A proper balance must always be maintained between the expectations and reality, and it applies to financial services’ industry too. Many banks have failed to capitalize on the data collected over time. Consequently, ended up with limited knowledge of their customers which implies banks cannot custom-make product or messages according to the customer preferences.
Nowadays, small and large enterprises use ‘data analytics’ to make smart decisions. The enormous structured and unstructured data produced by multiple devices across platforms lead to great insights.
In the coming section, let’s discuss how ‘big data analytics’ is being used by banks to achieve sustainable growth.
To Enhance Customer Relations
Recently, banks have embraced digitalization and were trying to expand their customer base by extensively advertising new banking facilities available. Banks have a huge consumer base and with the wide range of financial products sold by them— the complexity increases further. For example, home loans, mortgages, four-wheeler loans, personal loans etc. In traditional banking, a financial product is advertised to the entire customer base, ignoring the fact whether that product suits the customer needs or not. But in modern banking it is a different case, banks started giving importance to customer preferences and accordingly tailoring their promotional activities.
With the help of Big Data, banks create specific databases which could be of great value. The digital transformation helps banks to move from traditionally structured datasets with vague consumer data to more defined datasets. Big Data helps banks to understand the customer better: by knowing their habits, preferences, and lifestyle. By retrieving consumer data from different online and offline channels, banks can offer financial products according to their customer needs. Customer analytics combined with Big Data can make available customer data more valuable since it combines behavioral, transactional, and social data. So, customer satisfaction will be increased as they get more customized banking experience.
Better Risk Management
Banking has always risks attached to it, the more the bank grows the more will be the risks. Big Data and analytics can provide better insights to the banks regarding their customers, transactions, environments, and systems to help them mitigate risks. For example, a bank can use analytics to interpolate the weather data with the ‘reliability’ and ‘age’ of the buildings in the area. Based on the insights, banks can take a decision on approval of insurance. Historical sales-data merged with regional economic data helps to find out when the housing market is on the verge of revival; when to invest on renovation and helps in identifying potential markets to offer low-interest loans.
Also, banks assess the data and locate the factors leading to non-payment of loans and devise programs to avoid them. Analytics enable transparency in the entire system, thus banks can easily detect internal or external fraudulent activities and use the information of fraud patterns to mitigate future threats.
Retention of High-Value Customers
The business intelligence and data analytics collectively help banks identify the high-value customers and get information on their banking preferences. It helps banks to retain profitable customers, present suitable financial products to them and understand which products bring maximum returns.
Let's consider a case wherein a customer has a habit of visiting a certain place for breakfast or for shopping. Retrieving the data and analyzing the data can provide a base for offering highly personalized offers which have a high propensity for acceptance. Taking a step further by embracing mobility, offer details can be sent by SMS at the right time to enable quick decisions.
Smart Dashboards for Data Visualization
Data analytics allows bank staff to visualize data using animations, graphs, charts and customized interfaces. The managerial team can pull customized reports by simply running queries. For example, the regional-wise profit and loss, monthly operational expenses or monthly volume of loans (in %) can help the decision makers to get a clear picture of the scenario.
The old way of doing things is risky these days. The businesses should evolve and adopt new technologies to succeed in the ever-changing market. Implementing the Big Data Analytics and instilling it into the existing workflows of banking sector helps banks to survive and succeed in the race of digitalization.
Banks have become more sophisticated today and have invested largely into modernizing infrastructure and providing mobile-based services. In the years to come, they will have to learn to enable operations with Big Data Analytics, Machine Learning, and Artificial Intelligence.
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