In recent times, adopting a data-driven strategy has become essential for banks to keep on top of trends and satisfy their clients’ constantly shifting needs.

Le Khanh Lam

Three big trends are affecting the banking industry in the era of data and digitalisation. Firstly, big data technologies are essential to the bank operation daily. Banks may learn about client behaviour, spot fraud, and streamline their processes by utilising the power of big data. Big data will continue to be important to the banking sector in the future.

Banks can use big data to learn more about their customers and discover new ways to serve them, establish deeper connections with them, and provide more value. Additionally, banks may more effectively target their customers with the most effective marketing efforts thanks to customer segmentation.

As a result, the ads are modified in order to better meet the needs of the target audience. Based on big data and AI, banks can analyse user behaviours more accurately.

Moreover, they can also tailor the consumer experience based on the data. As an added benefit, banks would be able to categorise their customers based on various factors, such as preferred credit card spending or even net worth, by watching and tracking every client transaction.

Secondly, using statistical algorithms, machine learning approaches, and predictive analytics determine the banking industry based on historical data. Predictive analytics are utilised in the banking sector to spot credit concerns, client turnover, and even market movements. Predictive analytics enable banks to manage resources more effectively and make more informed decisions.

Thirdly, banks offer fintech banking-as-a-service, allowing them to benefit from the bank’s charters and deposit insurance while offering customers more responsive services. When working with less regulated organisations, banks must have the appropriate cybersecurity measures in place to safeguard both their own data and that of their clients.

In response to rising cyberthreats, including internal dangers, banks often use big data analytics and AI tools to strengthen their cybersecurity procedures. These systems can monitor internal operations and customer activity, assisting in the identification of potential security issues.

Additionally, banks can work with governmental organisations to reduce the dangers associated with financial terrorism by sharing knowledge from their big data analytics and business intelligence tech.

Pros, cons, and big data

Digital transformation can assist banks in developing a seamless and personalised consumer banking experience, boosting client loyalty and satisfaction. Banks can also lessen costs by digitalising internal operations and eliminating the need for physical branches.

Meanwhile, greater competitiveness is fuelled by an organisation’s ability to execute a digital transformation with agility and speed, which enables banks to stay one step ahead of their rivals and better serve their clients.

Banks can also more efficiently monitor and analyse consumer behaviour with the use of digital technologies, which can aid in the detection and avoidance of fraud and other financial crimes.

Disadvantages lead with customer data and digital environment risks. As there are more digital channels, the risk of cyberattacks and data breaches rises. Banks need to put strong security measures in place to safeguard client information and stop unwanted access.

In addition, companies with misaligned talent strategy may be in danger due to a lack of digital capabilities. The main obstacles that organisations fear will impede them from reaching their goals for digital transformation are skill gaps or lack of expertise.

Data has already made waves in the banking industry, and is not just a theoretical idea. There are actual instances of how big data analytics is used in banking and the benefits that customers can receive from them.

Big data is essential to banking institutions’ use of customer profiling. By reviewing a customer’s banking history, personal information, and transactional data, as well as tracking their spending trends over time, banks can provide customised financial solutions and plans.

By doing this, customers can have effective consultation and the accurate services they want from different bank services because banks can use demographic information to target customers with particular goods.

Banks can identify probable fraud before it even happens because of big data and statistical computation. Customers can be guaranteed from banks, which can identify people who may be at danger of committing fraud by using specialised algorithms to track and analyse spending and behavioural patterns.

In retail banks, investment banks, and other financial firms, dedicated risk management departments that can stop fraud and that extensively rely on big data analysis and business intelligence tools.

The digital transformation roadmap

The process of digital transformation in banking is intricate and demands careful planning and implementation. Banks can take several actions to guarantee a successful digital transformation.

In this first step, the bank evaluates the state of its technology and finds out any gaps. Finding technological or talent gaps can help organisations accomplish their objectives. To implement it, banks should define two factors: their vision and current status. Outlining the vision is always the first stage in any digital transformation because it helps businesses have a clear direction for the next steps.

Next, banks create a plan for putting it into practice. This step has banks to get along with the necessary schedules, finances, and resources. One of the most essential elements is engaging stakeholders. The adoption of the banking industry’s digital transformation requires support from all stakeholders, including clients, staff, and partners.

After that, organisations put their data and digital transformation strategy into practice at this level. Introducing new data applications, technologies and processes as well as improving existing ones may be part of this process. Banks implement data to accelerate innovation and the customer experience by altering their offerings, drawing in clients, giving staff members more freedom, and streamlining processes.

Banks adopt modern technologies to their activities such as account management, and may train AI models to help customers set up automatic payments, update personal information, and other account management tools. Moreover, chatbots that can submit insurance claims and obtain details about the claims process could be developed by banks.

They also adopt a customer interaction process. By providing customers with digital services and reducing direct client contact, the Internet of Things (IoT) can change the way that customers interact with banks. Users can easily make payments without using debit or credit cards thanks to IoT technologies and gadgets integrated into banking infrastructure and wearables.

In the process of digital transformation in the banking business, AI has changed the game. There are three degrees of AI used in digital banking, according to a framework that has been presented for the banking industry’s digital transformation.

The first step involves utilising machine learning to train computers to recognise patterns in order to implement digital banking. The use of chatbots and natural language processing for customer support is part of the second level. Utilising AI for fraud detection and prevention constitutes the third stage. To stay competitive, incumbent banks should alter their whole capabilities stack, including the engagement layer, AI-powered decision-making, core technology, and data infrastructure.

Finally, gamification refers to the use of game elements like points, medals, and leaderboards to encourage users to interact with online financial services. Utilising subliminal cues to persuade clients to adopt desirable habits is known as nudging. Utilising peer pressure to persuade clients to adopt desired behaviours is known as social proof.

Banks should evaluate the results of their efforts at digital transformation and pinpoint areas for enhancement. Key performance indicators should be concentrated on, such as the following measures of how many clients connect with their bank via digital channels, digital sales which quantifies the money that digital channels produce or the metric which assesses the contribution made by each priority digital endeavour to the achievement of strategic organisational objectives.

Customer feedback is important to improve the process, and banks should proactively obtain ongoing feedback on crucial issues across the customer and employee life cycles.