By Khurram Mir, Chief Marketing Officer at Kualitatem and Kualitee
Advanced technologies have taken over a variety of industries, and fintech is no exception. Numerous cutting-edge innovations such as blockchain and AI have changed how the financial sector handles business, reducing processing time and improving efficiency. Banking, insurance, wealth management, and electronic payments have seen significant benefits from this type of automation, especially in the testing process.
With that said, relying solely on AI to test software applications could lead to false outputs, putting the entire business at risk. Up to 30% of AI results are false positives, which can cause significant damage to business operations. This is why human testing alongside AI testing is essential to improve smooth operations, which this article will address in detail.
The Limitations of AI in Fintech
AI has brought substantial growth in the fintech industry, allowing the market to grow from $6.67 billion to $22.6 billion by 2025. This suggests a 23.7% CAGR that is expected to increase even more as time goes on. It’s all thanks to AI’s ability to use and apply raw data flexibly.
The issue is that AI technology is often exclusive to a certain group of people, making it increasingly challenging for minorities to use. For instance, blind, mute, or those with limited mobility may have difficulties operating the AI-driven system, rendering them unrecognizable due to a technicality.
AI systems also tend to discriminate, learning from the human’s biased attitude to be racist. As an example, based on previous data, they can unfairly label ethnicities such as Muslims as untrustworthy and criminal. This might make it difficult for them to use fintech services smoothly, as minorities tend to be excluded.
The main issue with AI is that it is flawed without an extensive database that has been trained continuously from every possible angle. Its purpose is to imitate user behavior, ultimately being unable to think outside the box and learn for itself. This can lead to bugs slipping through the cracks, creating vulnerabilities that could risk users’ money and private data.
Read More : AI’s Impact on Emerging Risk Management Trends
Why Human Involvement Is Essential
While AI testing can do an excellent job of reducing tedious, repetitive tasks, human involvement remains mandatory. These are some of the main reasons why:
Complex Decision Making
AI can be very helpful when providing answers, especially when you have a straightforward case ahead of you. Think of it as a high-performing search engine: it looks through massive amounts of data to achieve the desired results. However, when it is lacking the information, it can lead to hallucinations. The system uses theoretical data to make up an answer, not realizing it is unrealistic.
Human involvement can prevent that from happening. Unlike AI, humans are much more capable of weighing variables to make better decisions. They focus on what is realistic rather than what is theoretically possible. This way, they can better align with the organizational vision and culture.
User-Centric Design
When AI systems are used in Fintech, they can handle the bulk of many complex tasks. The result is usually something high-tech that brings them to the highest levels of innovation. The problem is that while these high-end AI developments have a good way of determining the potential in something, they also lack the human-centric part of the equation.
Adding human testing to the mix allows developers to gather more information about the user experience. They can ensure that the services are accessible and the interface intuitive, catching onto technicalities that an AI tester otherwise would not be able to. This can be essential if you have groups with different tech knowledge levels.
Bias Identification
Bias is a problem that both AI and human intelligence go through. However, while humans can catch onto these issues the moment they are pointed out, artificial intelligence is more difficult to train. Moreover, bias in AI is not just about the data – it’s about what the system does with it. Using technical thought, they find the easiest potential route to solve a possible problem or bug.
The problem is that while these ideas may have merit, they do not hold the human’s best interest in mind. Basing their results on data majorities that have not been trained on every angle can be potentially risky for a financial company. Human involvement can assist the AI in catching these issues early, improving the overall user experience.
Trust and Transparency
AI is effective in various sectors, speeding up the processing time in industries such as Fintech. However, only about 10% of them are fully trusting in AI’s ability to make things easier. About 53% believe it will do more harm than good, and the rest have a certain level of wariness. This might make them skeptical about using your product if they believe AI did all the testing.
By adding human testing into the mix, companies can show that their systems have been rigorously tested by an entity they can trust. This will make them more likely to use your product, as they will know an actual human being values their feedback.
Concluding Remarks
AI testing is a step toward improvement and innovation, but it’s not without flaws. Combining it with human testing is much better to get the best results. This way, each type of testing can pick up what the other has missed, improving company operations.
Read More : Global Fintech Series Interview with Christy Johnson, Chief Product Officer at Versapay
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