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DA Risk Cases - Coggle Diagram

- - - - Monitoring customer complaints, improving customer service, and implementing ethical business practices.
    - - Mitigated by implementing strong operational controls, having backup systems in place, and conducting regular staff training.
    - - Conducting regular compliance audits, reviewing policies and procedures, and providing training to employees.
    - - Underwriting standards, reviewing credit scores, and monitoring delinquent accounts.
    - - Regularly monitoring market conditions
    - - Monitoring Collateral Risk
  - - - Customer credit history
        
        Analyze historical loan data
        
        Evaluate borrower creditworthiness
        
        Consider loan-to-value (LTV) ratios
        
        Evaluate macroeconomic factors
        
        Create data visualizations
      - Payment trends
      - Overall market conditions
  - - - Identify Theft
        
        Assumption 1: Identity theft is a common external fraud scenario in auto financing, which can result in the first month default of the loan.
        Data Analytics: Utilize identity verification tools to check the authenticity of the customer's identity and use fraud detection algorithms to identify any inconsistencies or anomalies in their loan application. Additionally, monitor for changes in personal information and keep track of suspicious behavior or patterns associated with the applicant's identity.
        
        Identity theft: Utilize a scatter plot to visualize the relationship between the applicant's information and the fraud risk score, which is generated by a fraud detection algorithm. Use color-coding to differentiate between legitimate and fraudulent applications, and plot any suspicious data points that fall outside the normal range. Additionally, use a heat map to identify the areas or regions where fraud risk is highest, based on the location of the applicant's address and other factors.
      - Dealer fraud
        
        Assumption 2: Dealer fraud is a significant external fraud scenario in auto financing, where a fraudulent car dealer may inflate the price of the car, add extra fees to the loan, or misrepresent the terms of the loan to the customer.
        Data Analytics: Utilize statistical analysis and data mining techniques to identify unusual pricing trends and fees across dealerships and flag any outliers. Additionally, utilize machine learning algorithms to monitor for suspicious behavior, including frequent changes to pricing or fees, and detect patterns of misrepresentation.
        
        Dealer fraud: Utilize a box plot or histogram to visualize the distribution of car prices and fees across different dealerships. Use color-coding to differentiate between legitimate and fraudulent dealerships, and plot any outliers that fall outside the normal range. Additionally, use a scatter plot to visualize the relationship between the car price and other variables, such as the location of the dealership, the make and model of the car, and the number of sales made by the dealership.
      - Stolen vehicles
        
        Assumption 3: Stolen vehicles can be sold to unsuspecting customers who apply for an auto financing loan, resulting in the first month default of the loan.
        Data Analytics: Utilize data analysis and visualization techniques to monitor for unusual patterns in vehicle sales, such as a high number of sales of a specific model or make, which could indicate fraudulent activity. Additionally, utilize vehicle identification number (VIN) tracking tools to track the ownership history of the vehicle and verify its authenticity.
        
        Stolen vehicles: Utilize a map to visualize the location of the vehicle sales, and use color-coding to differentiate between legitimate and suspicious sales. Plot any clusters of sales that occur in unusual locations, or any sales that occur in close proximity to a known stolen vehicle report. Additionally, use a heat map to identify the areas or regions where stolen vehicle sales are most common, based on the location of the dealership and other factors.
    - - Assumption 1: Inadequate credit checks is a common internal control ineffectiveness scenario in auto financing, which can result in the first month default of the loan.
        Data Analytics: Utilize credit risk analysis tools to evaluate the borrower's creditworthiness, including their credit score, payment history, and other relevant financial information. Additionally, use data visualization tools to track the performance of loans made to customers with low credit scores and compare that data to the overall portfolio performance.
        
        Inadequate credit checks: Utilize a scatter plot to visualize the relationship between the borrower's credit score and other relevant financial information, such as their debt-to-income ratio and payment history. Use color-coding to differentiate between loans that have defaulted in the first month and those that have not, and plot any suspicious data points that fall outside the normal range. Additionally, use a heat map to identify the areas or regions where loans with lower credit scores are more common and compare that data to the overall portfolio performance.
      - Assumption 2: Insufficient verification of income is a significant internal control ineffectiveness scenario in auto financing, where the lender may not verify the borrower's income adequately, or the verification process may be inadequate, resulting in default in the first month.
        Data Analytics: Utilize income verification tools to confirm the borrower's income, including tax returns, pay stubs, and other relevant financial documents. Additionally, utilize machine learning algorithms to analyze income data and identify patterns of fraudulent behavior or inconsistencies.
        
        Insufficient verification of income: Utilize a box plot or histogram to visualize the distribution of borrower income across different loan cohorts. Use color-coding to differentiate between loans that have defaulted in the first month and those that have not, and plot any outliers that fall outside the normal range. Additionally, use a scatter plot to visualize the relationship between the borrower's income and other variables, such as their credit score, employment history, and location, and identify any patterns of fraudulent behavior or inconsistencies.
      - Assumption 3: Weak underwriting standards is an internal control ineffectiveness scenario in auto financing, where loans may be approved without adequate due diligence, resulting in loans being approved to borrowers who are not creditworthy or cannot afford to repay the loan, resulting in a default in the first month.
        Data Analytics: Utilize underwriting analysis tools to evaluate the strength of underwriting standards, including credit policies, due diligence procedures, and risk assessment methods. Additionally, utilize data visualization tools to track the performance of loans made under weaker underwriting standards and compare that data to the overall portfolio performance.
        
        Weak underwriting standards: Utilize a bubble chart or radar chart to visualize the strength of underwriting standards across different loan cohorts. Use color-coding to differentiate between loans that have defaulted in the first month and those that have not, and plot any suspicious data points that fall outside the normal range. Additionally, use a heat map to identify the areas or regions where loans with weaker underwriting standards are more common and compare that data to the overall portfolio performance.