Using Artificial Intelligence to reshape the banking sector


The fusion of Artificial Intelligence and banking is ushering in a new era of financial services, wherein innovation, efficiency and customer centricity are redefining the industry. With digital upheaval rippling across the world – rapidly transforming industries and revolutionising businesses with its power, no sector can afford to get marooned on the sidelines. As every industry engages in designing and developing approaches and methods to remain relevant in a world steered by technology, the banking sector is no exception.

Customers, now familiarising themselves with advanced technologies and techniques in their everyday lives, no longer expect banks to be characterised by long queues, frequent visits and excruciating degrees of paperwork. They need transformations, and they need them fast.

To keep pace with these expectations, banks have bolstered their industry outlook to retail, IT and telecom in order to facilitate services like mobile banking, e-banking as well as real-time money transfers. Besides, these advancements have enabled customers to avail themselves of most banking services and have them at their fingertips anytime, anywhere.

What is Artificial Intelligence (AI)

Artificial Intelligence refers to using of machines for performing tasks that normally require human intelligence – such as learning, reasoning and problem-solving (Source: Kerem Gulen, 2023). The application of Artificial Intelligence in banking has become more widespread in recent years, as financial institutions seek to remain competitive and meet customers’ changing needs in a rapidly evolving digital landscape.

In other words, Artificial Intelligence is the adoption of human intelligence processes by machines: especially computer systems and mobile applications. It enables the creation of intelligent machines which work and react like humans. Some activities that Artificial Intelligence can be designed for include speech-recognition, learning, strategising and problem-solving (Source: SGS Technologies).

What Influences the Use of Artificial Intelligence in the Banking Industry

Artificial Intelligence is changing the quality of products and services the banking industry offers. It has not only provided better methods to handle data and improve customer experience, but also simplified, sped up and redefined traditional processes to make them more efficient and competitive.

For many years, the banking industry has been working on transforming itself from a people-centric business to customer-centric one. This shift has forced banks to take a more holistic approach in meeting their customers’ demands and expectations. With their focus now on the customer, banks must begin thinking about how to serve them better.

Customers now expect banks to be there for them whenever they need them – which means being available 24 hours a day, 7 days a week, and they expect their bank to do it at scale. The way banks can do this is with Artificial Intelligence. In order to deliver on these customer expectations, banks must first overcome some of their internal challenges, legacy systems, data silos, asset quality and limited budgets. As these are just some of the issues that inhibit banks from moving quickly enough to keep up with their customers’ demands, it’s no wonder banks have turned to Artificial Intelligence as an enabler of this change.

Steps Banks Can Take to Adopt Artificial Intelligence

Now that we have seen how Artificial Intelligence is used in banking, it is imperative to look into the steps banks can take to adopt AI on a broad scale and evolve their processes while paying due attention to the four critical factors – people, governance, process and technology as detailed below:

Step 1.  Develop an AI Strategy

The implementation process starts with developing an enterprise – level AI strategy, keeping in mind the goals and values of the organisation. It is crucial to conduct internal market research to find gaps among the people and processes that AI technology can fill. Make sure that AI strategy complies with the industry standards and regulations. Banks can also evaluate the current international industry standards.

The first step in AI strategy formulation is to refine the internal practices and policies related to talent data, infrastructure and algorithms to provide clear directions and guidance for adopting AI across the bank’s various functional units.

Step 2.   Plan a Use Case-driven Process

The next step involves identifying the highest-value AI opportunities, aligning with the bank’s processes and strategies. Banks must also evaluate the extent to which they need to implement AI banking solutions within their current or modified operational processes.

After identifying the potential AI and machine learning use cases in banking, the technology teams should run checks for testing feasibility. They must look into all aspects and identify the gaps before implementation. Based on their evaluation, they must select the most feasible cases. The last step in the planning stage is to map out the AI talent. Banks require a number of expert algorithm programmers or data scientists to develop and implement AI solutions. If they lack in-house experts, they can outsource or collaborate with a technology provider.

Step 3. Develop and Deploy 

After planning, the next step for banks is to execute the process. Before developing fully-fledged AI systems, they need to first build prototypes to understand the technology’s shortcomings. To test the prototypes, banks need to compile relevant data and feed them into the algorithm. The AI model trains and builds on this data; therefore, the data must be accurate.

Once the AI model is trained and ready, banks must test it to interpret the results. A trial like this will help the development team understand how the model will perform in the real world. The next step is to deploy the trained model. Once deployed, production data start pouring in. As more and more data start coming in, banks can regularly improve and update the model.

Step 4. Operate and Monitor

The implementation of AI banking solutions requires continuous monitoring and calibration. Banks need to design a review cycle for comprehensively monitoring and evaluating the AI model’s functioning. This will in turn help banks in the management of cybersecurity threats and ensure robust execution of the operation stage. Therefore, banks should take appropriate measures to ensure the input data’s quality and impartiality.

Benefits of Artificial Intelligence in the Banking Industry

The impact of Artificial Intelligence in the banking sector has been transformative, with the following benefits:

A. Regulatory Compliance and Fraud Detection

The banking industry has had a colourful past, costing investors considerable amounts of money. Legislation lays out hefty penalties for players caught in violation of the industry’s regulations. It is therefore in the best interest of banks to automate compliance where possible.

Using a Decision Management System (DMS) allows for early fraud detection and comprehensive audit documentation. Third-party auditing exercises can be disruptive to regular operations when employees are called away from their desks to provide missing details or explain entries. With the right software and machine learning, information captured in the system will be accurate and errors immediately highlighted or disallowed.

As financial institutions increase their vigilance, fraudsters alter their behaviour. Since large-sum transactions are flagged for investigation, fraudsters have learnt to deal in amounts just under the limit of detection. Without proper analysis, criminal activity can go undetected despite not meeting the prescribed requirements.

This is one area where AI is genuinely superior to humans.  AI analyses large amounts of data and picks out suspicious transactions. Manually analysing such transactions leads to mistakes. Without an AI fraud detection system in place, it’s a field day for criminals to launder money or finance illegal activities.

B. Reduced Operational Costs and Risks

As much as we enjoy human interaction, it has one significant drawback. Errors are common, and they can have serious repercussions. Even when experienced employees are at the helm, the wrong key-stroke could expose the institution to liability and cause irreparable reputational damage. Decision Management Systems (DMS) reduce this risk by creating logic flows in data capture, and combining predictive and prescriptive techniques to solve business problems.

Using on-board as an example with the use of DMS, you can set up roles that show the client what type of accounts they can open depending on their bio-data or business information. If a client is opening an account online, age and source of income can determine the type of account available to them. In that case, underage persons cannot open accounts in their own names and personal savings accounts will not have an overdraft facility. This means that you need fewer customer-facing employees, which reduces your labour cost. Furthermore, with the increased accuracy, the number of people needed by the institution to assess transactions and activities is further reduced.

C. Better Customer Experience

There is a reason why people have derided banking hours. Banks never seemed to be open when you need them most; such as later in the day or on holidays and weekends. Call centres of some banks are not effective and/or receptive, and are notable for long waiting times.  Even when finally engaged, they often can’t resolve the customer’s issue. AI technologies are changing that. Customers are constantly looking for convenience – for example, the ATM was a success because customers could access a vital service even when banks were closed.

A chatbot, unlike an employee, is available 24/7 and customers have become increasingly comfortable using this software programme to answer questions and handle many standard banking tasks which previously involved person-to-person interaction. The COVID-19 outbreak underscored their usefulness. AI took a leap forward during the pandemic, because anything that can be handled by a bot doesn’t have to be handled by a person.

In addition to fielding customer service inquiries and conversations about individual transactions, banks are getting better at using chatbots to make their customers aware of additional services and offerings. For, example, business customers might not be aware of merchant services and loan offerings that can help resolve payment or credit issues. Supported by predictive analytics and AI tools like machine learning, chatbots (and customer service agents) can make the right offer on the right device in real-time, delivering highly personalised service and potentially boosting revenue.

D. Improved Loan and Facility Evaluation

Using credit scores to evaluate eligibility for financing often relies on outdated information, misclassification and errors. However, these days there is so much information available online that can give a more realistic picture of the person or business under evaluation.

An AI-based system can give approval or rejection recommendations by considering more variables even when the party, whether personal or business, has little documentation.

E. Improved Investment Evaluation

Interest income is only one facet of income generation. As a result, banks are continuously searching for lucrative opportunities to invest and earn a healthy return. The right investment software can provide investment recommendations that match the risk appetite of these institutions. In addition, they can accurately evaluate client funding proposals, given that industry-specific information is often difficult to understand.

The investment analysis software makes the process easier and accommodates more variables. If the institution has interests outside its national borders, accessing information can be a challenge; but the right AI software is instrumental in hastening the process.

Risk and Challenges of AI in the Banking Industry

While the use of AI in the banking industry offers several benefits, there are some challenges and concerns associated with its use. Let’s explore some of the most significant drawbacks or pitfalls, as detailed below:

A. Data Privacy and Security

As financial institutions collect and analyse more data by using Artificial Intelligence algorithms, the risk of data breaches and cyber-attacks increases. One of the key challenges of AI in banking is the amount of data collected which contain sensitive information – and therefore requires additional security measures to be implemented. It is thus imperative to look for the right technology partner who offers a variety of security options to ensure strong data privacy and protection of the bank’s customers.

B. Bias and Discrimination

Artificial Intelligence algorithms are only as unbiased as the data they are trained on. If the data used to train an AI algorithm is biased or discriminatory, the algorithm will produce biased or discriminatory results. Financial institutions must ensure that their AI algorithms are trained on unbiased and diverse data to avoid perpetuating bias and discrimination.

C. Customer Transparency, Explainability and Trust

Use of AI in the banking industry can create a perception of reduced human interactions, which may affect customer trust. Creating AI models that provide accurate predictions will only be successful if they are explained to, understood and trusted by customers. Since customer information is likely being used to develop these models, they will want to be sure their personal information is being collected responsibly, handled and stored securely.

Some will even want to understand the basics of how it’s being used.  Sometimes it can be unclear to people whether they are interacting with Artificial Intelligence or a person. As a result, financial institutions must be transparent about their use of AI and provide customers with clear explanations of how AI is being used to provide services.

D. People Still Prefer to Deal with People

Even though clients are direct beneficiaries of improved efficiency when dealing with machines, they are still suspicious of a fully automated system. In truth, a glitch in the system can spell disaster for a client – and negotiations are not possible with software. Whenever banks update their system and a bug is exposed, social media is always aflame with complaints and bad-press. Unfortunately, no one sings praises when things go well. Even though these incidents are few and far between, it hurts the brand.

E. Integration With Legacy Systems

Integrating Artificial Intelligence with legacy systems can be challenging, particularly for institutions with complex and fragmented IT systems. Institutions must ensure that their IT infrastructure is capable of supporting the integration of AI, and that their employees have the necessary skills to work with AI technology.

Future of Artificial Intelligence in the Banking Industry

The adoption of AI in the banking industry has already brought significant benefits. However, the potential of AI in banking is far from fully realized – and the future possibilities are even more exciting. As AI technology continues to evolve and improve, it will transform banking operations in new and unexpected ways.

Overall, banks that invest in AI will definitely have a competitive advantage in the future. They will be able to provide more personalised services, make better decisions and increase efficiency and profitability. So, it is crucial that banks continue to explore and experiment with AI to stay ahead in the landscape of banking.


In conclusion, the emergence of Artificial Intelligence in the banking industry presents both threats and opportunities. While there are concerns about its negative impact, there are also opportunities. Therefore, whether there are advantages or disadvantages from technological advancement depends on how we choose to use this technology. It is up to us to ensure the benefits of AI are shared by all, and that we address the risks and challenges it presents to create a better future for everyone.

According to the President-Future of Life Institute, Max Tegmark: “Everything we love about civilisation is a product of intelligence; so, amplifying our human intelligence with Artificial Intelligence has the potential of helping civilisation flourish like never before – as long as we manage to keep the technology beneficial”.


Robert is a Fellow of the Chartered Institute of Bankers (Ghana) and a seasoned banker with wide experience in Retail Banking, Internal Auditing, Project Management, Electronic Banking with high specialty in Internet Banking. He is also a Consultant and Supervisor of Chartered Institute of Bankers (Ghana) examinations.


E-mail addresskwa [email protected]; Tel. 0240 821597 & 0546 907904


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