The rapid development of converging technologies is bringing about fundamental changes to the insurance industry. In the long term, organisations that are slow to embrace these new technologies will struggle to compete and to retain their place in the market. In the insurance sector, the use of technology to innovate or disrupt is known as'insurtech'. This is an elastic term that takes in the use of new technologies by both start-ups and incumbent insurance companies to transform access to and analysis of data, build new products, drive customer engagement and squeeze inefficiencies from the current insurance model. Technologies such as telematics, the internet of things including smart home technologies, aerial imagery and drone technologies are giving insurers new ways to access data while developments in artificial intelligence (AI), machine learning and natural language processing are enabling insurers to process, analyse and gain insights from these large data sources.
There is no doubt that artificial intelligence (AI) and machine learning (ML) is becoming a hot topic within the fintech industry. At almost every seminar and conference, we are hearing about the rise of these emerging technologies and the potential they have to disrupt businesses. It's clear that AI and ML is a blueprint within which the fintech industry is operating. However, what is apparent is that no matter how much fintechs bang the drum of the impact of AI on enterprises, it still remains underutilized by many companies due to their inability to visualize, integrate and adopt these new technologies. Recently, there has been a great deal of conversation across multiple industries around the potential of these technologies, but according to research by Accenture, 87 percent of business leaders in the UK are struggling with how best to adopt it.
Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. Next, we establish fraud as a social phenomenon in the network and use the BiRank algorithm with a fraud specific query vector to compute a fraud score for each claim. From the network, we extract features related to the fraud scores as well as the claims' neighborhood structure. Finally, we combine these network features with the claim-specific features and build a supervised model with fraud in motor insurance as the target variable. Although we build a model for only motor insurance, the network includes claims from all available lines of business. Our results show that models with features derived from the network perform well when detecting fraud and even outperform the models using only the classical claim-specific features. Combining network and claim-specific features further improves the performance of supervised learning models to detect fraud. The resulting model flags highly suspicions claims that need to be further investigated. Our approach provides a guided and intelligent selection of claims and contributes to a more effective fraud investigation process.
The UK government's 2019 Fintech State of the Nation report identified a raft of areas in which artificial intelligence (AI) could have an impact on the financial services sector, ranging from delivering customer support through to underwriting loans and providing real-time fraud and risk management. These developments are underpinned by "machine learning", which allows computer programs to teach themselves by examining data. AI is a topic already being explored by many financial service providers, with 56 per cent believing it will reshape the sector, according to a survey from a "Big Four" accountancy firm. The poll found many companies plan to use AI to deliver automated advice to clients – having a "bot" deliver information online could cut costs which could be passed on to customers through lower fees. Banks using AI could examine data about young people's spending habits and enable them to qualify for personal loans or mortgages even if they have a short credit history, while start-up Financial services is highly-regulated and the use of AI can cut the cost of compliance dramatically.
Thousands of students in England are angry about the controversial use of an algorithm to determine this year's GCSE and A-level results. They were unable to sit exams because of lockdown, so the algorithm used data about schools' results in previous years to determine grades. It meant about 40% of this year's A-level results came out lower than predicted, which has a huge impact on what students are able to do next. GCSE results are due out on Thursday. There are many examples of algorithms making big decisions about our lives, without us necessarily knowing how or when they do it.
Across the globe, companies are amassing volumes of data with the intent of optimising performance, identifying trends and meeting rising consumer expectations. Yet nearly 75% of global financial services and insurance executives admit they are challenged by the fractured nature and vast amount of data available, citing rich analytics capabilities as difficult to achieve. In the UK alone, 71% of executives admit they are challenged by the immense data they have. With these challenges in mind, a new Aite Group study commissioned by TransUnion found that executives in the financial services and insurance industries plan on continuing to secure more data sources. Furthermore, they look to incorporate more artificial intelligence (AI) and machine learning (ML) technology into their analytic platforms to help them make sense of the information.
Earlier this year new health tech start-up EQL unveiled its debut product at the Google Campus in London to an audience of industry leaders in the healthcare and insurance markets. But how did they turn a dream into reality? Improving healthcare through the use of smart technology had long been a passion for EQL founders Jason Ward and Pete Grinbergs. Jason had seen his mum, a nurse and his step-dad, a GP, struggle with the inefficiency in the NHS and work hard to ensure that their patients received the best care. He started life working in finance in the City and in 2015 set up a digital primary care start-up in 2015 which sadly didn't make the grade.
Loughborough University and the Willis Research Network (WRN) would like to invite you to another conference bringing together a range of perspectives on the business application of Artificial Intelligence (AI) and its role in the ongoing digital transformation of the insurance industry. The goal is to look beyond the day to day business decision-making and examine the broader challenges of employing AI, the implication for business models and to address some of the organisational and public policy challenges to effective use of these new technologies. We will have a mix of top university researchers and industry practitioners participating as both presenters and panellists to enhance our depth of knowledge around AI and the use of AI in our industry. We look forward to welcoming you to a stimulating day of open debate and insightful discussion. The conference is the first major event organised by the TECHNGI research project, hosted by Loughborough University and Willis Towers Watson and funded from the UK Government Industry Challenge Fund's Next Generation Services program.
Since its establishment 100 years ago in 1919, the UK Government Actuary's Department (GAD) has been at the heart of actuarial advice, serving the public interest by providing specialist risk and finance advice to public policymakers. This is directly related to a requirement of the IFoA Royal Charter, under which the profession has a duty to put the public interest first. GAD's role has changed significantly since its inception. In the early years, it advised the National Health Insurance Joint Committee, providing advice to support the financial management of the newly introduced old age pension and health insurance systems. Over time, its influence widened as it provided advice on public service pensions, expanding social security benefits and population projections.
Machine learning technology is poised to be huge thing in financial services. In fact, two-thirds of UK-based firms are already using it. That is according to two of the UK's top financial regulators. The Financial Conduct Authority (FCA) and the Bank of England have taken a deep dive into how the financial services industry in the country is using machine learning. The research is based on a survey sent out to 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms.