BEL in Finance

BEL-based Emotional AI

One of the most rewarding technologies in the current economy is the use of Artificial intelligence in finance. From banking to every other sector of the financial industry, artificial intelligence has provided cutting-edge tools to the industry leaders to make smarter decisions and optimize their performance, in various processes ranging from credit and mortgage decisions to algorithmic trading and risk management.

# Credit Underwriting: “Cash is King”, and credit is Queen! The majority of customers prefer paying with credit cards and debit cards rather than paying cash. The number of people getting loans is skyrocketing and the world’s debt is the highest in history.

One of the biggest challenges of the banking and lending industry is to make better decisions in terms of assessing the customer, so the loans are not defaulted. This is even more critical, when the information about the customer is limited. For example, underserved borrowers, such as people moving from another country or the millennials, usually have trouble getting loans or their rates are much higher. The reason is that the financial institutions often don’t have enough data to make a decision. AI solutions, such as machine learning and deep learning are helpful for the banks and lenders to make smarter decisions by using a range of factors to more accurately assess the borrowers risk of default. Companies such as Los Angeles-based ZESTFINANCE, Boston-based DATAROBOT and UNDERWRITE.AI, and New York-based SCIENAPTIC SYSTEMS have been analyzing thousands of data points to assess credit risk for consumer and business loan applicants. Using data and proprietary models, these companies have transformed the FINTECH and finance industry by reducing bad debt and optimizing credit to lenders.

Our BEL-based intelligent prediction and classification models can predict nonlinearities and uncertainties with more accuracy than traditional AI solutions, such as machine learning. In other words, we can help banks and lenders make more accurate underwriting decisions by predicting which customers have a higher likelihood of default.  

And more importantly, our models need fewer datasets to train our models. That’s why our models can be extremely beneficial for the banks, lenders and financial institutions to sharpen their decision making in the loan industry.

As an example, auto lenders that utilize our BEL-based intelligent solutions are able to more accurately predict the underwriting risk and may be able to cut losses by 30 percent annually.