How Machine Learning Systems Accelerate Fraud Scams in Fintech by Chandra Gundlapalli

I was Managing Director, Data at Charles Schwab and 2/3 of my $400M Global Vice President P&L came from Financial Services at Unisys. I learned a lot about FinTech modernization opportunities and currently, I am building Fraud Detection/Revenue Assurance (part of AML - Anti Money Laundering) SaaS capability on AWS cloud with my global team of data scientists, and machine learning engineers. Let's understand step by step,

Context:

Fraud is a persistent problem in the world of finance, with scams and schemes constantly evolving to avoid detection. Fintech companies, in particular, have become a popular target for fraudsters due to their digital nature and the speed of transactions they process. However, machine learning systems have emerged as a powerful tool in the fight against fraud, allowing fintech companies to identify and prevent fraudulent activity before it causes damage. In this blog, we will explore how machine learning systems help reveal scams in fintech and the benefits they offer in combating fraud.

[FinTech market projection & CAGR here]

What is Machine Learning?

Before delving into the role of machine learning in fraud detection, it's important to understand what machine learning is. Machine learning is a subset of artificial intelligence that enables computer systems to learn and improve from data without being explicitly programmed. This allows machines to identify patterns and make predictions based on those patterns. The more data the machine receives, the more accurate its predictions become.

How does machine learning help reveal scams in fintech?

Fraudsters are constantly developing new tactics to evade detection, making it difficult for traditional rule-based systems to keep up. Machine learning systems, however, can analyze large amounts of data and identify patterns that may not be immediately apparent to human analysts. These systems can detect anomalies and flag suspicious transactions, which can then be investigated by fraud analysts.

One of the primary ways machine learning systems help reveal scams in fintech is by analyzing historical data to identify patterns (hidden and implicit correlations of data) of fraudulent activity. By doing this, the system can learn what normal behavior looks like and detect any deviation from that norm. For example, if a customer suddenly starts making a large number of transactions in a short period of time, this may be flagged as suspicious activity (SAR - suspicious activity report per the Banking Act TBD).

Another way machine learning systems help reveal scams in fintech is through the use of predictive analytics. These systems can analyze customer behavior and transaction history to identify potential fraudulent activity before it happens. For example, if a customer suddenly starts making transactions in a location they have never visited before, this may be flagged as potential fraud. The system can then send an alert in real time to the customer or freeze the account until the activity can be verified.

Benefits of using machine learning in fraud detection

The benefits of using machine learning systems in fraud detection are numerous. First and foremost, these systems are highly accurate and can analyze large amounts of data much faster than a human analyst. This means that fraudulent activity can be detected and prevented in real time, reducing the risk of financial loss for the fintech company and its customers.

Additionally, machine learning systems can adapt and learn over time, becoming more accurate as they analyze more data. This means that they can stay ahead of new and evolving fraud schemes, providing a more robust defense against fraudulent activity.

Finally, machine learning systems are cost-effective. By automating the fraud detection process, fintech companies can reduce the number of human analysts needed to monitor transactions, saving time and money in the process.

Typical machine learning algorithms used for Fraud Detection in Fintech?

• Logistic Regression: A popular algorithm for classification tasks, such as predicting whether a transaction is fraudulent or not. It works by modeling the relationship between the input variables and the output class.

• Decision Trees: A tree-like model that is used to represent decisions and their possible consequences. It is often used for fraud detection as it can easily detect anomalies in data.

• Random Forest: A decision tree-based algorithm that uses an ensemble of decision trees to classify transactions. It is useful for handling large datasets with many features.

• Support Vector Machines (SVM): A powerful algorithm for classification that works by finding the optimal decision boundary that separates the classes. It is often used for credit card fraud detection.

• Neural Networks: A highly flexible algorithm that can be used for both classification and regression tasks. It is often used for fraud detection as it can learn complex patterns and relationships in data.

• K-Nearest Neighbors (KNN): A non-parametric algorithm that uses the distance between data points to classify transactions. It is useful for detecting fraud patterns that are not easily modeled by other algorithms.

• Gradient Boosting: A machine learning technique that combines several weak learners to create a strong learner. It is often used for fraud detection as it can handle highly imbalanced datasets.

It's very critical to note that the effectiveness of the algorithm depends on the quality of the data used to train it.

Conceptual Architecture On AWS

[TBD]

Flow:

• Data Collection: Collect transaction data from various sources, such as credit card transactions, bank transfers, and online payments. The data should include information such as transaction amount, time, location, and other relevant details.

• Data Preprocessing: Perform data cleaning, data normalization, and data transformation on the collected data. For example, you may remove any incomplete or erroneous data, convert timestamps to a standardized format, and categorize transaction locations by geographic region.

• Feature Engineering: Create new features that can aid in fraud detection. For example, you may calculate the average transaction amount for each customer, calculate the number of transactions made by each customer per day, and calculate the distance between the location of the current transaction and the customer's home location.

• Model Selection and Training: Select a suitable machine learning model for the fraud detection task, such as a Random Forest or Gradient Boosting model. Split the dataset into training and testing sets and train the selected model on the training data.

• Model Evaluation: Evaluate the performance of the model using metrics such as precision, recall, and F1-score on the testing data. If the performance is not satisfactory, consider adjusting the model hyperparameters or selecting a different model.

• Real-Time Scoring: Once the model is trained and optimized, deploy it to production for real-time scoring. Every new transaction that comes in can be scored with the model to determine the likelihood of fraud.

• Alert Generation: Set a threshold for the fraud probability score, and if a transaction score is above the threshold, generate an alert for further investigation by fraud analysts.

• Continuous Improvement: Monitor the performance of the algorithm over time and update the model as new fraudulent patterns emerge.

Sample code:

# Load and preprocess data

data = pd.read_csv("transaction_data.csv")

data = data.dropna() # remove incomplete data

data['timestamp'] = pd.to_datetime(data['timestamp']) # convert timestamp to datetime

data['hour_of_day'] = data['timestamp'].dt.hour # extract hour of day

data['transaction_amount_log'] = np.log(data['transaction_amount']) # log-transform transaction amount

# Feature engineering

data['avg_transaction_amount'] = data.groupby('customer_id')['transaction_amount'].transform('mean')

data['num_transactions_per_day'] = data.groupby(['customer_id', data['timestamp'].dt.date])['transaction_id'].transform('count')

data['distance_from_home'] = haversine_distances(data[['customer_lat', 'customer_long']].values, data[['merchant_lat', 'merchant_long']].values)

# Model training

X = data[['hour_of_day', 'transaction_amount_log', 'avg_transaction_amount', 'num_transactions_per_day', 'distance_from_home']]

y = data['is_fraud']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)

model.fit(X_train, y_train)

# Model evaluation

y_pred = model.predict(X_test)

precision = precision_score(y_test, y_pred)

recall = recall_score(y_test, y_pred)

f1 = f1_score(y_test, y_pred)

Selecting the best model based on the accuracy

• Model 1 Accuracy TBD

• Model 2 Accuracy TBD

• Model 3 Accuracy TBD

• Model 4 Accuracy TBD

• Model 5 Accuracy TBD

Here is the 30-second takeaway:

• Data quality challenges -unsupervised learning puts data into clusters making data labeling more precise

• Where to find sample Financial Services

• Address model drifting ()

• Address bias during data collection, feature selection, and model training

• Explainable AI for regulatory compliance

PS: I am refining the article, please check back