How AI is transforming the Zim banking sector

AI can help employees with organising and increasing the efficiency of processes, which increases productivity.

Banks have always improved the way they apply FinTech solutions to stay ahead of the game.

The current FinTech solution to which banks are working to adapt is artificial intelligence, also known as AI.

Zimbabwean banks have started utilising this technology albeit at a slower pace compared to financial players in developed economies.

AI has already impacted a wide variety of operations within the banking industry.

Major processes that have witnessed the application of AI include customer services, credit risk management, asset management, document processing and fraud detection.

AI offers new and innovative opportunities in banking service delivery, and overhead management.

Locally, almost all commercial banks have created strategic departments and subsidiaries focusing on digitalisation and digital capabilities.

AI has impacted customer service departments in banks in a positive, way and customer feedback has started indicating that bankers are creating value through technology.

But human customer service remains critical in more complicated tasks.

Numerous researchers have concluded that, even though customers are satisfied with their experience with AI and accept the technology, they do not accept it equally as human customer service.

Instead of replacing human customer service, AI will mostly play a complimentary role.

AI can help employees with organising and increasing the efficiency of processes, which increases productivity.

In addition, fewer employees will be needed when AI gets integrated into banking processes.

In the last five years Zimbabwean banks have started utilising chatbot systems as one on an effective capability to minimise overheads.

A chatbot is a computer programme that helps a customer by answering questions from customers, using AI techniques such as Natural Language Processing.

The introduction of chatbots by banks helps ease the banking process for the employee.

A chatbot advisor has multiple advantages over a human advisor.

By being available round the clock giving instant answers to customers, chatbot improves turnaround time and increases the efficiency within the customer service process.

Still, human advice is preferred when it comes to accountability and effectiveness.

For this reason, in case a chatbot does not provide the answers a customer wants,escalations will be redirected to manual formats.

Another important process in which AIs play an important role is the customer on boarding process.

This process includes all activities that are involved in introducing a new customer to the bank.

This includes, among others, data and document collection, identification checks and know your customer (KYC) procedures.

The steps taken in this process are crucial as they can make or break the ongoing relationship with the customer.

Introducing AI speeds up this process and increases the security to start the new relationship with the customer on good terms without any prior physical engagements.

While the Zimbabwean banking industry is benefiting from AI, it comes with its downside risk factors.

General disadvantages observed thus far include:

High costs of implementation;

High maintenance and support costs;

High cyber risk; and

Shifting employment cost biased towards technical human resources

Beside the generic chatbots limitations, the tool also carries specific issues depending on the nature of deliverables.

A specific disadvantage of a chatbot can be around accuracy of responses on unique and unstructured enquiries. The chatbots are trained with a specific data set.

If an application contains data that is not accurate, the chatbot will provide inaccurate answers.

Further, chatbots have difficulties with handling complex queries.

Therefore, customer service should still have employees since AI cannot be expected to handle all possible queries of the customers.

Using AI in asset management also has its limitations and potential risks.

A big potential risk of using AI when making trading investments is over-fitting, using too much known information to create the algorithm.

 This concept of over-fitting means that the algorithm is trained to exactly match the training data, with the result that the algorithm is not accurate for unseen data. If this happens, the AI is useless for making trading decisions.

Using historical data to train these algorithms also makes them particularly ineffective during a crisis.

The reason for a crisis cannot always be traced back to historical data.

The rise of the crisis can be quite sudden. This makes the market more volatile and more unpredictable than normal.

Risks of the implementation of AI can also be found in financial crime protection.

A big risk is that a bank can lose its credibility when customers are mistakenly seen as fraudsters.

This type of mistake is also known as a ‘Type One Error’ or a false positive.

This mistake can be made by the system due to a racial bias within the algorithm.

A ‘Type Two Error’ can also happen when a fraudster is not detected.

The risk of bias is not only relevant for fraud detection systems.

This can also happen when AI is used to give credit risk scores, to determine if credit should be extended to the customer.

AIs also have some regulatory bottlenecks in areas where this technology is utilised.

Further, the implementation of these RegTechsolutions brings multiple challenges.

One of these challenges is the inconsistency in regulations between different national and international regulators.

Another challenge is that of replacing legacy systems that do not integrate with the new technologies.

Digital innovations in the modern banking landscape are no longer discretionary for financial institutions.

Instead, they are becoming necessary for financial institutions to cope with an increasingly competitive market and changing customer expectations.

In the era of modern banking, many new digital technologies have been driven by AI as the key engine leading to innovative disruptions of banking channels

Banking is at a pivotal moment. Technology disruption and consumer shifts are laying the basis for a new S-curve for banking business models.

Even though AI is at the root of innovation, it has already made a significant impact on the Zimbabwean banking industry, providingimprovements in the processes of client service, credit risk assessment, asset management, crime detection and regulatory compliances.



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