Supporting multinational banks with their behaviouralisation models for interest rate risk calculations, exploring the sensitivity of customer behaviour to changes in interest rate and other macroeconomic variables.
Market Data Mining
In order to build the most relevant behavioural models for individual clients, powered by Nebuli’s BehaviorLink framework, Nebuli collects data on customer behaviour under different interest rate scenarios. This includes such data as deposits and loan balances, as well as data on customer transactions and account activity.
The system also collects data on macroeconomic variables such as GDP, inflation, and employment rates, as well as data on market conditions such as the yield curve and the price of financial instruments.
Nebuli’s dedicated behaviouralisation framework for financial institutions involves the following key services:
Supervised learning algorithms, such as linear regression or decision trees, are applied to predict changes in interest rates based on input data.
Unsupervised learning algorithms, such as clustering or anomaly detection, are applied to identify patterns or anomalies in the data.
Natural language processing (NLP) techniques, and building dedicated large language models, are applied to process and analyse text data, such as customer feedback or news articles.
Mining a wide range of data sources to train and enhance the bank’s existing machine learning models, including customer behaviour, macroeconomic, and market data.
Data preprocessing and cleaning techniques to prepare the data for analysis.
Building custom data visualisation and augmented analytics tools.
Constructing a robust data governance and security plan should be put in place to ensure the integrity and confidentiality of the data.
Nebuli’s behaviouralisation models allow financial institutions to dive deep into their customers’ behavioural patterns following changes in interest rates. For example, the model details how increases in interest rates tend to lead to a decrease in loan balances and an increase in deposit balances, as customers are more likely to save and pay off debt when rates are higher.
The model can also demonstrate how changes in macroeconomic variables such as GDP and employment rates could significantly impact customer behaviour, with stronger economic conditions generally leading to more lending and borrowing activity.
By incorporating detailed customer behavioural analysis into risk calculations, financial institutions can more accurately predict how changes in interest rates and other macroeconomic variables will impact their business outcomes, service models or policies. This allows our clients and partners to make more informed decisions about their risk management practices, ultimately leading to improved financial performance.