Lenders are increasingly frustrated with OCR (optical character recognition) solutions that are designed to read data off paystubs, but they’re only pulling text from the documents.
They also aren’t an end-to-end solution helping lenders comply with ECOA and Fair Lending. Many OCR tools also focus on mortgage–super prime borrowers who don’t have income challenges. However, the use of these tools can open new challenges, such as a higher synthetic fraud rate, and charge-offs within 18-24 months from booking.
Crowdfund Insider recently connected with Justin Wickett, CEO of InformedIQ (AI data solutions for lenders), who explained why lenders continue to be frustrated with OCR, and to discuss how they are using new technologies to help overcome these challenges.
For additional context, OCR is often employed to extract information from documents relevant to the lending process, such as identification documents, financial statements, or other paperwork required for loan approval.
Many of today’s auto lenders use complex formulas to calculate applicant incomes that can change throughout the year and can’t be easily explained.
This often leaves many car shoppers and the dealers serving them confused about calculating or overstating income, which could result in an unwanted repossession down the road. And the lender misses an opportunity to capture more loans without additional credit risk. OCR also cannot determine loan defects. OCR capabilities, with the addition of machine learning, ensure contracts are complete and accurate.
Solutions that collect clearer documents ensure the lender gets what they need without disrupting their current workflow and provide the best solution from one financial technology provider.
Justin explains how AI and machine learning are transforming the way auto lenders use OCR Technology.
Our conversation with Justin Wickett, CEO of InformedIQ, is shared below.
Crowdfund Insider: What role does OCR (Optical Character Recognition) technology play in the auto lending industry?
Justin Wickett: A1: OCR technology is often the first step in the auto lending process as it allows lenders to convert various types of documents, such as scanned paper documents or images captured by a digital camera, into editable and searchable data. This is particularly valuable in automating and streamlining processes like document verification and data extraction.
Crowdfund Insider: How does AI enhance OCR technology for auto lenders?
Justin Wickett: AI significantly improves OCR technology by introducing machine learning algorithms. These algorithms adapt and learn from patterns within documents, enhancing their ability to accurately recognize and extract relevant information from a wide range of sources. This increases accuracy in document processing.
Crowdfund Insider: What specific challenges in auto lending does AI-powered OCR address?
Justin Wickett: AI-powered OCR addresses challenges such as manual data entry errors, time-consuming document verification, and the need for human intervention in document processing. By automating these tasks, AI ensures a higher level of accuracy, reduces processing time, and minimizes the risk of errors and fraud.
Crowdfund Insider: How does machine learning contribute to the accuracy of data extraction from documents in auto lending?
Justin Wickett: Machine learning algorithms in OCR systems “learn” from a dataset of documents, improving their ability to recognize and extract information over time. As they encounter more varied documents, they adapt and refine their recognition patterns, resulting in increased accuracy in extracting specific data points, such as names, addresses, and financial information from loan applications.
Crowdfund Insider: Can you provide an example of how AI and OCR work together to streamline auto lending processes?
Justin Wickett: Certainly. In the context of auto lending, AI-powered OCR from companies such as InformedIQ can swiftly process loan applications by extracting relevant information from documents like ID cards, pay stubs, and bank statements. This not only speeds up the approval process but also reduces the chances of errors that may arise from manual data entry, ensuring that lenders have accurate and reliable information for decision-making.
Crowdfund Insider: How does the integration of AI and OCR contribute to overall efficiency in auto lending operations?
Justin Wickett: The integration of AI and OCR enhances efficiency by automating time-consuming and error-prone tasks. Lenders can quickly and accurately extract essential information from a large volume of documents, enabling faster decision-making processes. This not only improves operational efficiency but also allows lending institutions to handle a higher volume of transactions with the same or fewer resources.
Crowdfund Insider What are the potential benefits for auto lenders in adopting AI-powered OCR technology?
Justin Wickett: The adoption of AI-powered OCR technology brings several benefits for auto lenders, including increased accuracy in data extraction, reduced processing time, lower operational costs, improved compliance through consistent document verification, and the ability to scale operations seamlessly to handle growing transaction volumes.
Crowdfund Insider: How does the ongoing evolution of AI and machine learning impact the future of OCR in auto lending?
Justin Wickett: The continuous evolution of AI and machine learning promises ongoing improvements in OCR technology. As algorithms become more sophisticated and capable of handling diverse document types, auto lenders can expect even greater accuracy, faster processing speeds, and enhanced adaptability to changing regulatory requirements, contributing to a more robust and efficient lending ecosystem.