Big Data Analytics vs Corporate Banking: What’s the Difference?
Have you ever thought about the data we create every day? Every message you send, every credit card transaction, every website you open creates data. All of these actions lead to 2.5 quintillion bytes of total data. It is the amount of data that Internet consumers around the world create on a daily basis.
However, the availability of this amount of data also creates endless opportunities with certain challenges for forward thinking industries across the world. Likewise, the corporate banking industry also capitalizes on this big data, as do other industries.
Digital banking has been used by more than half of the world’s adult population. As a result, financial service providers now have enough data to become more efficient and optimized in their operations.
Big data in banking: what do you need to know?
Arguably, banking is a prime example of how technology can revolutionize the customer experience. Gone are the days when bank customers had to queue just to deposit their checks. This is because customers can now easily carry out their banking activities through cell phones. Using mobile apps, they can do everything from money transfers to checking bank balances to paying bills, deposit checks, and more.
These features have brought astonishment to the customer experience. As most banking business takes place online, capturing and saving big data is no longer difficult for the banking industry.
This is why big data analytics in banking has become so important. Banks are now able to establish a 360-degree view of customers using both transactional and personal information.
Plus, with analytics-based tools and strategies, banks can now harness the true potential of big data. More importantly, companies that qualify their earnings through big data analytics in the banking industry experience an average 8% increase in revenue. In addition, they benefit from a 10% cost reduction, according to a BARC survey.
The main advantages of Big Data Analytics in the banking sector
Big data and analytics revenues are expected to reach $ 260 billion globally by 2022, according to an IDC report. Banking is arguably one of the most important business areas that invests the most in big data analytics technologies.
Big Data has developed in many business segments. The use of advanced methods such as artificial intelligence, data mining, predictive analytics, etc. ensures faster and superior business decisions based on big data analytics.
( Read also: Retail banking vs corporate banking: what’s the difference? )
Banks and other financial organizations regularly record millions of transactions. More importantly, these are usually real-time inputs. Even if recording this volume of data is quite difficult for the banking industry. However, the analysis of big data in the banking sector is an effective way to systematically record these transactions. Saving this large volume of data can benefit the banking industry in a number of ways.
Let’s see some of these benefits of big data analytics in banking below to learn more about them. So let’s go :
Get insight into the complex aspects of an individual’s life
Customer segmentation is now commonplace in the banking industry. This is because it helps credit unions and banks categorize their customers optimally. However, basic customer segmentation lacks the coarseness that these organizations need to understand their customers’ wants and needs.
Instead, big data analytics in the banking industry make segmentation easier and take it to the next level. As it can allow you to gain insight into the complex aspects of an individual’s life. This information takes into account various factors, including:
- Customer demographics
- Number of accounts associated with a customer
- Products that a customer currently owns
- The great events of life
- Offers that customers have already declined
- Products that they are more likely to buy in the future.
- Service Preferences
- Behavior models
- Attitude towards the bank and much more.
All in all, big data analysis can allow you to gain insight into complex areas of a person’s life, from their lifestyle to their preferences. Thanks to this, it becomes easier for banks to offer a more personalized experience to their customers.
Better risk management
Big data analytics can also help banks dramatically improve their risk management. This is because big data can provide real-time insight into the behaviors of your customers. Also, it can help banks make more informed decisions in the best possible way.
Using smart algorithms can help prevent potential malicious actions. These tools can further help to assess risks and manage their services accordingly to increase their productivity and efficiency in the best possible way.
Big data analytics in the banking industry can also help reduce fraudulent behavior.
Identity theft has become one of the fastest growing types of fraud. About 16.7 million victims were victims of identity theft in 2017 alone. This was a record number of cases, followed by previous records in 2016.
However, big data analytics has helped banks drastically reduce those numbers. That’s because monitoring customer spending habits and identifying unusual behavior through big data analytics in the banking industry helps prevent fraud. Ultimately, customers feel more secure when using their services.
Identify upselling and cross-selling opportunities.
Businesses are more likely to sell to their existing customers than to attract potential leads. This means that upselling and cross-selling can be among the easiest opportunities for banks to effectively improve their share of profits. Additionally, identifying effective upselling and cross-selling opportunities has become easier through big data analytics in the banking industry.