$SSFT Great for Banks.. Prevent Bad Loans Sect
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Posted On: 06/17/2020 9:17:11 AM
$SSFT Great for Banks..
Prevent Bad Loans
Sector: Finance
Company size: Large bank with over 500,000 customers
Performance Area: Non-performing Assets (NPA)
Goal: Reduce the number of loan defaults and focus on lending to prospects that have a strong probability of paying off the loan.
Challenge: Although the bank conducted due diligence and screened all loan applicants, it still found that it had a significant percentage of loans that defaulted. To gain insights and try to prevent non-performing assets, the bank implemented Sonasoft’s AI solution.
Results: Sonasoft ingested data of over 500,000 borrowers across 44 variables to determine the root cause. Sonasoft’s AI Solution then was able to provide a formula with 72 percent accuracy that determined if a loan would be paid or default.
The Sonasoft AI Solution also revealed some interesting insights to the variables that could determine whether a loan would default or not. These discoveries included the type of sub-grade loan, grade, years of employment, debt purpose, etc. that could determine whether the loan would be paid or defaulted.
https://sonasoft.com/prevent-bad-loans/
Prevent Bad Loans
Sector: Finance
Company size: Large bank with over 500,000 customers
Performance Area: Non-performing Assets (NPA)
Goal: Reduce the number of loan defaults and focus on lending to prospects that have a strong probability of paying off the loan.
Challenge: Although the bank conducted due diligence and screened all loan applicants, it still found that it had a significant percentage of loans that defaulted. To gain insights and try to prevent non-performing assets, the bank implemented Sonasoft’s AI solution.
Results: Sonasoft ingested data of over 500,000 borrowers across 44 variables to determine the root cause. Sonasoft’s AI Solution then was able to provide a formula with 72 percent accuracy that determined if a loan would be paid or default.
The Sonasoft AI Solution also revealed some interesting insights to the variables that could determine whether a loan would default or not. These discoveries included the type of sub-grade loan, grade, years of employment, debt purpose, etc. that could determine whether the loan would be paid or defaulted.
https://sonasoft.com/prevent-bad-loans/
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