Designing Quality into Expert Systems: A Case Study in Automated Insurance Underwriting

AAAI Conferences

It can be difficult to design and develop artificial intelligence systems to meet specific quality standards. Often, AI systems are designed to be "as good as possible" rather than meeting particular targets. Using the Design for Six Sigma quality methodology, an automated insurance underwriting expert system was designed, developed, and fielded. Using this methodology resulted in meeting the high quality expectations required for deployment.


How blockchain can improve the mortgage process

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Global banks that have a large mortgage business are facing pressure internally and externally to upgrade their operating model to save money, decrease processing times and enhance the customer experience โ€“ today it can take more than 60 days to complete a mortgage transaction. The pressure is particularly strong with FinTechs like US online lender Rocket Mortgage and UK digital mortgage broker Trussle creating a completely digital experience for prospective home buyers. Banks, therefore, are exploring everything from mature technologies like Optical Character Recognition (OCR) to more leading edge and high-tech solutions based on blockchain and artificial intelligence. While some of these solutions could dramatically impact day-to-day business for lenders and their brokers and customers, blockchain has the potential to completely transform the entire mortgage financing industry. The financial services industry is all about trust โ€“ whether relationship based, reputational, authoritative (legal) or transactional โ€“ banking today is built on trust.


Custom DU: A Web-Based Business User-Driven Automated Underwriting System

AI Magazine

Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules. Using Custom DU, lenders can create different rule sets for their products and assign them to different channels of the business, allowing for centralized control of underwriting policies and procedures--even if lenders have decentralized operations.


Russia Inaugurates Blockchain for Mortgage Loans

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The Russian subsidiary of the Austrian lender Raiffeisenbank has run the country's first ever mortgage deal on blockchain. It could be a taste of more to come in the nation. In the transaction, a mortgage contract was issued as an xml document containing all relevant information, including data on the mortgage loan issuer, the borrower, date and place of signing the deal, the total amount of the loan, and the repayment period. The use of blockchain for mortgage loan issuance is set to increase the safety of data storage, cut depository costs, and speed up transactions for both borrower and lender, Raiffeisenbank said in announcing the deal. Normally, after sealing a mortgage deal, the borrower has to visit the bank again to deposit the mortgage contract, while the application of blockchain allows the borrower to do it remotely, also cutting the amount of paper documents.


IAAI95-005.pdf

AAAI Conferences

The GENIUS Automated Underwriting System is an expert advisor that has been in successful nationwide production by GE Mortgage Insurance Corporation for two years to underwrite mortgage insurance. The knowledge base was developed using a unique hybrid approach combining the best of traditional knowledge engineering and a novel machine learning method called Example Based Evidential Reasoning (EBER). As one indicator of the effkacy of this approach, a complex system was completed in 11 months that achieved a 98% agreement rate with practicing underwriters for approve recommendations in the fist month of operation. This performance and numerous additional business benefits have now been confirmed by two full years of nationwide production during which time some 800,000 applications have been underwritten. As a result of this outstanding success, the GENIUS system is serving as the basis for a major re-engineering of the underwriting process within the business. Also, a new version has recently been announced as an external product to bring the benefits of this technology to the mortgage industry at large. In addition, the concepts and methodology are being applied to other financial services applications such as commercial credit analysis and municipal bond credit enhancement. This paper documents the development process and operational results and concludes with a summary of critical success factors.