What Is Data Analytics And Its Role In Banking?
Several institutions are realizing that ever-changing customer expectations and out-of-date technology are some of their main challenges.
According to recent findings by Cornerstone Advisors, close to three-quarters of credit unions and banks are embracing digital initiatives. Ending 2022, 11% of banks had not yet launched digital transformations in their business models.
Of all the digital tools available in the market, only data analytics offers a means of identifying revenue growth opportunities and improving customer experience. This blog post will explain data analytics and its role in banking.
What is Data Analytics?
According to Investopedia, data analytics refers to the science of raw data analysis to help develop conclusions concerning that information. The raw data is structured or unstructured and comes from external and internal sources.
Financial institutions employ data analytics in their engagement with consumers and operations. They can improve customer relationships to offer better services. Also, that can help them enhance their processes for better growth opportunities. Data and analytics in banking encompass several analysis forms, such as predictive, business, and customer analytics.
Role of Data Analytics in Banking
Data analytics is an asset for any banking institution as it plays different roles, including:
1. Offers a 360 Degree of Customer View
When banking institutions apply data analytics to customer data, they will get accurate details on their customers, what matters most to them, their primary motivating factor, and much more. The banks can use sentiment analysis to help them know how their customers feel about their services.
With all this information, banking institutions can pinpoint what their customers need most. Such insight levels will help them define their marketing efforts and sales, enabling them to deliver the right services and products to bright customers at the right time.
Banking staff can build a deeper understanding of customer financial requirements and improve performance when they more time connecting with them. Data analytics will help bring up a more profitable and productive institution.
2. Builds a Stronger Customer Relationship
One of the most significant issues affecting most banking institutions is customer attrition due to a lack of personalization and frustration. According to recent research, only 21% of frustrated customers plan to remain with the bank, while 13% will still advocate for the bank. Furthermore, 57% of millennial and Gen X customers will leave their banking institutions to seek better services elsewhere.
Banks use churn analytics to help them identify weak spots in their organization, develop and test different theories about why their customers churn, and help them determine which customers are likely to churn. After identifying areas to improve on and the customers who are at risk, banking institutions will develop the right strategies to ensure high customer retention.
3. Helps in Reducing Operational Costs
Banks are constantly under pressure to increase efficiency while reducing operational costs. In the past, some banks have tried reducing operation costs by reducing the number of staff. That has yet to get to the crux of the issue.
To achieve this goal, banks will need to adopt data analytics. Analytics will help them identify weak spots in their organizations. Analytics will also help them unearth opportunities that will help them reduce unnecessary expenses.
Data analytics will enable banks to develop efficient strategic recommendations that will help them optimize their processes.
4. Better Management of Risks and Mitigation
There are several ways data analytics can help banks protect themselves from risks. For example, using customer analytics will help segment customers into groups depending on their creditworthiness for credit risk management. It enables them to narrow their target for credit products and reduces exposure to default risk since they can count on the customers to make their payments regularly.
Banks can also use predictive analytics to help prevent fraud by analyzing customer behavioral patterns and detecting anomalous behaviors. Using analytics to help detect fraud and prevention will help protect customers’ and banks’ interests, as it will save them from possible retaliatory action or reputation damage.
5. Saves Valuable Time
Time is Money! Banks investing in data analytics can streamline operations and save the banking staff time. Data analytics will help do away with spreadsheets. It organizes data and frees up gray space in an organization.
Banking staff will be able to locate whatever they are looking for, allowing them to focus on meaningful tasks. They will not have to waste time organizing and sifting through lots of data as they can spend more time analyzing the data and developing strategic decisions that will significantly benefit the banking institution and its customers.
Conclusion
With the world increasingly becoming dependent on information and the collection of statistics, data analytics supports people and organizations in making sure they have the correct data. Using different techniques and tools enables banks to come up with raw numbers to convert to educational and informative insights that will help drive decision-making and meaningful management.
