Advanced analytics is known to be the top most priority of mostly every bank these days. While the banks face many challenges, they also evolve to become smarter with the help of analytics. So the growth of third- party analysis is huge with respect to their business services. Analytics is not a skill of a single person rather it is a team sport in order for any bank to make a right analysis. And these teams is a collection of data scientists, engineers, data Architects and design experts.
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Financial institutions use analytics and risk modeling to evaluate the portfolios and also to predict the occurable losses. With this, the banks can allocate in advances accordingly.
Without relying and increasing the number of human resources, banks make use of analytics to an extent to quickly understand the dimensions of risk.
The expensive compliance burden on Anti-money laundering and KYC departments have eventually reduced by the advancement in the analytical solutions. Advanced analytical solutions of portfolios have not only helped to figure out the quality of pool of assets, it has also helped to figure out advance payments, flow of cash, default of cash and delinquencies. Based on the capital requirements, firms adjust their regulations towards the Loan- to- Value (LTV) ratio.
BENEFITS OF BIG DATA ANALYTICS IN THE BANKING SECTOR
Fraud detection and prevention: These fraudulent activities can be detected and then prevented by Data Analytics, which tries to name and customize trends in the background and then find any difficulties in the smooth flow of normal activities. It detects any strange behavior or irregular transactions and can stop the fraud before it occurs.
Increased Operational Efficiency: Supply of repetitive banking services, but the frequency is high due to the large customer base. Their activities must be extremely effective to succeed. The use of Big Data Analytics aids in improving operational efficiency.
Personalization of services: To take advantage of data analytics-based personalization of services, clients must be segmented based on the findings of many parameters such as spending habits, repayment ability, and frequency, among others. An analysis is carried out to decide which type of service is most suited to which type of customer. Banks can use this platform to directly target potential consumers.
Analytics would be a valuable business discipline when the organizational strengths and the significant strategies are put behind by the banks. Analytics in banking would have greater value generating capacity if the following elements of success are well implemented.
- Data provides insights of business.
- Necessary important decisions are made based on these insights.
- Further improvement of data and analysis are made with the help of business decisions.
CONCLUSION:
Today’s essential question isn’t “why analytics?” but rather “how analytics?” “How to operationalize analytics?” rather than “which analytics?” In the banking industry, there is currently no other value-creation lever as powerful as analytics. However, to fully realize its potential, it must be elevated from a conversation level to a strategic goal.
SAI KEERTHANA M S
I MBA , DSCE
