Technology

GEN AI Risk Decision in the Modern Era

NY Weekly Staff

December 21, 2023
6:37 pm

By: Suresh Dodda

Credit card companies are spending millions of dollars to identify fraud and prevent it. Leveraging AI can reduce operational costs and improve accuracy, thereby preventing fraud.

The use of artificial intelligence, and machine learning techniques in particular, is very promising in the field of credit scoring and modern portfolio surveillance, where it can help with detailed analysis and interpretation of large datasets.

Connection with firms’ alternative datasets (i.e., digital fingerprints) can help refine the credit risk assessment and generate more accurate and timely signals for credit risk management and investment purposes.

The emergence of generative artificial intelligence (GEN AI) represents a quantum leap in technology, as demonstrated by the exceptional capacities of DALL-E and GPT-4 in decision support and content generation. The impact of these AI models on decision-making processes is growing exponentially as they are integrated more and more into a variety of industries, including healthcare and finance. There are risks associated with this integration, though.

AI GENERIC ARCHITECTURE

AI generic architecture tailored for FOR RISK MANAGEMENT

Here’s a conceptual overview of how AI can be applied to identify credit card risks:

Data Collection:

Gather diverse data, including historical credit card transactions, customer information, spending patterns, and any relevant external data sources (e.g., economic indicators, fraud databases).

Feature Engineering:

Extract meaningful features from the collected data. This may include transaction frequency, transaction amounts, geographical locations, time of day, customer demographics, and other relevant information.

Data Preprocessing:

Clean and preprocess the data to handle missing values, outliers, and inconsistencies. Normalize or scale numerical features and encode categorical variables.

Model Selection:

Choose appropriate machine learning models for credit risk identification. Common models include logistic regression, decision trees, random forests, and more advanced models like gradient-boosting machines or neural networks.

Training the Model:

Train the selected model using historical data, where the model learns to identify patterns associated with both normal and risky credit card transactions. This involves splitting the data into training and validation sets.

Anomaly Detection:

Implement anomaly detection techniques to identify unusual patterns that may indicate fraudulent or high-risk activities. Unsupervised learning algorithms, such as clustering or autoencoders, can be useful for detecting anomalies.

Predictive Analytics:

Utilize predictive analytics to assess the risk level associated with each credit card transaction. The model should provide a risk score or probability, indicating the likelihood of the transaction being fraudulent or high-risk.

Real-time Monitoring:

Implement a real-time monitoring system that continuously evaluates incoming transactions. This allows for immediate detection and response to potential risks, minimizing the impact of fraudulent activities.

Model Evaluation and Iteration:

Assess the performance of the model using metrics such as accuracy, precision, recall, and F1 score. Iterate on the model by incorporating new data and refining features to improve its predictive capabilities.

Integration with Fraud Prevention Systems:

Integrate the AI model with existing fraud prevention systems to enhance overall security measures. This could involve automated alerts, transaction blocking, or other actions based on the identified risk level.