Financial risk management is the practice of protecting economic value in a firm by using financial instruments to manage exposure to risk: operational risk, credit risk and market risk, foreign exchange risk, shape risk, volatility risk, liquidity risk, inflation risk, business risk, legal risk, reputational risk, sector risk etc. In this repo, we aim to target each risk using deep reinforcement learning algorithms.
cd gym-fraud
pip install -e .
Step - 1 : Create a directory named dataset in your folder containing the main program.
Step - 2 : Download Kaggle's Credit Card Fraud Detection Dataset and place it inside dataset folder
Step - 3 : In your code create an instance of gym_fraud environment using the following commands
import gym
import gym_fraud
env = gym.make('fraud-v0')Due to the rapid advancement in electronic commerce technology, the use of credit cards has dramatically increased. The increasing popularity of credit card as a payment mode for both online and regular purchases has led to a rise in fraudulent cases of credit card transactions. For many years, numerous supervised machine learning models for anomaly detection have achieved state-of-the-art performance. In this paper, we present a novel deep Q-network architecture and a custom OpenAI Gym environment for our deep reinforcement learning agent that utilizes Experience Replay and uses value function approximation. The deep Q-agent employs epsilon-greedy policy to perform classification action based on batches of input. The OpenAI environment then evaluates the agent’s action and rewards the agent accordingly. The agent’s memory stores this entire experience. At the end of batch completion, the deep Q-agent samples a batch of memory from its experience buffer and updates the Q-value using the Q-network computes the loss and performs back-propagation to update the weights. Results show that our model successfully classified fraudulent and non-fraudulent transactions and has achieved state-of-the-art performance.
Table 1 represents the accuracy score of various models that have been pro- posed for classifying fraudulent and non-fraudulent transactions. After ex- tensive training, our model was able to correctly classify fraudulent and non- fraudulent transaction with 90.29% accuracy on test data.
A comparison of classification accuracy of multiple models discussed in section 4 against our DQN-based model is elaborated in Table 1.
According to the results, artificial neural networks perform the best when given a classification problem such as credit-card fraud detection with 99% accuracy. Random forest model and Logistic Regression algorithm looks promising for our dataset. They have high true positive rate and low false positive rate. Our model has achieved state-of-the-art performance on a highly imbal- ance credit-card fraud data-set and was able to correctly classify fraudulent and non-fraudulent transactions 90.29% of the time. This accuracy opens the door to many opportunities of exploring the scope of reinforcement learning in the field of classification problems and decision making process. By exploring better reward functions and performing hyper-parameter tuning, we can increase the accuracy of our model even more.
Under review.