Navigating the Scoring Landscape: AI is NOT One-Size-Fits-All
AI and automated credit scoring has the potential to offer you a substantial boost in your lending business. However, incorrectly implemented AI could be ineffective, or worse, destroy the carefully-structured processes that drive your business forward. In credit scoring, AI is widely known to deliver strong benefits, highlighted by an immediate gain in lift and […]
Our Response to the OCC’s Senate Testimony on Regulatory Priorities
The Acting Comptroller of the Currency is responsible for addressing matters facing the OCC and the American federal banking system. This article makes reference to his testimony on August 3, 2021 to the Senate Committee on Banking, Housing and Urban Affairs, which can be read in full here: Written Testimony of Michael J. Hsu, Acting […]
Are You Really Scoring for Ability to Pay and Risk in Today’s Environment?
After Sub-Prime Auto and Leasing were obliterated during the “Great Recession”, various data points the Auto Finance eco-system relies on went through various ups & downs before things are “Normalized”. Today is quite different, with many more variables available to reach your goals accurately, competitively, and safely in Auto lending. Since GM & Ford first […]
[COVID-19] Open Letter from Our CEO: We Want to Help Lenders
Dear lenders, The global COVID-19 health and economic crisis is changing hourly. We believe we can only get through this together and we want to try and help in small ways. Individuals and businesses are already feeling financial shortfalls. Lenders like you are going to need to make decisions about a growing number of individuals […]
Adopting AI for Credit and Lending Decisions: 4 Common Data Mistakes
Are you interested in adopting AI and machine learning to grow your lending? If so, the journey to realizing AI and machine learning for your credit and lending decisions essentially begins with data. Handling and preparing a massive amount of data for AI and machine learning can be uncharted territory for many. If you’re looking […]
The Role of Time Series in Loan Data Analysis
In the first two blogs of our four part blog series we covered structured and unstructured data in underwriting and the importance of ontologies, or agreements of what the data mean. This brings us to the next topic, time, and its impact when analyzing loan data. Time series modeling is a very popular and powerful way to […]
The Importance of Data Ontology in Big Data Loan Underwriting
This is the second blog of our four part blog series with topics pertaining to structured and unstructured data in big data loan underwriting. In our first blog of the series, we provided an overview of structured and unstructured data, its impact in data driven underwriting, and how to integrate unstructured data into machine learning […]
Structured and Unstructured Data in Data Driven Underwriting
Gartner estimates that 80% of enterprise data is comprised of unstructured data. Yet until tech advances afforded by machine learning, structured data has been the go-to for data analytics and models. The advances in computation power have afforded organizations the ability to analyze and incorporate unstructured data into their business decisions. One could even say, unstructured […]
Our Bet on Alternative Data for Credit Scoring
Recently five federal regulators issued a joint statement advocating the use of alternative data in credit scoring. Ultimately, the statement was to expand access to credit and provide more favourable terms to borrowers. At Trust Science we’ve been firm believers in the use of alternative data to not only serve the underbanked but also give lenders […]
Top 2020 Underwriting Data Trends
Are you ready for 2020? We believe in giving people the credit they deserve and arming lenders with credit decisioning tools and analytics that keep their business current. And to continue working on helping the credit underwriting market our CEO and CTO put together the top 5 underwriting data trends lenders need to consider for their business in 2020. […]
