Building Our Predictive AI Model for Commercial Credit Risk

Building predictive AI models for commercial credit involves a few intricate steps, some advanced technical understanding, and an eye for innovation. In the last post, we shared some insights into how Crediture is leveraging generative AI to evaluate commercial credit risk. In this post, we share the development of our predictive AI models for commercial credit, detailing the steps involved and showcasing how we are leveraging this technology.

Predictive AI Primer

Before diving deeper, let's take a moment to understand what predictive AI really is. At its core, Predictive AI is about forecasting future outcomes based on past data. It involves using a variety of machine learning and statistical techniques to analyze historical data, identify patterns and trends, and build AI models that can predict future occurrences. These predictions are then used by our customers to make more informed decisions, reducing risks and maximizing opportunities. The power of predictive AI lies not only in its ability to make accurate predictions, but also in its capability to continuously improve as more data becomes available. Building our predictive AI model involved many steps from data collection and feature engineering to validation and deployment.

Collecting and Tagging Macroeconomic Data Sources

We started by gathering a wide variety of macroeconomic data sources relevant to the commercial credit industry. This included data points like Gross Domestic Product (GDP), inflation rates, unemployment rates, and a lot of industry-specific data. This macroeconomic data helps our AI model understand larger economic trends that can impact a business's financial health and creditworthiness.

Each data source was tagged with relevant identifiers for easier processing and analysis later on. This step was crucial for the data preprocessing stage.

Data Preprocessing and Feature Engineering

After the data was collected and tagged, the next step was preprocessing. This involved cleaning the data by removing outliers and handling missing values. The data was also normalized so that values in numerical columns were set to a common scale, without distorting the differences in the value ranges or losing any information. The preprocessed data was then converted into a format suitable for feeding into the AI model.

With massive amounts of macroeconomic data successfully preprocessed, we were ready for feature engineering. This transforms the raw data into meaningful inputs (or 'features') for the model. In some cases, this involved creating new composite features, such as 'business performance index' or 'market volatility score,' calculated from several other data points.

Building the Predictive AI Model

The AI model-building phase involved selecting an appropriate machine learning algorithm and training it on the processed data. The most common algorithm choices include Decision Trees, Logistic Regression, Random Forests, and Neural Networks. The choice of algorithm often depends on the nature and complexity of the data. Early on at Crediture, we explored a few different software solutions to help us identify the most appropriate algorithm to apply.

After choosing an algorithm, our AI models were trained by feeding it the features (input data) and the corresponding outcomes (whether a business was creditworthy or not). The AI model learned the patterns and relationships in the data. With enough business data processed, our AI models were making predictions based on new, unseen data.

Testing and Validation

With our AI model trained and making credit predictions, we began testing on a validation dataset that the AI model hadn't seen during training. This would help assess how well the model adapts to new data. We were mostly looking for accuracy and had to keep iterating the whole process until the model was producing consistently reliable results.

Application to Real-World Data

Once the predictive AI model was built and validated, we applied it to real-world small business data. We have some really supportive partnerships that bring us direct access to small businesses. At Crediture, we gather information from commercial borrowers such as financial transactions, social media data, and public records. We feed this data into our predictive AI model, which produces a credit risk score for each borrower along with other insights that lenders find useful.

Our AI model isn't static, though. It's designed to learn and adapt over time as more data becomes available. This enables us to continually refine our credit assessments and ensure they remain accurate and relevant as market conditions evolve.

What's Next?

While we continue to leverage predictive AI for improved credit risk assessments, we're also exploring new avenues. We're researching how deep learning, a subset of AI that's modeled after the human brain, can help us uncover even more nuanced insights from the data we collect. Deep learning could allow us to consider even more factors in our credit risk assessments and predictions with the ability to learn from unstructured data. More on that to come...

Predictive AI, along with generative AI and other technologies that Crediture is applying to the credit industry, is enabling more accurate, efficient, and fair credit risk assessments. We're excited to drive our mission of creating a more inclusive and transparent commercial credit landscape forward.

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