Quantum Machine Learning Method for Predicting Loan Defaulters During Economic Uncertainty

Abstract

This invention provides a quantum machine learning method for accurately predicting loan defaulters during periods of economic uncertainty. The method utilizes quantum algorithms and quantum hardware to process large and complex datasets more efficiently than classical machine learning techniques. By leveraging the power of quantum computing, the method can identify patterns and correlations that are difficult or impossible to detect using traditional methods.

Specification

Description:FIELD OF THE INVENTION
The field of the invention is quantum machine learning applied to financial risk management. Specifically, the invention relates to a method for predicting loan defaulters using quantum machine learning techniques. A critical task in financial risk management where institutions aim to identify borrowers who are likely to default on their loans.

BACKGROUND OF THE INVENTION
The prediction of loan defaulters is a critical task for financial institutions. Traditional machine learning methods have been used for this purpose, but their effectiveness can be limited during times of economic uncertainty when data patterns may become more complex and unpredictable, for example, during the period of covid 19, the uncertainty hit the peak and business paralyzed across the world.

SUMMARY OF THE PRESENT INVENTION
The invention comprises the following steps data preparation, Quantum Feature Engineering, Quantum machine learning model and prediction. Data Preparation is a process of collect and preprocess a dataset containing relevant borrower information, such as credit history, income, and financial behavior. Encode the data into a quantum state using a suitable quantum encoding scheme. Quantum Feature Engineering which is to apply quantum algorithms, such as quantum Fourier transform or quantum principal component analysis, to extract meaningful features from the encoded data. Quantum Machine Learning Model is to train a quantum machine learning model, such as a quantum support vector machine or quantum neural network, on the extracted features. Optimize the model's parameters using quantum optimization techniques and Prediction is to use the trained model to predict the likelihood of loan default for new borrowers based on their input data.
Advantages of the Invention:
The quantum machine learning method can achieve higher accuracy in predicting loan defaulters compared to traditional methods, especially during economic uncertainty. Quantum algorithms can process large and complex datasets more efficiently than classical algorithms, reducing computational costs. The use of quantum machine learning for loan default prediction is a novel approach that provides a significant advancement in the field.
DETAILED DESCRIPTION OF THE INVENTION

The invention proposes a novel approach to predicting loan defaults leveraging the power of quantum machine learning. By harnessing the capabilities of quantum computers, this method aims to significantly improve the accuracy and efficiency of loan default prediction, particularly during periods of economic uncertainty.
Technical Description
Data Preparation: Data Collection: A diverse range of financial and demographic data is collected, including credit history, income, employment status, asset ownership, and macroeconomic indicators.
Data Cleaning: The collected data is meticulously cleaned to remove inconsistencies, errors, and missing values. Feature Engineering: Relevant features are identified and engineered to capture the underlying patterns and relationships within the data.
Quantum Data Encoding: Classical to Quantum Mapping: Classical data is encoded into quantum states using techniques such as amplitude encoding or phase encoding. This involves representing the data as the amplitudes or phases of qubits.
Quantum Feature Engineering: Quantum Feature Extraction: Quantum algorithms, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), are employed to extract meaningful features from the quantum data. These algorithms can efficiently identify complex patterns and correlations that may be difficult to detect with classical methods.
Quantum Machine Learning Model: Model Selection: A suitable quantum machine learning model, such as a Quantum Support Vector Machine (QSVM) or a Quantum Neural Network (QNN), is chosen based on the specific characteristics of the data and the desired level of accuracy.
Model Training: The selected model is trained on the quantum data using quantum optimization algorithms, such as Quantum Approximate Optimization Algorithm (QAOA) or Variational Quantum Eigensolver (VQE), to minimize the prediction error.
Prediction: Input Data: New borrower data is encoded into a quantum state.
Model Inference: The trained quantum model processes the input data and generates a probability of default.
Decision Making: Risk Assessment: The predicted probability of default is used to assess the creditworthiness of the borrower. Decision: Based on the risk assessment, a decision is made regarding loan approval or rejection.


BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1, Illustrating the steps involved in the quantum machine learning approach for predicting loan defaulters

Fig 1. Flow Diagram
Brief Description of Drawing
Data Collection and Preprocessing:
Data Collection:
Gather historical loan data, including borrower demographics, credit history, financial
behavior, and economic indicators.
Data Cleaning: Handle missing values, outliers, and inconsistencies in the data.
Feature Engineering: Select relevant features and transform them into a suitable format for quantum processing.
Quantum Data Encoding:
Quantum State Preparation: Encode the classical data into quantum states using quantum encoding techniques like amplitude encoding or phase encoding.
Quantum Feature Engineering:
Quantum Feature Extraction: Apply quantum algorithms like Quantum Fourier Transform (QFT) or Quantum Principal Component Analysis (QPCA) to extract relevant features from the quantum data.
Quantum Machine Learning Model:
Model Selection: Choose a suitable quantum machine learning model, such as a Quantum Support Vector Machine (QSVM) or a Quantum Neural Network (QNN).
Model Training: Train the model on the quantum data using quantum optimization algorithms to minimize the error function.
Model Evaluation:
Performance Metrics: Evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.
Hyperparameter Tuning: Fine-tune the model's hyperparameters to optimize its performance.
Prediction:
Input Data: Input new borrower data into the trained quantum model.
Prediction: The model processes the input data and outputs a probability of default.
Decision Making:
Risk Assessment: Use the predicted probability of default to assess the risk associated with the loan application.
Decision: Make a decision on whether to approve or reject the loan application based on the risk assessment. , Claims:1.A method for predicting loan defaulters, comprising:

Collecting and preprocessing a dataset containing borrower information;
Encoding the dataset into a quantum state;
Applying quantum feature engineering techniques to extract features from the encoded data;
Training a quantum machine learning model on the extracted features; and
Using the trained model to predict the likelihood of loan default.

2.The method of claim 1, wherein the quantum feature engineering techniques include quantum Fourier transform or quantum principal component analysis.

3.The method of claim 1, wherein the quantum machine learning model is a quantum support vector machine or a quantum neural network.

4.The method of claim 1, wherein the quantum machine learning model is trained using quantum optimization techniques.

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