In spite of the wide acceptance of technology in various sectors, there is still a tiny glitch in how customers feel about the intervention of robots while taking financial decisions. Many customers still rely on human decision-making skills over algorithms when it comes to money matters.

For instance, in the banking sector, robo-advisors have influenced almost every aspect of it, especially in customer experience. However, it has been seen that customers feel assured of an organization where the representatives decide for them instead of algorithms, even if the decision result is the same.

This is much more prominent in the banking sector. In instances such as approval of loan applications, customers tend to rely more on suggestions provided by bank representatives over a chatbot or any other AI-based system. Whereas, the same customer is happy with AI-based ATMs where there is little human involvement from the bank.

This contradictory psychology in customers has been revealed by a study carried on by Stefano Puntoni, a marketing professor at Wharton, and his team. He found that there is always a trust issue seen in customers when bots are in charge of their future. Puntoni further said that “we find that people feel a bit alienated and objectified when an algorithm is put in place…”

So, let’s get to know the reasons why customers feel a certain way when it comes to decisions run by algorithms.

 

Why are customers still apprehensive about algorithms?

While algorithms are more objective and unbiased compared to humans, customers still feel more comfortable with human interactions over algorithms. As banks and companies increasingly implement algorithms for the smooth running of tasks, cut down costs, and enhance functional efficiency, simultaneously there is also an increasing point of concern for managers of alienating customers.

It is the customer-organization relationship that keeps up a business and managers are worried that if there is a loss of warmth felt by customers due to bots then there will be a loss of business as well.

It has been found that customers are happy when there is a positive result derived by the bank representatives but are less happy when the same prediction is done by algorithms. The reasons behind this behavior are following.

1. Data security

Banks and other financial institutions, have to deal with a lot of sensitive data and thus data security is a critical issue for them. Though they assure high-quality security measures, there has been a number of instances where security breach was a huge issue.

The anticipation of a plausible security threat with more implementation of technology might be a cause of customers’ adverse feelings.

2. Lack of explainability

AI-based systems have found wide applicability in decision-making processes due to their capability of eliminating errors and saving time. Yet these systems may be quick to follow biases learned from poor human judgment in previous cases.

Since there is a lack of explainability in how the AI models make decisions, customers may be easily misguided risking the bank’s reputation and overall functioning.

3. Trust in human cognitive skills

It is the humans who made the algorithms, not the other way around. This thought has two directions. One, humans are susceptible to errors and thus there might also be the presence of errors in the algorithms.

Two, comparing humans with algorithms, it is easy to say humans have far better cognitive skills. Thus, in both ways, it is better to trust human decision-making skills rather than algorithms.

4. Lack of warmth

It is easily understandable that a relationship blossoms only when there is a certain amount of warmth in it. Algorithms hugely lack in this space. Till now, businesses depended upon this customer-organization relationship to build up a sustainable business.

Whether it is about the brand impression or customer loyalty, it is the good relationship between the customers and the company that mattered. Replacing bank representatives with algorithms while interacting with customers, might form a massive dent in the relationship between banks and customers.

 

The possible ways to improve customers’ reactions

If banks or other financial institutions are looking for ways to mitigate this critical issue, then here are some solutions that might help.

1. Humanizing the bots

Making the algorithms more humanlike might be a plausible trick in influencing customers’ positive behavior. For example, when a company named their algorithm ‘Sam’, they found a better response from their customers. Especially, in the case of using chatbots while interacting with customers, human-like names for bots are helpful in retaining customer attention.

2. Active involvement of the representatives

If organizations want better customer feedback, representatives require to be actively involved instead of merely observing the operations of algorithms. A combination of both algorithms and human involvement would assure the customers of organizational credibility.

3. Transparency

It is a matter of debate whether or not to disclose to the disclose the fact about who is making the decisions, humans or bots. However, to retain organizational credibility it is always recommended to maintain algorithmic transparency to build a secure customer-organization relationship.

In case, anything goes wrong, the organization need not worry about its transparency policy or lose customers’ trust.

4. Tracking insights

Regularly checking the insights on how the algorithms running for customer-facing decisions are affecting customers’ reactions, is also a good approach for any organization. This will help banks to pay attention to customers’ reactions to outcomes, whether positive or negative while automating customer-facing decisions.

 

Conclusion

Though technology has smoothened the path for us, there is still some space for improvement. While the advancement of technology and automation is inevitable, it is equally important to keep a track of customers’ feelings towards increasing usage of bots whenever there is an instance of customer-facing decisions.