In today’s data-driven world, data analytics play a crucial role in informed decision making to drive organizations forward, improve efficiency, increase returns, and in turn achieve business goals. For the uninitiated, data analytics is the process of discovery, interpretation, and conveying meaningful insights from the data to help in the decision-making process.
In the Banking and Financial Services sector, through data analytics, institutions can monitor and assess large amounts of customer data and create personalized/customized products and services specific to individual consumers.
For example, when a customer buys a vehicle, the bank sends promotional offers of insurance to cover the customer’s vehicle. In the future, such applications could be expanded even further. One way this could happen is if a customer got a large bill, the bank could offer an EMI conversion or a loan to cover the cost.
Most financial institutions still use data like they did two decades ago. They build “rearview mirror” reports that their leaders and managers rely on to make decisions about the future. Financial marketing teams pretty much follow suit, even for their vaunted “next likely product.”
The disconnect arises not because institutions cannot identify what is possible. They have no clue where to start. Typically, each institution’s IT organization has built a data warehouse or a data lake. This improves access to the data and eases development of reports. However, institutions lack resources to transform data into actionable insights, enabling creation of better products and customer experiences.
Often, institutions fail to properly define a business case. This stymies gaining approval to hire appropriate resources. Sometimes, a vendor promises a great solution. But then management hesitates to release customer data when the vendor will store it off-premises where the institution has no control over it. And some institutions work with vendors able to solve some of their problems but unable to answer any request beyond the features already provided.
So, back to square one: The financial institution remains stuck with the problem of lots of data and no new uses for it. But not all banks and credit unions have stalled. Some have found technology partners, often fintech firms, that can help them leverage data and find creative solutions.
Some of the areas where banking and financial institutions are increasingly using data analytics include:
- Fraud detection
- Managing customer data
- Risk modelling for investment banks
- Personalized marketing
- Lifetime value prediction
- Customer segmentation
- Customer spending patterns
- Transaction channel identification
- Customer feedback analysis and application
The importance of data analytics in the banking and financial services sector has been realized at a greater scale and most of the established banks have already started reaping the benefits.
To gain competitive advantage, banks should recognize the importance of data science, incorporate it in their decision-making process, and develop strategies based on the actionable insights from their customers data. Start with small, doable steps to integrate data analytics into operating models and stay ahead of competition.
COMMON PROBLEMS THAT CAN BE AVOIDED BY DATA ANALYTICS
- Overdue loans
Overdue loans of cooperative banks are increasing yearly, restricting the recycling of funds which in turn affects the lending and borrowing capacity of the bank.
- Professional management and technological advancement
Cooperative banks are often reluctant to adopt new technologies like computerised data management. Professional management in the banks is often missing due to lack of training of personnel because of lack of funds.
- Risk Management/Fraud Detection
With the financial crisis of 2009, risk profiling and fraud detection became top priorities. Expanding the use of alternative channel insight and increasing the velocity of data capture, the use of data beyond the institution’s firewalls provides an enhanced snapshot of household finances and spending behaviours.
The ability to better understand consumers, seamlessly matching “right-time” offers to a customer’s or prospect’s needs, allows a financial institution to optimize the management of profitable, long-term customer relationships. The addition of a vast amount of relatively unstructured online insight provides an enhanced view to this end, potentially improving both effectiveness and efficiency of marketing efforts.
COLLECTING METHODS AND PSYCHOLOGY
“Are there certain days of the week that you feel are more productive than others in terms of collecting installments or savings?”
The results showed that, overall, most respondents see their collection efforts are most effective on Tuesdays. Following closely behind, collectors also see Wednesday and Thursday as an effective day to reach out to their customers and receive payment. Mondays and Fridays tend to be touch and go for customers.
And on a monthly basis, people like to pay at the beginning of the month. Around 1st week - 2nd week that's the exact time that customers are willing to pay. Through making these collection arrangements, we can produce the exact revenue as possible at exact time.
The truth is that financial institutions are struggling to profit from ever-increasing volumes of data. Banks are only using a small portion of this data to generate insights that enhance the customer experience. For instance, research reveals that less than half of banks analyze customers’ external data, such as social media activities and online behavior. And only 29% analyze customers’ share of wallet, one of the key measures of a bank’s relationship with its customers. Only 37% of banks have hands-on experience with live big data implementations, while the majority of banks are still focusing on pilots and experiments.
Customer data analytics has been a relatively low priority area for banks. Most have concentrated their energy on risk management, not using analytics to enhance the customer experience. But the research shows that banks applying analytics to customer data have a four-percentage point lead in market share over banks that do not. The difference in banks that use analytics to understand customer attrition is even starker at 12-percentage points.
Cooperative banks play an integral part in the implementation of development plans and are important for the effective functioning of the banking system in India. India is termed as an under banked country, and after so many scams, it is need of the hour to take necessary measures like DATA ANALYTICS and to boost the confidence and trust of the public in the banking system.