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.
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.
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.
These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.