Data is becoming more and more critical in every business, and banking is no exception. You can’t do banking without your data, and it’s essential to your overall financial health.
A growing number of financial services companies are exploring Oracle Flexcube universal banking in using data and predictive analytics to improve the client experience and increase the value of their assets.
As a result, banks are seeking more than just incremental benefits. They are looking for new sources of income by using data to uncover previously untapped potential that may have a significant influence on their bottom line. Isn’t it time for you to use your data to its fullest potential?
If that’s the case, what should we do? Data in banks and other financial organizations plays a critical role. How can data be used by financial institutions to provide data-driven banking services? Can financial institutions benefit from data-driven decision-making?
What is data-driven banking?
The term “data-driven banking” refers to a new method of looking at your bank records. As an alternative to providing you with a list of your account’s information or showing you currency fluctuations, banks utilize this information to enhance their own goods and services.
To put it another way, data-driven banking refers to employing data and analytics to improve the experience of bank clients.
You may use technology and analytical procedures to help you plan your strategy and make better decisions. With better data on their clients’ and customers’ demands, banks get a deeper insight into the lives of their customers as well as an advantage in terms of growing earnings.
In addition to helping you to make smarter judgments, this also offers societal advantages – allowing individuals to learn about a bank’s goods via third-party firms.
How is data changing the banking landscape?
- Better Customer Experience
As technology advances, so does the way data is used in banking. Customers used to be able to open a simple bank account with only a few basic options. The banking experience changed as their requirements and the financial situation changed. Most clients now have several bank accounts with a broad range of options. They have improved access to their data via applications, mobile devices, and the internet.
Automated data collection and analysis using Oracle Flexcube 14.x have also substantially impacted operational departments, including those that deal with customers.
Customers are increasingly turning to “digital” or “personal” banking options, which means conventional banks will see a drop in income.
- Automated Credit Approval
One of the most innovative ways to handle client accounts is through the use of computerized credit approval systems (ACS). Customer data, such as payment history and purchases, is used to build a machine learning model that is customized to the customer’s particular risk profile. Faster and more efficient account servicing is possible with only the click of a button.
New opportunities for clients are now accessible since they may now acquire financing for applications that were previously discounted or only available on weekends and holidays.
Family and individual financial stability will be improved as a result of faster and easier access to credit for less-qualified consumers, as well as the chance to create good habits by obtaining credit at a slightly reduced cost.
- Better Risk Management
Banks must take a more proactive approach to risk management, beginning with gathering and analyzing as much data as possible. Better risk management, lower expenses, and a more pleasant customer experience are all benefits that follow.
The way banks operate is changing as a result of data-driven decision-making in important areas like risk management. In order to improve risk management, more data is being gathered, and this data is being used to provide actionable insights.
A more strategic approach to risk management is possible when these strategies are integrated, considering the impact of their actions on customers, companies, and the economy as a whole.
- Capital Reconstruction
Rebuilding capital is the fourth and most significant use case for big data that will have a significant influence on the banking sector. A company’s financial deficiency may be found using data-driven analytics, which subsequently identifies and pursues other sources of funding to bridge the gap.
Within only a few weeks of identifying regenerative investment opportunities, cost savings and enhanced shareholder value may be realized.
To stay competitive in a fast-evolving market, conventional financial institutions must remain adaptable and responsive as more customers turn to technology to address their financial problems.
- Faster Fraud Detection
One of the most difficult parts of integrating data-driven analytics into daily banking is the identification of fraud.
To prevent future losses due to insider assaults, cyber-attacks, fraudulent activity by unauthorized parties, and other forms of data abuse, companies must improve their fraud detection processes. Due to the persistent little acts of misconduct by workers or consumers that slip through the net, lengthy investigations might take months or years to complete.
Banks can better react to their customers’ shifting risk behaviors while decreasing operating expenses and regulatory concerns by accelerating fraud detection via data.
- Productive Sales and Marketing Engines
For the banking industry, data-driven marketing and sales are about to take off. It’s possible that banks may use the additional information they have about their clients to make better judgments regarding marketing campaigns and sales channels.
Banks are beginning to realize how integrating data may enhance campaigns and raise profitability, which is great for both marketers and customers.
To better serve customers, banks are always searching for methods to make better judgments based on data. Self-service technologies, such as Flexcube, that directly touch customers are becoming increasingly common in the financial sector as more data is made public.