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Fremont, CA: Data is abundant in the banking industry. There is an abundance of data in these sectors, whether it is used or unused. Most banks are under pressure to remain profitable while also understanding their customers' wants, needs, and preferences. Many financial institutions have recently adopted new models to help them compete. Banks must go beyond standard business reporting and sales forecasting in order to identify a set of critical success factors.
Significant applications for banking analytics:
Fraud detection is a critical activity that must be present in order to prevent any fraudulent activities, whether by employers or customers.
Because banking is a highly regulated industry, banks must adhere to a plethora of external compliance requirements in order to combat fraudulent and criminal activity.
Loan Amount Prediction/Classification
Banks can automate the loan eligibility process based on customer information (real-time). Gender, Marital Status, Education, Income, Number of Dependents, Loan Amount, Credit History, and other information can be obtained through online applications. Building a high-accuracy predictive model will aid in automating this process for those who are eligible for loan amounts, allowing them to target these customers specifically.
In the current environment, it is said that it is more profitable to retain a customer than to acquire a new one. Banks are constantly at risk of losing customers or members, and in order to stem the tide, they may offer to waive annual fees, better rates, and prioritize treatments to their best customers.
Such retention strategies, nevertheless, come at a cost, and banks cannot afford to make such offers to every single customer. The success and feasibility of such strategies are dependent on determining the appropriate action for the appropriate customer.
The banking industry, more than any other, is expected to face the greatest risk analytics investments, with 73 percent of banking respondents anticipating a 10 percent increase in spending.
Its spending is expected to increase in the most specific areas, such as system integration, data quality and sourcing, and modeling.
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