ML
Finance & Banking
Risk Monitoring for a Financial Regulator
Problem
People take out mortgages that impose unnecessary risk on themselves and the financial system as a whole.
Solution
We developed an algorithm that analyzes bank transactions of mortgage holders, and indicates potential risks, reports them to the regulator, together with the expected non-payment timeline (based on the loan equity evaluation).
Results:
The implementation of these models has yielded significant results:
- Improved prediction accuracy of potential customer conversions by 35%, leading to a more focused and cost-effective marketing strategy.
- Enhanced resource allocation efficiency within the sales team, with a 25% increase in lead conversion rates.
- A cumulative 20% uptick in revenue through dynamic pricing and effective upselling strategies.
- More engaging and relevant content production, leading to higher student satisfaction and course completion rates.
- Increased customer retention by 15% and a notable improvement in Lifetime Value through targeted engagement initiatives.
Conclusion:
The success of our machine learning solutions in transforming their business operations demonstrates our commitment to advancing educational technology and supporting our clients in achieving their business goals. We continue to work closely with them to innovate and refine strategies, ensuring sustained growth and success.