Flood insurance has been a wallflower at the coverage dance- an eager participant but not able to find a suitable partner. Innovation efforts have found suitable risk prediction partners for carriers- FloodMapp, Hazard Hub, and Previsco among others- but is the flood insurance market ready? Politics, inertia, customer preferences and regulation might keep the music […]
These are interesting times with Brexit around the corner, an Indo-Pak (China) war looming, and a disastrous trade relationship between the two largest economies of the world.
This week I delivered a speech at Cass Business School on how and if AI could help in dealing with recessions. There is so much noise about the next recession, that I wonder, if people prefer a recession to cool down the economy a bit.
And the expectations on Constantinople is pushing up crypto prices again – although I don’t believe for a second that, the crypto market is big enough yet, to trigger recessions.
Assume you are driving a Ford Fiesta, can the speed indicator on your dashboard keep you from having an accident? Upgrading your car to a more sophisticated, intelligent one would definitely help. But that doesn’t prevent you from having an accident either. Even self driving cars could be hacked, or could have a bug that causes accidents.
AI/Machine learning or any variation of data driven intelligence, as we know them today, can provide us suggestions – and clever ones.
But if a market filled with irrational exuberance from humans have to be fixed by rational machines, it is a tall ask.
The dot com bubble burst and the subprime mortgage crash happened because of too much liquidity in the market leading to bad lending and spending decisions. And it only took a trigger like a policy change or a crash of Lehman Brothers to sap liquidity off the market. So, what are the signs now?
How can we get intelligent with the data around us and spot recessions? An analysis of Consumer data should provide us with a view on consumer behaviour, and almost predict where inflation would be heading. One of the firms I recently met, used open banking to collect consumers data, enrich it, and help them manage their finances. But the intelligence they gather from millions of transaction level data are used by their institutional clients to understand customer sentiments towards a brand.
Those insights combined with macro economic data should give these institutions the intelligence to choose their investments. The applications of open banking have largely been focused around selling services to customers in a personalized fashion. However, open banking data should help us understand where the economy is heading too.
Risk management functions in banks/FIs have been beefed up since the recession. About £5 Billion is spent in the UK alone on risk and regulatory projects every year. The ability to perform scalable simulations in a Quantum computing ready world will help banks provide near real time risk management solutions.
In capital markets, we model the risk of a position by applying several risk factors to it. Often these risk factors are correlated to each other. To be able to model the effect of a dozen or more correlated risk factors on a firm’s position is hard for conventional computers. And as the number of these correlated risk factors increase, the computational power required to calculate risks increase exponentially. This is one of the key issues of simulations (not just in financial services) that Quantum computers are capable of solving.
11 years ago, when the recession happened, regulators were ill-equipped to react due to the lack of real time insights. Today they have regular reports from banks on transactions, and better ways to understand consumers’ behaviour. That clubbed with macro economic data trends, should provide enough indicators for regulators to set policies. So, when there is a tax law that would trigger a collapse is being proposed, they should come up with strategies to bring the law into effect with minimal damage to the economy.
In the machine learning world, there are two different approaches – supervised and unsupervised models. If you understand the problem well, you typically go for the supervised model and see how the dependent variable is affected by the independent variables.
However, I believe, recessions often have the habit of hitting us from a blind spot. We don’t know what we don’t know.
It’s important for regulators and central banks to run exploratory analysis – unsupervised models, and assess the patterns and anomalies that the algorithms throw.
Data from consumer behaviour, geo-political events, macro economics and the market should give these algorithms enough to identify patterns that bring about recessions. This may not necessarily help us avoid a recession, but could definitely reduce the impact of a sudden recession, or help us engineer a controlled recession when we want a cool down of the economy.
The Theme this week is Artificial Intelligence trends in Insurtech. AI has always been a critical subject of not only InsurTech, but also the whole digital age. Let’s see some AI-related Insurtech news this week.
“With ambitions to challenge insurance giants like Allianz SE, newcomer Deutsche Familienversicherung AG needs 100 million euros ($116 million) in fresh funds to finance its expansion plan. An initial public offering is one path that Stefan Knoll, founder and chief executive officer, is considering.
DFV uses artificial intelligence to decide which insurance claims are legitimate and which are not. In partnership with Frankfurt-based startup Minds Medical GmbH, it developed an algorithm that can read so-called ICD-10-Codes, used by doctors and hospitals to categorize their bills.”
The news was from June, a recent interview on DFV founder Dr. Stefan M. Knoll was released on InsurTechnews, one of the biggest feature of DFV is that they use AI to process claims.
Editors Note: medical insurance claims has long been a hairball of complexity that causes a lot of pain for customers/patients. The most broken big market today is America, but the politics around Health Insurance are so divisive in America, that it is possible that the breakthrough will come from another market like Germany.
“Much of executives’ enthusiasm is justified. AI is already being deployed in a range of arenas, from digital assistants and self-driving cars to predictive analytics software providing early detection of diseases or recommending consumer goods based on shopping habits. A recent Gartner study finds that AI will generate $1.2 trillion in business value in 2018—a striking 70 percent increase over last year. According to Gartner, the number could swell to close to $4 trillion by 2022.”
Despite the growth momentum, AI is unlikely to help insurers yield big results in the short term. The decision on when and where to adapt AI will be a key decision senior executives have to make.
“According to analysis from global law firm Reynolds Porter Chamberlain (RPC), 2017 saw a 40-per-cent jump in the number of insurtech patents being filed worldwide. RPC found that 917 insurtech patents were filed globally last year, compared with 657 in 2016.
Telematics, artificial technology and machine learning, and P2P insurance were among the most frequent subjects of patent protection last year.”
Telematics, artificial technology and machine learning all involve a certain degree of AI. And the growth of patent numbers signifies a positive growth on InsurTech adaption.
AI application in Insurance is still premature, but Rome was not built in one day, there will be a process. And it’s good to see that insurers featuring AI have been well received by the capital markets. This can inspire more startups and insurers to adapt AI.
Zarc Gin is an analyst for Warp Speed Fintech, a Fintech, especially InsurTech-focused Venture Capital based in China.
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