Introduction

JG Wentworth is going through a complete digital transformation with the acquisition of Stilt and Ottopay. These two companies gave JG Wentworth the foundational pieces to grow their personal lending business and positioning themselves as one of the most data driven lenders in the United States.

Credit Bureau Tradeline Orchestration

There are several major credit bureaus here in the United States. Each credit bureau receives information slightly differently from one another. There are also differences in which financial services are reporting to which bureau.

This causes classification issues across all bureaus. Credit cards, mortgages, auto loans, student loans and personal loans are frequently reported to the credit bureaus however the frequency and completeness of so called “Trade lines” could vary from one financial institution to another and therefore the data quality is slightly different from one bureau to another.

LendAPI has tools to help financial services companies such as JGW to correct the information within these tradelines to properly feed these information into their credit risk analysis to be more accurate than if it was fed the incorrect information from the get go.

Open Banking & Transactional Deep-Dives

Open banking is wildly popular nowadays in personal finance underwriting. Cashflow analytics is key for many financial institutions to properly assess the creditworthiness of the customer. With major players such as Plaid and Yodlee, the open banking industry has not developed a standard way of displaying and categorizing banking transactions. Banking transactions could also come from scanned bank statements from our Doc AI tool that transcribes all of the transactions and its banking transaction categories.

We provide a tool for our clients to identify and categorize transactions collected from Doc AI, and any of the open banking platforms to properly tag and label these transactions before being sent to the decisioning process. Properly codifying banking transitions and credit tradelines is one of the utmost important tasks for our clients to maintain the quality of data prior to the overall credit decisioning process.  

Model Implementation

Our clients have specific needs with a multi-layered credit modeling approach. Sometimes, these transactions from the credit bureau as well as the open banking data sources need to be further refined before going into a non-linear credit risk model.

These non-linear risk models are designed in specific modeling languages and LendAPI has the ability to implement any types of credit risk models without converting them into standard computer engineering languages. If the models are programmed using Python, we can easily implement these models in the model creation native languages.