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Elevating Recovery Rate (RR) and Reducing Loan Default using AI-Driven Predictive Analytics For A Leading Financial Lender

Elevating Recovery Rate (RR) and Reducing Loan Default using AI-Driven Predictive Analytics For A Leading Financial Lender

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    Synopsis

    Technology Stack

    Microsoft ASP.Net

    Python

    SQL Server

    Oracle DB

    Oracle

    Gupshup

    CRM

    SSIS

    Client Overview

    The Client is one of the leading Financial Services provider in India.

    Challenge

    The client needed accurate predictive analysis to forecast customer-wise monthly EMI payments for active agreements, considering at least 6 months of data. They also sought to predict the repayment of previously bounced EMIs to mitigate financial risks and optimize cash flow management.

    Solution

    • Utilized machine learning algorithms such as Random Forest and Neural Network to develop a predictive analytical model.
    • Gathered historical data encompassing customer details, past collection information, and current demand status.
    • Leveraged the predictive model to forecast the repayment behavior of customers for the next month.
    • Determined the likelihood of customers making payments or defaulting based on the analysis of the collected data.
    • Enabled the NBFC to make informed decisions and optimize their collection efforts by focusing on customers likely to make payments.
    • Improved cash flow management and reduced financial risks by identifying customers who may potentially default on their EMIs.

    Outcome

    Improved collection efficiency by assigning high-risk agreements to productive agents.
    Proactive identification of customers likely to bounce/default, aiding in risk management.
    Recommended contact/visit dates increase the likelihood of timely payments.
    Optimal resource allocation and cost savings.

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