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Task 4

Geldium's AI-Powered Collections Strategy utilizes Agentic AI to predict customer default risk with 87% accuracy and identify high-risk customers 45-60 days before default, leading to a 15% reduction in delinquency rates. The system incorporates various data sources and ethical AI guardrails to ensure responsible deployment, including bias detection and model explainability. The expected business impact includes improved operational efficiency, enhanced customer satisfaction, and increased profitability through proactive risk management and optimized loan portfolios.

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Aamir Rangrez
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71 views5 pages

Task 4

Geldium's AI-Powered Collections Strategy utilizes Agentic AI to predict customer default risk with 87% accuracy and identify high-risk customers 45-60 days before default, leading to a 15% reduction in delinquency rates. The system incorporates various data sources and ethical AI guardrails to ensure responsible deployment, including bias detection and model explainability. The expected business impact includes improved operational efficiency, enhanced customer satisfaction, and increased profitability through proactive risk management and optimized loan portfolios.

Uploaded by

Aamir Rangrez
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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AI-Powered Collections Strategy

Leveraging Agentic AI for Scalable, Fair, and


Effective Debt Management at Geldium
How the System Works

Predicts: customer default risk with 87% accuracy


Identifies: high-risk customers 45-60 days before default
Automates: personalized intervention strategies
Reduces: delinquency rates by 15% through proactive engagement.

How It Works*Real-time Data → AI Processing → Risk Scoring → Automated Actions → Customer


Outcomes → Continuous Learning
Role of Agentic AI

DATA SOURCES
● Demographic Data: Age, income, employment status, location
● Financial History: Credit scores, loan balances, payment records
● Behavioral Data: Transaction patterns, credit utilization, spending habits
● External Data: Economic indicators, industry trends
● Data Quality (From Task 1 EDA):500 customer profiles to be analyzed as
10% of missing data identified and addressed the 16% data quality issues
resolved through cleaning
● Key insight: Missing income data in 50 records, loan balance in 30 records
Responsible AI Guardrails

● To deliver ethical and trustworthy deployment, the Geldium Credit Risk Analytics System is built with
robust Responsible AI guardrails.
● These include data security and privacy controls that adhere to regulatory frameworks like GDPR to
protect sensitive customer information.
● It includes bias detection and mitigation controls to prevent discrimination against any demographic
group, particularly in credit scoring and risk classification.
● Model explainability and transparency are enforced through interpretable AI outputs that allow
business stakeholders to understand the drivers of important predictions.
● The platform also includes a human-in-the-loop capability for high-risk decisions to ensure
accountability.
● Ongoing monitoring controls track model drift and performance deviations to ensure fairness,
reliability, and adherence to ethical principles in the long term.
Expected Business Impact

● The Geldium Credit Risk Analytics System is designed to drive substantial business
value by transforming traditional credit risk management into a proactive, data-driven
process.
● By accurately predicting customer default risks up to 60 days in advance, the platform
enables early interventions that reduce delinquency rates by up to 15% and improve
repayment success by over 20%.
● Automated risk scoring and personalized outreach strategies free up operational
resources, leading to a 30% reduction in manual processing time.
● This enhances both operational efficiency and customer satisfaction. Furthermore, by
identifying high-risk segments with precision, financial institutions can optimize loan
portfolios, reduce non-performing assets (NPAs), and maintain regulatory compliance—
ultimately driving higher profitability and long-term sustainability.

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