Banking, financial services and insurance (BFSI) is a data-driven industry comprising of multiple data sources. The different data sources include cash transactions, ATM transactions, new accounts, internet banking transactions, and card transactions. Banks and financial institutions gain insights from sources such as transaction details, helpline data, log data, emails, social media, external feeds, loan data, sponsorship data, audio recordings, and video recordings.
Data Analytics helps the BFSI organizations to create an enhanced user experience, create omnichannel platforms, and focus on customer acquisition & retention. Also, Data Analytics is helping insurance companies to underwrite risks and unearth fraudulent insurance claims.
BFSI companies use data analytics to analyze an individual’s lifestyle, needs, and preferences. Data analytics enables the banks to personalize their products and offerings. Furthermore, data analytics helps to discover market trends and make informed business decisions. Some of the applications of Data Analytics in BFSI sector include Fraud Detection, Risk Management, Operations Optimization, CRM, and Customer Analytics. Data Analytics includes predictive analytics, forecasting, text mining, data mining, and data optimization. Furthermore, it includes analysis of customer investment patterns, market segmentation, and financial reporting.
Data analytics provides insights into knowing the target customers better. As a result, BFSI firms can enhance their products, improve up-sell and cross-sell offerings, strategize customer retention methodologies, simplify documentation, find out fraudulent insurance claims, and so much more. Furthermore, data analytics helps the organizations to analyze previous and present data and then predict future scenarios. Data analytics combined with interactive visualizations helps to tell a cohesive story from the data insights.
Here are some of the most important use cases of Data Analytics in the BFSI industry:
1. Fraud Detection in BFSI Companies
Financial institutions use advanced algorithms and data analytics solutions to minimize frauds. Data analytics solutions studies the fraud patterns and customer behavior banks to detect possible frauds. Consequently, the banks take preventive action to stop the fraudulent activities.
2. Customer Acquisition and Customer Engagement
Banks and insurance companies find potential customer segments using data analytics. They are reinventing their customer retention strategy by creating new loyalty methods. Analytics solutions provides reports on customer churn patterns which helps the financial institutions to identify the gaps.
Banks use data analytics solutions to know the customer is expecting next. They continuously improvise to meet the customer expectations and gain more new customers. This helps the banks to cross-sell their products, leading to higher profitability and better customer relationships. In the same way, banks use data analytics to strategize their next sales move.
Customer loyalty is always a challenge. While new customers are important, retaining old customers is also equally important. Banks use data analytics solutions to identify customers who are willing to choose another bank and the reason behind. The solution helps the bank to further investigate the customer’s spending patterns to predict the next step.
3. BFSI Customer Targeting
Banks and financial institutions know which product to pitch forward once they know the buying patterns of customers. Analytical dashboards provide them with this necessary insight. They run targeting messaging campaigns by analyzing every customer profile.
Analytics helps banks to gain insights into customer behavior. Consequently, they learn about the trend of customer’s banking patterns. Accordingly, the banks time the messaging to their customers and offer them the correct plans, schemes, offers, and programs.
4. Collections Management in BFSI sector
Collections is another important area for banks and financial institutions. Banks use data analytics to gain control on the collection risk portfolio and determine the risky customers in advance. As a result, banks stay prepared to improve the productivity of their collections and act in the right time.
5. Cash Liquidity Planning
Banks have their branches and ATMs across different locations. Both bank branches and ATMs need to have enough cash liquidity to serve their customer base. Banks use data analytics solutions to track the in-and-out payment patterns. Besides, the solutions provide a dashboard to understand how the banks can distribute their liquidity. Liquid assets planning helps the banks have a better control on their future demand.