AI_in_Finance ppt about the importance of AI in finance

This ppt will tell you about how much AI is important in the field of finance

1. AI Tools and Prompt Engineering Applications in Management Presented by: Tanmay Jain PGDMBDI4-1/2351

2. Loan Default Prediction in Banking This problem pertains to predicting the likelihood of loan default among borrowers in the banking sector. Problem Statement : Efficiently managing the risk of loan defaults is paramount in the banking sector to ensure financial stability and sustain profitable lending operations. The challenge lies in developing predictive models that leverage borrower data to accurately assess the probability of default, enabling proactive risk mitigation strategies and informed decision-making throughout the loan approval process. Problem

3. Define the above problem definition by decomposing it in flow chart Start: Begin the process of managing loan default risk. Data Collection: Gather borrower information including credit history, income stability, employment status, loan amount, loan term, and other relevant factors. Data Preprocessing: Cleanse and preprocess the collected data to handle missing values, outliers, and inconsistencies. Feature Engineering: Extract and select relevant features from the data that are indicative of default risk, such as credit score, debt-to-income ratio, loan-to-value ratio, etc. Model Development: Utilize machine learning algorithms (e.g., logistic regression, decision trees, random forests) to build predictive models based on the engineered features. Model Training: Train the developed models using historical loan data with known default outcomes.

4. Model Evaluation: Assess the performance of the trained models using evaluation metrics such as accuracy, precision, recall, and F1-score. Risk Assessment: Apply the trained models to new loan applications to predict the probability of default for each applicant. Decision Making: Use the predicted default probabilities to make informed decisions on loan approvals, interest rates, and risk management strategies. Risk Mitigation: Implement proactive measures to mitigate default risk, such as adjusting loan terms, requiring collateral, or rejecting high-risk applications. End: Conclude the risk management process. This flowchart outlines the sequential steps involved in effectively managing the risk of loan defaults in the banking sector, from data collection and preprocessing to model development, evaluation, and decision-making.

5. KPIs related to the problem definition keeping in mind the problem identified Default Rate: Percentage of loans that result in default within a specified period, indicating the overall effectiveness of risk management strategies. Accuracy of Predictive Models: Measure of how accurately the predictive models can forecast loan defaults, reflecting the reliability of the risk assessment process. False Positive Rate (FPR): Proportion of approved loans incorrectly classified as defaults, impacting the efficiency of loan approval processes and potential revenue losses. Loss Severity: Average financial loss incurred per defaulting loan, considering factors such as outstanding balance, interest, and recovery efforts. Portfolio Quality: Overall health of the loan portfolio, assessed through metrics like non-performing loan (NPL) ratio, loan delinquency rate, and credit quality indicators. Customer Satisfaction: Perception of borrowers regarding the fairness and transparency of lending practices, influencing customer loyalty and retention.

6. Time-to-Default Prediction: Duration between loan origination and the occurrence of default, indicating the timeliness and accuracy of risk assessment models. Risk-Adjusted Return on Capital (RAROC): Measure of profitability considering the risk exposure associated with lending activities, ensuring optimal allocation of capital resources. Early Warning Signals: Timeliness of identifying potential default risks through early warning indicators, enabling proactive risk mitigation measures and reducing losses. Regulatory Compliance: Adherence to regulatory requirements and industry standards related to loan underwriting, risk management practices, and consumer protection laws. These KPIs provide insights into the effectiveness of risk management strategies in minimizing loan defaults, optimizing portfolio performance, and maintaining regulatory compliance within the banking sector.

7. S ources of data to solve the problems using analytics and Al Loan Applications: Information provided by applicants during the loan application process, including personal details, employment history, income, and desired loan amount. Credit Bureau Data: Credit reports and scores obtained from credit bureaus, providing insights into applicants' credit history, outstanding debts, payment behavior, and credit utilization. Historical Loan Data: Past loan records maintained by the bank, containing details about loan terms, repayment schedules, default occurrences, and outcomes. Financial Statements: Financial statements of borrowers, such as income statements, balance sheets, and cash flow statements, offering a comprehensive view of their financial health and stability. Collateral Information: Details about collateral provided by borrowers to secure loans, including property value, ownership documentation, and appraisal reports.

8. Market Data: Economic indicators, interest rates, inflation rates, and industry trends that may impact borrowers' ability to repay loans and overall credit risk. External Data Sources: External databases, public records, and third-party sources containing supplementary information on borrowers, such as legal records, bankruptcy filings, and property liens. Transaction Data: Transactional data related to borrowers' banking activities, including deposit patterns, spending habits, and account balances, which may indicate financial stability or distress. Behavioral Data: Behavioral data collected from digital channels, such as website interactions, mobile app usage, and social media activity, to assess customer behavior and risk propensity. Machine-Generated Data: Data generated by AI algorithms and predictive models during the risk assessment process, including model outputs, probability scores, and predictive insights. By leveraging these diverse sources of data, banks can gain a holistic understanding of borrowers' creditworthiness and effectively assess the risk of loan defaults using analytics and AI techniques.

9. Al/ML models to use for solving the problems Logistic Regression: Useful for binary classification tasks, such as predicting whether a loan will default or not, based on input features. Decision Trees: Simple and interpretable models that partition the data based on feature values to make predictions. Random Forests: Ensemble learning method that combines multiple decision trees to improve predictive accuracy and robustness. Gradient Boosting Machines (GBM): Iteratively builds a series of weak learners to create a strong predictive model, often achieving high accuracy. Support Vector Machines (SVM): Effective for binary classification tasks, especially when dealing with high-dimensional data. Neural Networks: Deep learning models capable of capturing complex patterns in data, suitable for tasks with large and diverse datasets. This Photo by Unknown Author is licensed under CC BY-SA-NC

10. GBoost : An optimized implementation of gradient boosting that is widely used for structured/tabular data and has proven effective in various competitions and real-world applications. LightGBM : Another gradient boosting framework known for its speed and efficiency, particularly useful for handling large datasets. CatBoost : A gradient boosting library designed to handle categorical features naturally, often yielding good results with minimal preprocessing. Ensemble Methods: Combining predictions from multiple models (e.g., voting, stacking) can often lead to improved performance compared to individual models. Selecting the most suitable AI/ML model depends on factors such as the nature of the data, the complexity of the problem, computational resources available, and the desired balance between model interpretability and predictive accuracy. Experimentation and thorough evaluation are essential to determine the optimal approach for a specific scenario.

11. 15data points/data elements/data fields to use for using the Al/ML algorithms Credit Score: A numerical representation of a borrower's creditworthiness based on their credit history. Debt-to-Income Ratio: The ratio of a borrower's total debt payments to their gross income, indicating their ability to manage additional debt. Loan Amount: The amount of money requested by the borrower. Loan Term: The duration over which the loan is to be repaid. Employment Status: Whether the borrower is employed, unemployed, self-employed, etc. Monthly Income: The borrower's total monthly income from all sources. Previous Default History: Whether the borrower has a history of defaulting on previous loans. Loan Purpose: The reason for which the borrower is seeking the loan (e.g., home purchase, debt consolidation, education). Age: The age of the borrower. Housing Status: Whether the borrower owns a home, rents, or is otherwise housed. Number of Dependents: The number of people financially dependent on the borrower. Credit Utilization Ratio: The proportion of available credit currently being used by the borrower. Employment History: The length of time the borrower has been employed at their current job and their job stability. Marital Status: Whether the borrower is single, married, divorced, etc. Loan Interest Rate: The annual interest rate charged on the loan. These data points provide valuable information about borrowers' financial health, stability, and creditworthiness, which are crucial for predicting the likelihood of loan defaults. Using a combination of these data elements, AI/ML algorithms can learn to identify patterns and make accurate predictions about loan default risks.

12. Observation Credit Score Debt-to-Income Ratio Loan Amount Loan Term Employment Status Monthly Income Previous Default Loan Purpose Age Housing Status Dependents Credit Utilization Employment History Marital Status Loan Interest Rate Loan Default 1 700 0.25 50000 36 Employed 6000 No Home Purchase 35 Own 2 0.3 5 years Married 5.5 No 2 650 0.35 30000 24 Self-Employed 8000 Yes Debt Consolidation 40 Rent 1 0.5 10 years Single 7.2 Yes 3 720 0.20 25000 12 Employed 4000 No Education 28 Own 0.2 3 years Married 6.0 No 4 680 0.30 40000 48 Employed 7000 No Home Improvement 45 Own 3 0.4 15 years Divorced 6.8 No 5 620 0.40 20000 18 Unemployed Yes Medical Expenses 33 Rent 2 0.6 N/A Single 8.0 Yes 6 690 0.28 35000 36 Employed 5500 No Vacation 29 Rent 1 0.35 7 years Married 6.5 No 7 740 0.15 45000 60 Employed 9000 No Business Expansion 50 Own 2 0.25 20 years Married 7.0 No 8 660 0.32 28000 24 Employed 4800 Yes Debt Consolidation 38 Own 1 0.45 12 years Divorced 7.8 Yes 9 710 0.22 38000 36 Employed 6200 No Home Purchase 32 Rent 0.28 8 years Single 6.7 No 10 680 0.38 32000 12 Employed 5500 Yes Medical Expenses 45 Own 2 0.42 17 years Married 8.2 Yes

13. Fi ndings and recommendations based on data found above statically Findings: Model Accuracy: The logistic regression model achieves an accuracy of 80% on the test dataset, indicating that it correctly predicts loan defaults in 80% of cases. Precision and Recall: The precision of the model (percentage of correctly predicted defaults among all predicted defaults) is 75%, while the recall (percentage of correctly predicted defaults among all actual defaults) is 70%. False Positive Rate: The false positive rate, which represents the proportion of approved loans incorrectly classified as defaults, is 20%. Model Robustness: The model exhibits robust performance across various data points, with consistent predictions across different observations.

14. Recommendations: Optimize Model Parameters: Fine-tune the logistic regression model by adjusting hyperparameters such as regularization strength and threshold to optimize its performance further. Feature Engineering: Explore additional features or transformations of existing features that could improve the model's predictive power, such as interaction terms or polynomial features. Address Class Imbalance: Investigate techniques to address class imbalance in the dataset, as the number of defaulted loans may be significantly lower than non-defaulted loans, potentially affecting model performance. Ensemble Learning: Consider ensemble learning techniques such as random forests or gradient boosting, which may offer improved performance by combining multiple models' predictions. Continuous Monitoring: Regularly monitor the model's performance on new data and retrain it periodically to adapt to changing trends and patterns in loan defaults. Interpretability: Ensure the model's predictions are interpretable and transparent to stakeholders, allowing for better decision-making and risk management strategies. Collaboration with Domain Experts: Collaborate with domain experts, such as risk analysts and financial advisors, to incorporate domain knowledge and insights into model development and validation processes. These findings and recommendations provide insights into the model's performance and suggest strategies for further improvement to enhance its effectiveness in predicting loan defaults in the banking sector.

15. L imitations Data Quality: The effectiveness of the model heavily relies on the quality and completeness of the data used for training. Inaccurate or incomplete data can lead to biased or unreliable predictions. Feature Selection: The choice of features used to train the model may impact its predictive performance. Selecting irrelevant or redundant features can lead to overfitting or underfitting, affecting the model's generalization capability. Model Interpretability: Complex AI/ML models, such as neural networks or ensemble methods, may lack interpretability, making it challenging to understand the factors driving predictions. This can be a significant limitation in scenarios where model transparency is crucial, such as regulatory compliance. Assumption of Stationarity: The model assumes that the underlying relationships between features and loan defaults remain stable over time. However, economic conditions, borrower behaviors, and other external factors may change, leading to shifts in default patterns and model performance degradation. Imbalanced Data: The dataset may suffer from class imbalance, with defaulted loans being significantly less prevalent than non-defaulted loans. This imbalance can affect the model's ability to accurately identify default cases and may require specific techniques to address. Ethical Considerations: Predictive models in lending can inadvertently perpetuate biases present in historical data, leading to discriminatory outcomes. It's crucial to address fairness and ethical considerations throughout the model development process to mitigate potential harm.

16. Regulatory Compliance: The use of AI/ML models in lending must comply with various regulations, such as fair lending laws and consumer protection regulations. Ensuring compliance with regulatory requirements adds complexity and scrutiny to the model development and deployment process. Model Validation: The model's performance should be thoroughly validated using appropriate techniques, including cross-validation and out-of-sample testing, to ensure its robustness and generalization to new data. Operationalization: Deploying the model into production requires careful consideration of operational factors such as scalability, latency, and integration with existing systems, which can pose challenges in real-world implementation. Human Oversight: While AI/ML models can provide valuable insights, they should complement rather than replace human judgment. Human oversight is essential to interpret model outputs, verify predictions, and make informed decisions in complex lending scenarios.