TimeGPT in banking

On this post:

• TimeGPT Architecture & TimeGPT for Technical Practitioners
• Use Case #1: Personalized Product Recommendation
• Use Case #2: EWI for Loan Repayment Risk
• Use Case #3: EWI for Credit Card Renewal Decline
• The Future of Time Series Analysis in Banking

The modern banking sector is a dynamic environment, constantly shaped by evolving customer expectations, technological advancements, and economic fluctuations. In this landscape, the ability to accurately predict customer behavior is no longer a luxury, but a necessity.

Traditional customer behavior analysis methods, often relying on statistical models and rule-based systems, face significant challenges in capturing the intricacies of modern banking data. Customer behavior is inherently non-linear, influenced by a multitude of factors that interact in complex ways. Traditional models often struggle to capture these non-linear patterns, leading to inaccurate predictions. As transaction volumes and customer interactions increase, traditional methods struggle to process and analyze data in real-time.

Nixtla’s TimeGPT represents a paradigm shift in time series forecasting, offering a powerful and scalable solution for analyzing customer behavior in banking. Unlike traditional models, TimeGPT leverages state-of-the-art techniques to capture complex patterns and generate accurate forecasts. Its ability to handle large datasets and adapt to evolving trends makes it an ideal tool for the banking industry.

TimeGPT goes beyond basic forecasting, offering features like automatic feature engineering, anomaly detection, and probabilistic forecasting, which are crucial for understanding the nuances of customer behavior.

Nixtla TimeGPT

This section provides a comprehensive technical exploration of Nixtla TimeGPT, tailored for architects and data scientists seeking to leverage its advanced capabilities for customer behavior analysis in banking.

TimeGPT Architecture

At its core, TimeGPT leverages the power of Transformer models. Unlike traditional recurrent neural networks (RNNs) that process data sequentially, Transformers utilize attention mechanisms to capture long-range dependencies and parallelize computation. This allows TimeGPT to efficiently model complex temporal patterns and scale to large datasets.

The attention mechanism, a key component of Transformers, allows the model to weigh the importance of different parts of the input sequence when making predictions. A simplified representation of the attention mechanism is:

Where:

• Q: Represents the “search query” for relevant information. In time series, this might be the current time step’s representation.
• K: Represents the “keys” that correspond to each element in the input sequence. These are used to determine how relevant each element is to the query.
• V: Represents the “values” associated with each element in the input sequence. These are the actual information that is retrieved based on the attention weights.
• QK^T (Query-Key Transpose): This step calculates the dot product between the query and the transpose of the keys. The result is a matrix of “attention scores” that indicate the relevance of each key to the query.
• √d_k (Scaling Factor): This factor scales the attention scores to prevent them from becoming too large, which can lead to instability during training. dk is the dimension of the keys.
• softmax(): This function converts the attention scores into probabilities, ensuring that they sum to 1. This allows the model to assign weights to different parts of the input sequence.
• V (Value Matrix Multiplication): Finally, the softmax-weighted attention scores are multiplied by the value matrix to obtain the “attention output.” This output represents the weighted sum of the values, where the weights are determined by the attention scores.

Let’s illustrate this with a simplified example. Imagine we’re forecasting transaction amounts.

We have a sequence of transaction amounts: [100, 150, 200, 180].

We can represent this as a sequence of vectors. For simplicity, let’s assume each transaction amount is a vector of dimension 1.

Let’s create simplified Q, K, and V matrices. In real-world scenarios, these would be learned by the model.

• Assume:
• Q=[1.0] (query for the last element)
• K=[[1.0],[1.5],[2.0],[1.8]] (keys for each element)
• V=[[100],[150],[200],[180]] (values for each element)

Calculate Attention Scores:

• QK^T=[1.0]?[[1.0,1.5,2.0,1.8]]=[[1.0,1.5,2.0,1.8]]
• Let’s assume dk=1 for simplicity. Then, QK^T/√d_k =[[1.0,1.5,2.0,1.8]].

Apply Softmax:

softmax([1.0,1.5,2.0,1.8])≈[0.15,0.23,0.31,0.31] (these values are approximate).

Then Calculate Attention Output:

Attention(Q,K,V)=[0.15,0.23,0.31,0.31]?[[100],[150],[200],[180]]

This will give us a weighted sum of the transaction amounts, where the weights are determined by the attention scores. The result is approximately: [169.3]

In this simplified example, the attention mechanism has given higher weights to the later transactions, as they are more relevant to the query (the last element). This allows the model to focus on the most recent trends when making predictions.

TimeGPT for Technical Practitioners

Effective data ingestion and preprocessing are essential for accurate forecasting. TimeGPT’s API provides flexible tools for handling diverse data formats and performing necessary transformations.

TimeGPT is designed to automatically detect and model complex time series patterns, including seasonality, trends, and anomalies. Its sophisticated architecture allows it to learn and adapt to diverse patterns present in banking data, such as monthly salary deposits, daily transaction fluctuations, and sudden spikes in loan applications. This robustness makes TimeGPT a versatile tool for forecasting various aspects of customer behavior.

Retail banking data presents unique challenges due to its mixed data types and temporal nature. We’ll need to handle current account balance history, transaction data, loan data, and customer interaction logs. Techniques for handling mixed data types include one-hot encoding for categorical variables and normalization for numerical features. Temporal features, such as day of the week and month of the year, can be engineered to capture seasonality and cyclical patterns.

TimeGPT is compatible with standard data structures like Pandas DataFrames, making it easy to integrate with existing data pipelines. Specialized time series libraries can also be used for advanced data manipulation and feature engineering. It is important to have the data in a structure that has a time stamp, and the value that is being predicted.

import pandas as pd

# Example DataFrame for transaction data
data = {
    'timestamp': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04']),
    'transaction_amount': [100.0, 150.0, 200.0, 180.0]
}
df = pd.DataFrame(data)
print(df)        

Banking datasets often contain missing data and outliers, which can significantly impact forecasting accuracy. Imputation techniques, such as mean imputation and interpolation, can be used to fill in missing values. Anomaly detection algorithms can identify and remove outliers before feeding data into TimeGPT. Libraries like scikit-learn provide tools for imputation and anomaly detection

Use Case #1: Personalized Product Recommendation

By leveraging the combined power of transaction history, customer interaction logs, and TimeGPT’s forecasting capabilities, we can build a real-time recommendation engine that anticipates customer needs.

Transaction history provides insights into past purchasing behavior, while customer interaction logs reveal preferences and engagement patterns. By analyzing these data sources, we can identify which products customers are likely to be interested in. For example, customers with frequent savings account transactions might be receptive to investment products, while those with high loan repayment activity could benefit from credit card offers. We can use graph databases, like Neo4j, to efficiently query and analyze these relationships, identifying patterns and correlations that inform our recommendations. Feature engineering can extract meaningful signals, like RFM from transaction data.

TimeGPT’s forecasting capabilities allow us to go beyond static affinity analysis and predict future product needs. By analyzing historical spending patterns and loan repayment behavior, we can anticipate when customers are likely to require specific financial products. This proactive approach enables us to deliver timely and relevant recommendations, maximizing customer engagement and conversion rates.

import pandas as pd
from timegpt import TimeGPT

# Sample transaction data
data = {
    'ds': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05']),
    'y': [100.0, 150.0, 200.0, 180.0, 220.0]
}
df = pd.DataFrame(data)

# Initialize TimeGPT
timegpt = TimeGPT()

# Forecast future spending patterns
forecast = timegpt.forecast(df=df, h=7) # h is the forecast horizon

print(forecast)

# Example for Loan Repayment Behavior

loan_data = {
    'ds': pd.to_datetime(['2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01', '2023-05-01']),
    'y': [1000.0, 950.0, 900.0, 850.0, 800.0] # remaining loan balance
}

loan_df = pd.DataFrame(loan_data)

loan_forecast = timegpt.forecast(df=loan_df, h=5)

print(loan_forecast)        

To improve forecasting accuracy, we can incorporate external factors such as economic indicators (e.g., GDP, interest rates) and market trends. In a cloud environment, we can seamlessly integrate data from external APIs and databases, enriching our forecasting models with valuable contextual information. This allows us to capture the impact of macroeconomic factors on customer behavior, leading to more accurate predictions.

Use Case #2: EWI for Loan Repayment Risk

By leveraging TimeGPT’s forecasting capabilities, we can develop early warning systems that detect anomalous patterns and predict potential risks, enabling proactive intervention strategies.

Detecting early signs of customer disengagement or loan default requires a deep understanding of customer behavior. Analyzing transaction patterns, account activity, and customer interaction logs can reveal subtle changes that indicate potential risks. For instance:

• Customer Disengagement: Reduced transaction frequency, decreased account balances/aum, or a decline in customer support interactions can signal waning interest.
• Loan Default Risk: Missed payments, late fees, or changes in spending habits can indicate financial distress.

We can identify trends and anomalies that precede these events, enabling us to develop predictive models that provide early warnings.

import pandas as pd
from timegpt import TimeGPT

# Sample transaction data with potential anomaly
data = {
    'ds': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05', '2023-01-06']),
    'y': [100.0, 150.0, 200.0, 180.0, 220.0, 10.0]  # Sudden drop on 2023-01-06
}
df = pd.DataFrame(data)

timegpt = TimeGPT()

# Forecast future transaction amounts and detect anomalies
forecast = timegpt.forecast(df=df, h=7)

# timegpt has anomaly detection built into it.
print(forecast)

# Example: Loan Payment Delay
loan_data = {
    'ds': pd.to_datetime(['2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01', '2023-05-01']),
    'y': [1000.0, 950.0, 900.0, 850.0, 800.0] # remaining loan balance
}
loan_df = pd.DataFrame(loan_data)

loan_forecast = timegpt.forecast(df = loan_df, h = 4)

print(loan_forecast)        

To improve the accuracy of our risk assessment models, we can engineer features from customer interaction logs and loan data. For instance:

• Interaction Logs: Sentiment analysis scores, frequency of customer support inquiries, and channel preferences can provide insights into customer satisfaction and engagement.
• Loan Data: Loan-to-value ratios, debt-to-income ratios, and payment history can help assess loan default risk.

Use Case #3: EWI for Credit Card Renewal Decline

This sample code demonstrates how to use Nixtla TimeGPT to develop EWI for declining credit card renewals by forecasting key customer behaviors like transaction count, average transaction amount, and customer support interactions. It analyzes historical data, predicts future trends, and flags potential renewal risks based on predefined thresholds, allowing for timely intervention. By combining time series forecasting with customized risk detection logic, banks can proactively address customer disengagement and mitigate potential revenue loss.

import pandas as pd
from timegpt import TimeGPT

# Sample credit card transaction and activity data
data = {
    'ds': pd.to_datetime(['2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01', '2023-05-01', '2023-06-01', '2023-07-01', '2023-08-01', '2023-09-01', '2023-10-01', '2023-11-01', '2023-12-01']),
    'transaction_count': [10, 12, 15, 13, 11, 9, 8, 7, 6, 5, 4, 3],  # Declining transaction count
    'average_transaction_amount': [50.0, 55.0, 60.0, 62.0, 61.0, 58.0, 55.0, 52.0, 50.0, 48.0, 45.0, 40.0], # Declining average transaction amount
    'customer_support_interactions': [2, 1, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7] # increasing support interactions
}

df = pd.DataFrame(data)

# Initialize TimeGPT
timegpt = TimeGPT()

# Forecast transaction count and average transaction amount
transaction_forecast = timegpt.forecast(df=df[['ds', 'transaction_count']], h=6)
amount_forecast = timegpt.forecast(df=df[['ds', 'average_transaction_amount']], h=6)
support_forecast = timegpt.forecast(df = df[['ds','customer_support_interactions']], h = 6)

print("Transaction Count Forecast:")
print(transaction_forecast)

print("\nAverage Transaction Amount Forecast:")
print(amount_forecast)

print("\nCustomer Support Interaction Forecast:")
print(support_forecast)

# Simple EWI Logic (customize based on your needs)
def detect_renewal_risk(transaction_forecast, amount_forecast, support_forecast, threshold_transaction, threshold_amount, threshold_support):

    risk_flags = []
    for i in range(len(transaction_forecast)):
        transaction_risk = transaction_forecast['transaction_count'].iloc[i] < threshold_transaction
        amount_risk = amount_forecast['average_transaction_amount'].iloc[i] < threshold_amount
        support_risk = support_forecast['customer_support_interactions'].iloc[i] > threshold_support

        if transaction_risk or amount_risk or support_risk:
            risk_flags.append(True)
        else:
            risk_flags.append(False)
    return risk_flags

# Define thresholds for risk detection
threshold_transaction = 5  # Example: If transaction count falls below 5
threshold_amount = 45.0  # Example: If average transaction amount falls below 45
threshold_support = 4 # example: if customer support interactions goes above 4.

# Detect renewal risks
risk_flags = detect_renewal_risk(transaction_forecast, amount_forecast, support_forecast, threshold_transaction, threshold_amount, threshold_support)

print("\nRenewal Risk Flags:")
print(risk_flags)

# Example output with date.
output_df = pd.DataFrame({'ds':transaction_forecast['ds'], 'renewal_risk':risk_flags})
print(output_df)        

This code predicts credit card renewal decline by first preparing a dataset with key indicators like transaction count, average transaction amount, and customer support interactions, which are then forecasted into the future. A detect_renewal_risk function analyzes these predictions against predefined thresholds, calibrated using historical data, to flag potential risks with boolean outputs, providing a date-associated risk assessment. For improved accuracy, consider incorporating additional features like credit card usage frequency, payment history, customer demographics, and recent behavioral changes.

The Future of Time Series Analysis in Banking

As we’ve explored the power of Nixtla TimeGPT in various banking use cases, it’s clear that advanced time series analysis is poised to redefine how financial institutions understand and interact with their customers. By leveraging sophisticated forecasting techniques, banks can gain unprecedented insights into customer behavior, optimize product offerings, and mitigate risks with remarkable precision.

Throughout our exploration, it’s become clear that maximizing the potential of Nixtla TimeGPT in banking hinges on foundational best practices: prioritizing data quality, meticulous feature engineering, integrating contextual information, rigorous model evaluation through backtesting, leveraging cloud scalability, and utilizing TimeGPT’s built-in anomaly detection.

Looking ahead, the evolution of time series analysis in banking will be shaped by emerging trends like Explainable AI, which fosters transparency, federated learning, which safeguards privacy, real-time forecasting for dynamic decision-making, multimodal time series analysis for richer insights, and AutoML for streamlined model development.

Nixtla TimeGPT stands at the forefront of this transformation, offering a powerful tool for building personalized recommendation systems, developing early warning indicators, forecasting financial behaviors, and detecting anomalies.