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.
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 – 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 – whether relationship based, reputational, authoritative (legal) or transactional – banking today is built on trust.
Supports the NAIC's macro-prudential surveillance of US insurance industry assets by (a) monitoring investment markets for each of the major asset classes owned by insurers (fixed income, equities and real estate) as well as other potential markets insurers may consider for investment; (b) considering the potential risks and issues related to those investments and markets; and (c) analyzing the potential impact of adverse market conditions on US insurers' investments, individually and as a group. Demonstrates broad, innovative thinking that encompasses analyzing credit risk and other issues such as liquidity and volatility; and also takes into account portfolio and asset/liability considerations Leverages a variety of resources (i.e. industry experts, investment banking research, rating agency reports among others), with guidance identifying relevant theses, and deriving thoughtful conclusions as part of the analytical process Performs accurate and complete qualitative and quantitative analysis of investment portfolios or specific parts of an investment portfolio of insurance companies, identifying specific risks and potential concerns and any significant exposures that could impact insurer solvency Writes and interprets SQL or Access queries for standard as well as ad hoc data mining purposes. Tableau) to spot trends and anomalies as well as create unbiased stories and conclusions with data Attends conferences, webinars, seminars, NAIC continuing education courses, etc. to further knowledge of capital markets, various types of investments (i.e. Supports the NAIC's macro-prudential surveillance of US insurance industry assets by (a) monitoring investment markets for each of the major asset classes owned by insurers (fixed income, equities and real estate) as well as other potential markets insurers may consider for investment; (b) considering the potential risks and issues related to those investments and markets; and (c) analyzing the potential impact of adverse market conditions on US insurers' investments, individually and as a group. Writes and interprets SQL or Access queries for standard as well as ad hoc data mining purposes.
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.
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.