This post was co-written with Robert Berger and Adine Deford from InformedIQ.
InformedIQ is the leader in AI-based software used by the nation’s largest financial institutions to automate loan processing verifications and consumer credit applications in real time per the lenders’ policies. They improve regulatory compliance, reduce cost, and increase accuracy by decreasing human error rates that are caused by the repetitive nature of tasks. Informed partnered with Origence (the nation’s leading lending technology solutions and services provider for 1,130 credit unions serving over 64 million members) to power Origence’s document process automation functionality for indirect lending to automatically identify documents and validate financing policies, creating a better credit union and dealer experience for their network of over 15,000 dealers. To date, $110 billion in auto loans have originated with Informed’s automation, which is 8% of all US auto loans. Six of the top 10 consumer lenders trust Informed’s technology.
In this post, we learn about the challenges faced and how machine learning (ML) solved the problems.
Manual loan verification document processing is time-consuming. The verification includes consumer stipulations like proof of residence, identity, insurance, and income. It can be prone to human error due to the repetitive nature of tasks.
With ML and automation, Informed can provide a software solution that is available 24/7, over holidays and weekends. The solution works accurately without conscious or unconscious bias to calculate and clear stipulations in under 30 seconds, vs. an average of 7 days for loan verifications, with 99% accuracy.
Informed uses a wide range of AWS offerings and capabilities, including Amazon SageMaker and Amazon Textract in their ML stack to power Origence’s document process automation functionality. The solution automatically extracts data and classifies documents (for example, driver’s license, paystub, W2 form, or bank statement), providing the required fields for the consumer verifications used to determine if the lender will grant the loan. Through accurate income calculations and validation of applicant data, loan documents, and documented classification, loans are processed faster and more accurately, with reduced human errors and fraud risk, and added operational efficiency. This helps in creating a better consumer, credit union, and dealer experience.
To classify and extract information needed to validate information in accordance with a set of configurable funding rules, Informed uses a series of proprietary rules and heuristics, text-based neural networks, and image-based deep neural networks, including Amazon Textract OCR via the DetectDocumentText API and other statistical models. The Informed API model can be broken down into five functional steps, as shown in the following diagram: image processing, classification, image feature computations, extractions, and stipulation verification rules, before determining the decision.
Given a sequence of pages for various document types (bank statement, driver’s license, paystub, SSI award letter, and so on), the image processing step performs the necessary image enhancements for each page and invokes multiple APIs, including Amazon Textract OCR for image to text conversion. The rest of the processing steps use the OCR text obtained from image processing and the image for each page.
Informed provides solutions to the auto lending industry that reduce manual processes, support compliance and quality, mitigate risk, and deliver significant cost savings to their customers. Let’s dive into two main advantages of the solution.
Automation at scale with efficiency
The adoption of AWS Cloud technologies and capabilities has helped Informed address a wider range of document types and onboard new partners. Informed has developed integrated, AI/ML-enabled solutions, and continuously strives for innovation to better serve clients.
Almost the entirety of the Informed SaaS service is hosted and enabled by AWS services. Informed is able to offload the undifferentiated heavy lifting for scalable infrastructure and focus on their business objectives. Their architecture includes load balancers, Amazon API Gateway, Amazon Elastic Container Service (Amazon ECS) containers, serverless AWS Lambda, Amazon DynamoDB, and Amazon Relational Database Service (Amazon RDS), in addition to ML technologies like Amazon Textract and SageMaker.
Reducing cost in document extraction
Informed uses new features from Amazon Textract to improve the accuracy of data extraction from documents such as bank statements and paystubs. Amazon Textract is an AI/ML service that automatically extracts text, handwriting, and other forms of metadata from scanned documents, forms, and tables in ways that make further ML processing more efficient and accurate. Informed uses AWS Textract OCR and Analyze Document APIs for both tables and forms as part of the verification process. Informed’s artificial intelligence modeling engine performs complex calculations, ensuring accuracy, identifying omissions, and combating fraud. With AWS, they continue to advance the accuracy and speed of the solution, helping lenders become more efficient by lowering loan processing costs and reducing time to process and fund. With a 99% accuracy rate for field prediction, dealers and credit unions can now focus less on collecting and validating data and more on developing strong customer relationships.
“Partnering with Informed.IQ to integrate their leading AI-based technology allows us to advance our lending systems’ capabilities and performance, further streamlining the overall loan process for our credit unions and their members”
– Brian Hendricks, Chief Product Officer at Origence.
Informed is constantly improving the accuracy, efficiency, and breadth of their automated loan document verifications. This solution can benefit any lending document verification process like personal and student loans, HELOCs, and powersports. The adoption of AWS Cloud technologies and capabilities has helped Informed address the growing complexity of the lending process and improve the dealer and customer experience. With AWS, the company continues to add enhancements that help lenders become more efficient, lower loan processing costs, and provide serverless computing.
Now that you have learned about how ML and automation can solve the loan document verification process, you can get started using Amazon Textract. You can also try out intelligent document processing workshops. Visit Automated data processing from documents to learn more about reference architectures, code samples, industry use cases, blog posts, and more.
About the authors
Robert Berger is the Chief Architect at InformedIQ. He is leading the transformation of the InformedIQ SaaS into a full Serverless Microservice architecture leveraging AWS Cloud, DevOps and Data Oriented Programming. Principal or founder in several other start-ups including InterNex, MetroFi, UltraDevices, Runa, Mist Systems and Omnyway.
Adine Deford is the VP of Marketing at Informed.IQ. She has more than 25 years of technology marketing experience serving industry leaders, world class marketing agencies and technology start-ups.
Jessica Oliveira is an Account Manager at AWS who provides guidance and support to SMB customers in Northern California. She is passionate about building strategic collaborations to help ensure her customer’s success. Outside of work, she enjoys traveling, learning about different languages and cultures, and spending time with her family.
Malini Chatterjee is a Senior Solutions Architect at AWS. She provides guidance to AWS customers on their workloads across a variety of AWS technologies. She brings a breadth of expertise in Data Analytics and Machine Learning. Prior to joining AWS she was architecting data solutions in financial industries. She is very interested in Amazon Future Engineer program enabling middle-school, high-school kids see the art of the possible in STEM. She is very passionate about semi-classical dancing and performs in community events. She loves traveling and spending time with her family.
Read MoreAWS Machine Learning Blog