By Neil Murphy, Global VP at ABBYY
There are still many industries that are document and paper heavy in their processes and the insurance industry is one of them. It’s certainly true that prior to the pandemic, digital transformation was well underway in the insurance industry, but the last year has accelerated the need for technology and it has shone a light on the importance of insurers taking a new digital approach to underwriting.
To be frank, paper forms in today’s digital world are archaic, why should insurers be filing through documents when intelligent automation can do the job for them? One thing’s for certain, continuing to use manual processes will not drive the level of profitable premium growth that insurers need to thrive. Even for companies who were already working in the modern era of automated underwriting, there were many processes that fell through the cracks. As a result, insurers have had to pivot to manage virtual interactions and exchanges between all stakeholders in the process. In fact, Deloitte forecasted that, post-pandemic, historical data may be less valuable to the underwriting process, insurers might face unexpected spikes in claims, and insurers who take an agile approach to risk assessment might be more resilient than those who do not.
This is where process intelligence comes in: it can help insurers see their processes in real-time, spot bottlenecks, and identify where data is missing. So, how can insurance leaders make the most out of their technology investments to accelerate their underwriting transformations?
Embrace document AI
Automated underwriting within itself is nothing new, and while it’s a step in the right direction and can process lots of data at once, there is still a large amount of content that is creating vulnerability. In turn, cracks can form in the system. In fact, since the pandemic, there have been several examples of gaps and broken processes that have emerged. One insurance carrier, for example, focused process automation on improving the customer experience, while gearing up for automation at scale. However, when employees began working from home, operational processes supporting critical outcomes became difficult and many were simply broken.
Insurers are flooded with copious amounts of data in various forms every day. As soon as data enters automated underwriting – whether for personal lines, commercial, or life – it feeds into many manual processes. This can result in a delay in the process, an incorrect decision, or both. In today’s digital age, companies may have automated systems in place for their structured data, but they lack artificial intelligence (AI) solutions capable of addressing the volume of unstructured data from documents.
With more consumers preferring to engage digitally, there is now an even greater emphasis to accelerate the digitisation of content and business processes across the front, middle, and back office. The significant gap between customer expectations and insurer’s abilities is driving insurers to push for better connections, including their people, processes, and customers.
Understand your processes before you start
There is no doubt that digital transformation has revolutionised customer relationships. But the journey is yet to be complete, as many insurers are still struggling to identify which automation technologies would benefit their customers experiences the best. The first step to achieving this is having a better understanding of their current document-driven processes.
This is where process intelligence come in. Process intelligence can help insurance companies gain an oversight into the businesses. This includes discovering in real-time where bottlenecks occur, where repetition happens, where data is missing, and where automation is working or not. It can also see the flow of documents and their data through your processes, identifying exactly where automation – additional technologies like Artificial Intelligence (AI), Robotic Process Automation (RPA), and Machine Learning (ML) – can make the biggest impact. Too often, business leaders simply guess which processes would be best to automate without leveraging real data.
For the best results, it’s more than just one type of technology like RPA tools. By introducing content intelligence, AI that understands documents, combined with RPA solutions, businesses can quickly improve process bottlenecks, strengthen operational efficiencies, and enhance the customer journey. Not only this, but employees can focus on meaningful and creative responsibilities within the workplace, rather than wasting time on admin-heavy tasks. Essentially, content intelligence solutions are now enabling enterprises to make unstructured content more valuable and equip AI-powered robots with the necessary skills and understanding to make intelligent business decisions.
Gain digital intelligence
By marrying process and content intelligence solutions together, companies can speedily process a variety of documents while simultaneously automating insurance processes to gain comprehensive digital intelligence. Implementation of intelligent solutions to underwriting can significantly speed up functions such as data collection from both external and internal sites, assessment of loss runs and engineering reports, reviewing the history of customers’ claims and producing recommendations based on previous losses.
By having a strategic, holistic approach in place, insurance companies can reap the near-term rewards across the board and set the pace for the industry for years to come. Looking ahead, the potential for intelligent automation in the industry is limitless.
The future of retail trading
Joe Jowett, CEO of StrikeX
The 2020s look set to be the decade of the retail trader. As the pandemic forced large parts of the globe to turn their bedrooms into offices, a new generation of mostly young traders and investors piled onto online trading platforms hoping to combat the doom and gloom of financial insecurity that hung over many at the time. This trend looks likely to outlast the pandemic itself and the considerable power of retail traders, at times making up over 20% of total worldwide trading volume, continues to disrupt the market.
As new trading platforms vie for users in an increasingly competitive environment, 2022 will pose a number of challenges concerning safety and accountability, while a consolidation of crypto and traditional asset trading looks likely. Tools like StrikeX’s own upcoming platform TradeStrike, due to be released later in the year, will ensure that trading and investing can achieve further democratisation and transparency, while enabling wider market access for both new and experienced investors.
Generation investor is here to stay
The skyrocketing growth of online trading platforms offering commission-free trading has fundamentally altered the demographics of the stock market. Research shows that the median age of new investors since 2020 is around 35, a significant reduction from pre-pandemic traders, whose median age was 48. Similarly, the average age of Robinhood’s 22 million users is 31, highlighting the fact that most online platforms are predominantly catering to millennials and Gen Z traders.
This dramatic shift in demographic, fuelled by easy access to online platforms with mobile apps and extensive social media networks on Twitter and Reddit, means that this new generation of traders and investors has a substantial influence on the market. This was seen at its most extreme in early 2021, when the subreddit WallStreetBets conspired to “short-squeeze” institutional investors who had bet against the ailing GameStop stock, causing headlines around the world.
While making money remains a priority for young traders, the sentiment behind the GameStop saga was one driven by a boisterous confidence that the traditional gatekeepers of the stock market could be swept aside, and a world previously shrouded in secrecy could be democratised and made accessible to the amateur investor. This same sentiment is shared by large swathes of crypto traders and investors, who believe in the transformative potential of decentralisation inherent in blockchain technology.
While online trading platforms like Robinhood enabled the GameStop rally, the decision to momentarily suspend trading of a number of so-called “meme stocks” caused millions of traders to lose their money and cast aspersions on the platform’s credentials of democratising the trading world. Hundreds of lawsuits concerning the episode are still pending and many users took to crypto and NFTs instead, where the blockchain-enabled peer-to-peer trading mechanics eliminate the need for intermediaries.
The GameStop saga has highlighted that trading platforms must prioritise accountability and transparency as part of their mission to benefit the retail investor. A trading platform with the unilateral right to restrict the trading of its users without prior warning will find it hard to win over a generation of investors and traders which values transparency and access above all else.
Further factors can play a part in providing broader access to new investors, including a clear breakdown of costs, such as withdrawal and order fees. As many online platforms have cluttered and complex user interfaces, these aspects are easily missed by beginners and can inhibit the accessibility to new users more generally.
Tokenisation is the future
One way to significantly democratise retail trading is the tokenisation of assets. Blockchain technology is seeing a wave of adoption across multiple sectors, from digital art and the metaverse to asset finance and real estate. As is demonstrated by the world of NFTs, any asset can be tokenised to establish an immutable and transparent record of ownership on a blockchain. Tokenising shares in stocks, bonds or commodities can completely transform the way we trade and offers the transparency and security lacking in many existing platforms.
One of the benefits of tokenisation is the possibility to trade 24/7, regardless of stock exchange cycles. As transactions can be recorded on the blockchain even when markets are closed, users can trade irrespective of their time zone, opening the market up to a wider base of traders and investors across borders. Further, blockchain automation allows for maximised transaction speeds with minimal transaction fees, while any information stored on the blockchain is accessible and verifiable by all, taking data ownership out of centralised control.
One of the most transformative benefits of tokenisation is the possibility to trade all assets, from stocks and commodities to crypto and NFTs, on one single platform. Juggling multiple portfolios on various exchanges is a significant entry barrier, as traders can lose sight of their investments. Tokenisation removes this barrier and opens the market to new users wishing to invest in both crypto and traditional shares. Finally, tokenisation allows for fractionalised shares, making diversification possible at lower costs.
A future-proof platform
At StrikeX, we are developing a solution which delivers on the benefits of tokenisation, while offering a transparent and user-friendly product to its users. Our flagship platform TradeStrike, due to launch later in 2022, is developed by retail investors for retail investors and offers tokenised assets, including stocks, NFTs and real estate, as well as cryptocurrencies, all in one unified interface.
TradeStrike will enable users to access the widest possible range of assets and 24/7 trading across borders will open up the market to a whole range of new traders who had previously been restricted from investing. Complete with a clean and intuitive interface and a range of educational tools, TradeStrike is designed to empower retail traders to make the best decisions based on clear and transparent information.
Online trading platforms have seen a monumental growth in recent years and have enabled a new wave of investors to access a previously safeguarded market. The year ahead will show whether these platforms are equipped to deal with challenges such as transparency and accessibility. One thing is clear: Generation Investor has changed trading for years to come.
Predictions for Alternative Data in 2022
Neil Chapman, CEO of Exabel
2021 saw various firsts for alternative data. The $1.6bn flotation of SimilarWeb evidenced the emergence of the first ‘unicorn’ alternative data provider, with Yipit Data’s capital raise subsequently resulting in a second unicorn valuation. On the regulatory side, the Securities and Exchange Commission issued its first fine against an alternative data provider, charging App Annie with securities fraud. Meanwhile alternative data adoption continued apace following its breakout year of 2020, in which investors had found alt data’s often higher frequency to be particularly valuable amidst such unprecedented uncertainty. This year the London Stock Exchange Group published research showing that in 2021, of all the financial services firms that it contacted, only 1% are not using alternative data at all; in 2018 that number was still up at 30%.
Looking ahead into 2022, it is now possible to make some predictions around what awaits the ever-growing community of alternative data stakeholders. 2022 will be the year when barriers to usage of alternative data will truly begin to come down:
Tooling solutions have their moment
Alternative data’s history is rooted in a form of elitism. For much of its early development, only the most sophisticated hedge funds had access to the cutting edge technology and brainpower required to successfully extract value from alternative data. As the sector matures this truth is changing; as the knowledge spreads out of the hedge funds so too do the technical capabilities, increasingly in the shape of external software platforms that allow practitioners to extract value from alternative data. Such platforms can bring an alt data capability to new users of all shapes and sizes, from non-data savvy investment teams at larger long-only investment funds to smaller family offices that have previously had the knowledge and the appetite to make the most of alt data, but had lacked the technological opportunity. This externalization trend could ultimately touch the sophisticated funds that first conceived the use of alternative data, since growing efficiencies could make these external platforms more competitive than that which is possible within a single fund.
The SEC swings into action
As mentioned, the SEC issued its first fine to an alternative data provider in 2021, finding App Annie guilty of securities fraud. This was not the precedent-setting example that the market has long been anticipating however. For several years, legal advisers have been warning hedge funds and alternative data providers that the SEC might wade into the sector and punish a practitioner that was using alternative data in an as-yet unspecified manner deemed by the regulator to be ‘too loose’. In the case of App Annie, the regulator found the app data provider to be guilty of behaviour that would be reprehensible in any sector, not related to alternative data specifically. What the App Annie judgement demonstrates is the fact that alternative data is now firmly embedded on the SEC’s radar, so there may well be further regulatory activity in 2022.
Buyside personnel moving into product
2021 has seen a growing trend for buyside personnel taking their hard-earned skills onto the product side. This could be a sign that strong venture capital flows have finally convinced these asset managers that the time is ripe for a more entrepreneurial project with high growth potential, or it could be a signal that the market is moving towards the externalization trend mentioned above, or both. Either way, it is a trend that looks likely to continue in 2022.
The rise of Synthetic Data
Synthetic data, or data that has been manufactured or created artificially for a specific purpose, is coming increasingly into vogue in data science circles, and alternative data is no different. Hedge funds have long used data pertaining to private individuals, in almost all cases for uses in which personal identifiers are irrelevant to the value. With public and regulatory scrutiny increasing around privacy, the benefits of synthetic data in which personal identifiers are scrambled and obfuscated are becoming increasingly obvious. Other uses of synthetic data, such as for generating a larger dataset for model training, or using tweaked datasets for scenario-planning, might also have potential futures in the alternative data world as the techniques are being perfected more widely.
The march of the retail investor
The Gamestop affair back in January 2021 announced the return to the limelight of an established but sometimes forgotten player – the retail investor. The year turned out to be an influential one for the man on the street, who drove valuations both up and down, meaning an investor not paying attention to the chatter could easily find themselves burned. In 2022 this trend is likely to continue and alternative data offers opportunities both to institutional investors seeking to track what retail investors are investing in in real time, and increasingly opportunities for retail investors themselves to make more informed decisions with platforms tailored for their use.
Expansion into Europe
Alternative data originated in the United States, which is still the sector’s hinterland. In recent years inroads have been made in Asia, but the next push looks likely to be taking place in Europe. Increasing local availability of credit card and other data types, taken along with the developed nature of European markets, has made Europe a geography ripe for alternative data to increase its influence. Language and privacy regulation hurdles still exist though, and the market will need to continue to find solutions that negate these hindrances.
New forms of NLP
Natural language processing has been in use since the earliest forms of alternative data were emerging in this millennium’s first decade. Textual analysis has spread from creating sentiment gauges to track social media such as Twitter, and into the cat-and-mouse contest between hedge funds trying to extract extra meaning from earnings calls and investor relations executives attempting to keep corporate communication as neutral as possible. In 2022 a new battleground is being mapped around audio analysis, with alternative data emerging around the tone and cadence of corporate communicators, with the aim being to mine this data and reveal more than the speaker is intending.
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