responsive and stable even during peak usage times.
• Usability testing: Evaluates the overall user experience of the FinTech app, focusing on aspects like navigation, layout, and intuitiveness to ensure it's easy and not confusing to use.
• ‍Regression testing: Confirms that recent updates or changes to the application haven't introduced new bugs or issues, maintaining its reliability over time.

7 innovative QA strategies for fintech applications

Let’s get down to the main section of this blog, the 7 best advanced and practically used testing strategies that are used for fintech applications for maximum accuracy of testing and getting the most out of the testing efforts.

AI-Driven behavioral testing

AI-driven behavioral testing uses machine learning algorithms to predict user behavior and test FinTech apps accordingly. This method aids in detecting unexpected use patterns and probable failures.

This strategy involves the use of machine learning models, such as neural networks, to process and learn from data. These models are trained on historical user interaction data to identify and learn from trends, which can then be used to predict future user behavior.

How to implement

To implement AI-driven behavioral testing using TensorFlow, follow these steps:

1. Data preparation: Collect and preprocess user interaction data to form a dataset suitable for training a machine learning model.
2. Model selection: Choose an appropriate model architecture, such as LSTM (Long Short-Term Memory) networks, which are effective for time-series data like user interactions.
3. Model training: Use TensorFlow to train the model on your dataset.
4. Integration: Integrate the trained model into your testing framework.
5. Simulation and Analysis: Simulate user behavior based on the model’s predictions and analyze the outcomes to identify potential issues.

Here’s an example of how you might use TensorFlow to create a behavioral model:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

# Assuming 'user_interaction_data' is a pre-processed dataset
# Define the model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(user_interaction_data.shape[1], 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(user_interaction_data, epochs=100, batch_size=32)

Effect of this strategy

Implementing AI-driven behavioral testing can lead to a 40% reduction in undetected issues, enhancing app reliability and user trust.

Disaster recovery testing

Disaster recovery testing is an essential strategy for FinTech applications, ensuring that services can be restored quickly and effectively after a catastrophic event. This type of testing validates the effectiveness of disaster recovery plans, which include procedures for failover processes, backup systems, and data recovery methods.The goal is to minimize downtime and data loss, maintaining the integrity and availability of financial services even in the face of disasters such as cyberattacks, natural disasters, or system failures.‍

How to implement

1. Develop a comprehensive plan: Create a detailed disaster recovery plan that outlines specific actions to be taken in the event of various disaster scenarios.
2. Regular testing: Conduct regular drills and tests to ensure the plan is effective and that staff are trained to execute it properly.
3. Off-site data replication: Implement real-time data replication to a secure off-site location to provide immediate accessibility and minimize downtime.
4. Clear communication: Establish clear communication strategies to inform stakeholders and customers during a crisis, maintaining trust and transparency.

Effect of this strategy

‍Effective disaster recovery testing can significantly reduce the risk of prolonged service interruptions and data loss.It builds resilience into FinTech platforms, ensuring that they can withstand and quickly recover from disruptive events, thereby preserving customer trust and meeting regulatory compliance standards.

Risk-based testing

Prioritizing testing efforts according to the possible risks and effects of app functions is known as risk-based testing. This strategic approach focuses resources on areas that could significantly affect operational integrity and customer satisfaction.By assessing the probability of risk, which includes software complexity, criticality of business, and frequency of use, this method ensures that features and functions more likely to have defects are tested thoroughly.‍

How to implement

1. Identify risks: Understand the product and business requirements to identify areas with the highest risk.
2. Prioritize test cases: Select test cases that cover the most critical functionalities based on their impact and potential risks.
3. Optimize testing: Use the risk assessment to optimize the time and effort spent on testing, focusing on high-priority areas.
4. Monitor and Adjust: Continuously monitor testing outcomes and adjust the focus as new risks emerge or business priorities change.

Effect of this strategy

‍Risk-based testing ensures that testing efforts are aligned with business objectives, improving the quality and reliability of the FinTech application. It helps in the early detection of critical issues, optimizes resource allocation, and supports informed decision-making in the software development lifecycle.

Chaos Engineering

The technique of purposefully introducing errors into a system to assess its resilience and capacity to continue operating in the face of adversity is known as "chaos engineering." This strategy is particularly useful for FinTech apps, which must operate reliably under various scenarios, including high traffic volumes and cyber-attacks.‍

How to implement

1. Fault injection: To mimic real-world events, introduce controlled errors into the application's environment.
2. Monitoring: Closely monitor the system’s response to these faults using observability tools.
3. Resilience assessment: Analyze if the system can bounce back from any unexpected event and carry on without any problems.
4. Improvement plan: Using the evaluation results as a guide, create and carry out an improvement plan that will give a road map for areas of improvement.

Effect of this strategy

‍Chaos engineering helps ensure that FinTech applications are robust and can withstand unexpected disruptions, thereby maintaining trust with users and stakeholders.

Synthetic Monitoring for performance validation

Scripted transactions are used in synthetic monitoring to mimic user interactions with an application in order to track functionality and performance. This proactive approach is crucial for FinTech applications to ensure they perform well under various conditions and maintain a high level of user satisfaction.‍

How to implement Pingdom for synthetic monitoring

1. Set up test accounts: Create test accounts within the FinTech application for the checkout process.
2. Script creation: Write a script in Pingdom that mimics a user completing a transaction using the test account.
3. Geographic testing: Configure Pingdom to run the script from multiple locations to simulate different user scenarios.
4. Schedule monitoring: To constantly monitor the checkout process, set the script to run at regular intervals, such as every fifteen minutes.
5. Alert configuration: Create alerts in Pingdom to let the team know if there are any problems with the script or if performance limits are crossed.
6. Review and Optimize: Regularly review the performance data collected by Pingdom and use it to optimize the checkout process.

Effect of this strategy

‍No matter where their consumers are situated, the FinTech organization can guarantee a consistently speedy and dependable checkout process by utilizing platforms like Pingdom or any other tool to conduct synthetic monitoring. This leads to a better user experience, higher conversion rates, and increased trust in their platform.

Contract testing

A fresh approach for confirming interactions between different microservices and their consumers—like a web interface or mobile application—is called contract testing. It's especially helpful in FinTech scenarios when several services collaborate to manage complicated transactions.‍

How to implement

• Define contracts: Establish clear contracts that define the expected requests and responses between services.
• Implement contract tests: Create tests that validate if the services are adhering to their defined contracts.
• Continuous integration: To guarantee continuous adherence, incorporate contract tests into the CI/CD workflow.

Here’s a conceptual code snippet using the Pact framework for contract testing:

// Define a consumer contract with Pact
const { Pact } = require('@pact-foundation/pact');
const consumer = new Pact({
  consumer: 'MobileApp',
  provider: 'TransactionService',
});

// Write a test for a specific interaction
describe('Transaction Service Contract', () => {
  it('should process user transactions correctly', () => {
    const expectedResponse = { status: 'success' };
    consumer
      .uponReceiving('a request for transaction processing')
      .withRequest({
        method: 'POST',
        path: '/process-transaction',
        body: { amount: 200, currency: 'USD' },
      })
      .willRespondWith({
        status: 200,
        body: expectedResponse,
      });

    // Verify the contract
    return consumer.verify();
  });
});

Effect of this strategy

Contract testing ensures that as individual services are updated, they continue to communicate correctly with other services, preventing failures in the complex web of interactions within a FinTech application.

Visual Regression testing for UI consistency

Visual regression testing is an original method that compares images of user interface elements to find any unintentional changes. This is particularly important for FinTech apps, as user consistency is essential to maintaining trust and ease of use.‍

How to implement

• Capture baseline images: Use a tool to take screenshots of key UI elements.
• Run comparative analysis: After updates, take new screenshots and compare them to the baseline to identify visual changes.
• Review changes: Manually review any detected changes to confirm whether they are intentional or bugs.

Here’s a conceptual code snippet using the BackstopJS tool for visual regression testing:

const backstop = require('backstopjs');

// Define scenarios for visual regression testing
const scenarios = [
  {
    label: 'Homepage Test',
    url: 'https://www.example-fintech-app.com',
    referenceUrl: '',
    hideSelectors: [],
    removeSelectors: [],
    selectors: ['header', 'footer', 'main'],
    readyEvent: null,
    delay: 500,
  },
];

// Run the visual regression test
backstop('test', { config: { scenarios } })
  .then(() => {
    console.log('Visual regression tests completed.');
  })
  .catch((error) => {
    console.error('Visual regression tests failed:', error);
  });

Effect of this strategy

‍Ensuring that consumers always see the recognizable interface they trust is made possible by Visual Regression Testing, which is particularly crucial in the FinTech industry, where trust is of the utmost importance.

Conclusion

The seven innovative Fintech app testing strategies outlined above are not just theoretical—they are practical, actionable, and can significantly improve the quality and security of FinTech applications in 2024. As we look towards the future, these strategies will become the standard, driving the FinTech industry towards greater innovation and excellence.

If you have any queries or are willing to use our Fintech testing services, contact us at Alphabin.