In Korea, Kyobo Life has announced the launch of its new AI-based underwriting platform called Best Analysis and Rapid Outcome (BARO). The platform employs machine learning technology with the ability to process large amounts of natural language data. Kyobo life's AI-based underwriting platform employs machine learning technology and has the ability to process large amounts of natural language data The platform provides real-time services to sales staff and customers. The platform leverages Kyobo Life's underwriting manual to facilitate online deliveries by enabling instant communication with its sales staff. BARO's intelligence allows for easy approval or denial of insurance contracts with the help of screening criteria for pre-existing conditions and medical history.
In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.
Financial institutions have a wealth of information available to them from consumers. Due to manual and antiquated models, residential lending processes so far have had several negative experiences for both the lender and the borrower. Banks are plagued with application limitations, transaction complexities and data collection and processing challenges. The'one-size-fits-all' loan application simply does not work anymore. The newly implemented and redesigned URLA (Uniform Residential Loan Application), aims to simplify, organize and streamline the entire consumer journey – from loan request, to the underwriting and approval process.
When people talk about artificial intelligence and mortgages -- and they do, a sign of AI tackling more real-world problems -- they mostly refer to making the process less of a paper-heavy slog. Born of a unique tech startup builder called Entrepreneur First -- a "talent investor" that pairs up smart strangers and helps them develop whatever company idea they come up with -- Proportunity uses AI based on Intel technologies to find London properties that are poised to appreciate in value. The company then offers a sort of "bridge loan" for first-time buyers who can afford the monthly payments but don't have enough for the down payment that traditional lenders require. "We started with first-time buyers because this is a huge problem here in London," says Stefan Boronea, Proportunity co-founder and CTO. The company estimates that down payment requirements keep some 200,000 more would-be first-time buyers from joining the U.K. market today, compared with 2001.
Enterprises adopt artificial intelligence in an effort to positively impact their business performance. But the power of AI goes beyond business and can even change human experiences. This 21st century technology is serving as a driver and even impacting consumer services across a variety of industries, from retail, finance and beyond. The following client experiences serve as a gateway to better understand AI, which not only helps create a reaction, the technology can also help us act proactively in advance. Imagine a young couple who just became first-time parents and want the peace of mind that if anything happens, their new family member is protected.
Medical information and data has grown exponentially in recent years, posing new challenges for life insurance underwriting. With often voluminous medical histories to assess risk, the process can take an inordinate amount of time. Applicants can end up frustrated, dropping out of the application process and seeking other alternatives, perhaps with competitors, in search of a quicker turnaround. In response, insurers are turning to natural language processing – a more focused implementation of conversational AI – to assist with sifting through massive amounts of medical documentation to identify and even assess mortality risk. The benefits are manifold: not only does this result in an accelerated and accurate new business underwriting process, but it's also a way to create a quality data set for improved predictive underwriting.
Conceptually, AI ethics applies to both the goal of the AI solution, as well as each part of the AI solution. AI can be used to achieve an unethical business outcome, even though its parts--machine learning, deep learning, NLP, and/or computer vision--were all designed to operate ethically. For example, an automated mortgage loan application system might include computer vision and tools designed to read hand-written loan applications, analyze the information provided by the applicant, and make an underwriting decision based on parameters programmed into the solution. These technologies don't process such data through an ethical lens--they just process data. Yet if the mortgage company inadvertently programs the system with goals or parameters that discriminate unfairly based on race, gender, or certain geographic information, the system could be used to make discriminatory loan approvals or denials.
This is not a blog about Old MacDonald or his farm. Instead it is about Artificial Intelligence (AI) in the mortgage industry. And we will NOT allow any sarcastic, caustic or offhand remarks about the mortgage industry needing some kind of intelligence. First of all, exactly what is artificial intelligence, at least how it is described of late? One thing it is not is fake intelligence (not related to fake news … and you might like this site that helps YOU create your own fake news … but I digress, and so soon ... sorry).
AI Foundry is aiming to further cut the time it takes to originate a mortgage by adding, among other things, artificial intelligence technology designed to improve on optical character recognition. The fintech firm on Tuesday launched a new "cognitive business automation platform" aimed at improving automated decision-making related to loans, and a new version of its Agile Mortgages technology, which loan officers use to automate the collection and organization of documents. The new technology classifies data and information that at a 90%-plus accuracy rate is "significantly more accurate than optical character recognition," according to a company press release. "Competition in the mortgage lending industry is intense, and while many companies have deployed point-of-sale solutions for the customer, the back-end processes have not undergone a digital transformation, until now," Steve Butler, founder and general manager of AI Foundry, said in a press release. How does your digital mortgage stack up? "We will truly disrupt the'status quo' by automating the mortgage application process and enabling lenders to complement those front-end capabilities with one-day mortgage approvals. This not only opens enormous potential for acquiring and delighting new customers; it also drives down the cost per mortgage, so lenders can be more profitable."