Insurance is a $1.2 trillion industry in the U.S. alone, employing 2.9 million people. Historically, the insurance industry hasn’t felt the effects of digital disruption, due to a strict regulatory environment, the scale required to create a risk portfolio, and the time needed to establish trust with customers. But in a recent IBM Institute for Business Value (IBV) survey, insurance executives identified changing market forces (such as increased competition and changing customer preferences) as the top driver affecting their enterprise. The core function of the insurance industry, risk management, has gotten more complex as customer data continues to compound. Insurance companies collect data scattered across siloed business units in paper or various unstructured digital formats. In this data-rich environment, underwriting and claims management workers don’t have immediate access to the information needed for informed internal and external decision-making, leading to burnout and costly mistakes. In fact, knowledge workers spend 30% of their time finding information required to…
Founded in 2018, Bengaluru-based Artivatic AI uses AI to assist insurance companies in building personalised risk profiles of customers, track and understand their financial and behavioural journeys, and develop real-time intelligence based on those patterns. "InsurTech is a specialised branch of fintech earmarked for insurance use cases by leveraging forever-evolving AI capabilities and mining multi-source big data via ML algorithms to acquire better insights of our users and offer the best advice and analysis. Artivatic is an AI firm, and we're streamlining insurance and healthcare as our basic model via insurance tools," said Layak Singh, CEO of Artivatic AI. In an exclusive interview with Analytics India Magazine, Layak spoke about how the firm embeds ethics into its AI systems. Layak Singh: Artivatic has gone beyond that requirement of servicing only clients to actually offering 360-degree support to all of our stakeholders, from insurance providers and TPAs to agents, underwriters, users, and any other peripheral ones.
This blog post was written by Dr. Maryam S. Jaffer, Director Data and Statistics, Emirates Health Services; Dr. Bashar Balish, Senior Director, Cerner; and Michel Ghorayeb, UAE Managing Director, SAS. The future of health care has never been more exciting. Artificial intelligence (AI) and data analytics have captured center stage for any business planning on surviving and thriving. Given the pace of technological development, AI is transforming the future on an unprecedented scale. And that includes the future of health care.
Data and analytics capabilities are becoming increasingly important'table stakes' in the property and casualty sector across Europe, North America and Asia, according to a session at the Barbados Risk and Insurance Management (BRIM) conference. Speaking at the session'Next generation insurtech: predictive modelling and artificial intelligence (AI)', Klaas Stijnen, co-founder and chief product officer at Montoux, cited NewVantage Partners' 2022 Big Data and AI Executive Survey, which found that although investment in data and AI initiatives continues to grow, achieving data-driven leadership remains an elusive goal for most organisations. Similarly, the survey found that although the take-up of AI initiatives is accelerating, the actual implementation of AI into widespread production remains low. Stijnen outlined that a data analysis-driven approach is led by data scientists, in which the focus is on available, known data and is separate from the decision-making process. Alternatively, a decision-driven approach is based on data science, which more readily challenges bias to seek missing data and is integrated into a firm's decision-making process.
Did you miss a session from the Future of Work Summit? In health care, the process of underwriting and claims analysis can be both labor-intensive and error-prone. Claim adjusters and underwriters are often required to read and carefully parse hundreds of documents per case. Each year, the insurance market invests an estimated more than $3 billion in work hours devoted solely to collating and summarizing medical records. A 2006 U.S. National Institutes of Health study identified several major challenges in researching medical records, including assessing the quality of data and combining data from companies with dissimilar coding systems.
Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into "active" mode. Scott's personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott's assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a "pay-as-you-live" basis, will increase by 2 percent for this quarter. The additional amounts are automatically debited from his bank account. When Scott pulls into his destination's parking lot, his car bumps into one of several parking signs.
A common concern surrounding automation in recent years is that it will result in widescale job losses as the work previously done by people is taken over by technology. Of course, the reality doesn't really support this narrative, and indeed, companies that invest in technology often end up employing more people as a result of the improvement in their fortunes heralded by the investment. The leadership team of the fintech company Kashat highlight the reality of investing in technology. They reveal that microfinance has traditionally been highly labor intensive, with many of the skills the same as those used in the sector for years. With the introduction of AI, new skills have been introduced into the underwriting process in order to serve at scale, while enabling employees to further expand their skillset and become even more valuable in the future.
Decisions made by various Artificial Intelligence (AI) systems greatly influence our day-to-day lives. With the increasing use of AI systems, it becomes crucial to know that they are fair, identify the underlying biases in their decision-making, and create a standardized framework to ascertain their fairness. In this paper, we propose a novel Fairness Score to measure the fairness of a data-driven AI system and a Standard Operating Procedure (SOP) for issuing Fairness Certification for such systems. Fairness Score and audit process standardization will ensure quality, reduce ambiguity, enable comparison and improve the trustworthiness of the AI systems. It will also provide a framework to operationalise the concept of fairness and facilitate the commercial deployment of such systems. Furthermore, a Fairness Certificate issued by a designated third-party auditing agency following the standardized process would boost the conviction of the organizations in the AI systems that they intend to deploy. The Bias Index proposed in this paper also reveals comparative bias amongst the various protected attributes within the dataset. To substantiate the proposed framework, we iteratively train a model on biased and unbiased data using multiple datasets and check that the Fairness Score and the proposed process correctly identify the biases and judge the fairness.
With the continuous advent of technology usage, numerous innovations are being developed and used to improve society's quality of life. Artificial intelligence is one of these innovations, and now it is being applied in health insurance, a critical and incredibly important component of today's healthcare industry. Artificial intelligence has numerous impacts in the health insurance industry to date, and these impacts may be separated into two types - the benefits it has brought, and the drawbacks it inevitably has. Read on to know more. Doing manual verification of claims is incredibly tedious work, with average, mid-sized insurers receiving about 700,000 claims from hospitals every year. With a pandemic still in our midst, the number of claims will inevitably boom.
The graph represents a network of 3,133 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 10 September 2021 at 12:18 UTC. The requested start date was Friday, 10 September 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 5-day, 16-hour, 21-minute period from Thursday, 02 September 2021 at 18:05 UTC to Wednesday, 08 September 2021 at 10:26 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.