The Role of Data Analytics in Banking


When it comes to the dynamic landscape of the banking industry, digital transformation is no longer considered the solution, it is now a necessity. Research conducted by Cornerstore shows that three-quarters of banks and financial institutions (FIs) have already implemented a digital transformation initiative in 2022.

A persistent trend can be observed in the industry where banks are gearing towards more customer-centric approaches through leveraging data analytics for more precise decision-making. This further asserts just how imperative digital solutions are for banks in order to remain competitive.

What is Data Analytics in banking?

In addressing some of the industry’s biggest challenges, data analytics has long become an integral part of FIs’ operations as it can monitor and anticipate sudden changes in the market. Nowadays, banks can leverage data analytics to derive granular insights from massive amounts of data. Out of all the digital tools available, data analytics is one of the most reliable when it comes to providing banks with the means to enhance their customer experience and identify opportunities for revenue growth.

This digital tool is a relatively broad term, as it encompasses many different kinds of analysis such as customer analytics, business analytics, predictive analytics, and many more. FIs can utilize data analytics to improve their end-to-end processes, from learning more about their customers to innovating predictive models and forecasting revenue growth.

Utilizing Data Analytics

Data analytics can modernize credit risk modelling by factoring aspects that the traditional model could not gain access to, such as social media profiles, utility bills, monthly spending, and savings. By considering these dynamics in credit risk modelling, risk mitigation can be greatly improved due to the profound insights that data analytics can bring to the table. AI-based text analysis and consumer personas can ultimately help banks make better decisions and lend to the right types of customers. This reduces the delinquency rate and therefore boosts profitability.

Though risk models differ from one FI to another, generally for banks, the major models would be credit risk, fraud risk, and liquidity risk. With data analytics, banks’ fraud detection systems can be leveraged to become more intelligent as they analyze many types of transactions and behaviours using predictive, behavioural, and advanced analytics. Hence, better risk management and mitigation for banks.

With data analytics, banks can gain access to a more detailed customer lifetime value, which predicts future revenue sources from the customer. However, with constant behavioural changes over time, it can be challenging for banks to accurately estimate the factors that can impact customers’ decisions. Hence, by using data analytics to integrate AI-powered advanced models, banks can recognize customer patterns more effectively in the data and gain behavioural insights that could not be identified otherwise.

Benefits of using Data Analytics

1)    360-degree insights on customer behaviour

A notable strength of data analytics would be how it can assist FIs in knowing more about their customers’ preferences and identifying important multichannel touchpoints and buyer behaviour factors. With a more thorough understanding of the customers’ behaviour, banks can strategize more effective marketing initiatives, while at the same time ensuring the overall customer experience journey is always improved and customized according to the customers’ needs and personalities.

2)    Minimizing operational costs

As banks and FIs alike are under constant pressure to maintain high profit margins and improve operational effectiveness, they can leverage predictive analytics, visualization, and AI to automate their workflows, especially if they’re centred around structured data. By integrating data analytics as part of their operations, FIs can digitize many paper-based applications and processes which reduces manual efforts and the chances of human errors.

3)    Strengthening competitive advantage

With many disruptors already showing up in the financial landscape, banks are now competing not only with each other but also with fintech and non-financial organizations (such as tech giants). To maintain their competitive edge, banks need to adapt to data analytics now more than ever. With AI and advanced analytics, loan processes can be done within minutes, opening more space for other customers. Unmet customer needs can also be met with the help of data analytics, allowing banks to unfold new customer-centric business models.

Create a Customer-centric Approach to Banking with INFOPRO

There is no denying that data analytics solutions can optimize FIs’ abilities to oversee their whole organization clearly. Not only does it increase visibility, but it can also assist banks in maximizing their revenue growth, predicting customer trends and reducing manual processes.

Here at INFOPRO, our team of specialists can help you digitally transform your organization across all levels with data analytics. We provide a diverse range of customizable solutions that can be modified according to your business problems and needs. What’s more INFOPRO is here to assist you on your journey of digital transformation every step of the way.

Contact us today for a free consultation and find out how you can take your banking processes to the next level.

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