Daniele Grassi, CEO of Axyon.AI
From the shifting economy to the changing political landscape – everything has an impact on the market. Yet, even with all the advancements in econometrics and analysis over the last few decades, the sector has still been unable to fully anticipate the true effect these changes will have.
The answer lies in the past and understanding how to harness historical incidents to inform future events. Those firms that can unlock the data hidden in these trends will be able to lead the market, see increased profits and avoid the negative impact of market shifts.
The challenge of looking back
There is an innate challenge in using past events to anticipate future change, however. Markets face extremely complex dynamics where thousands upon thousands of actors interact in non-linear ways. As a result, it becomes extremely complicated to identify the underlying trends and processes that will influence future change. However, through understanding these intricate relationships, firms can begin to anticipate regime shifts and even black swan events.
It’s here that the traditional quantitative analysis, supported by human supervision, is not enough. There is simply too much data and too many variables to consider. Even if analysts do make predictions, these are mainly based on surface-level trends. However, there are many more patterns and dynamics that traditional quantitative analysis and the human mind are unable to perceive, such as small shifts in entirely different industries that can create huge waves for the entire market.
A storm in a clear sky
The benefits of anticipating market shifts go beyond remaining competitive or increasing profit; these insights can also offer protection against entirely unexpected developments. While experts may be able explain in hindsight how a black swan event occurred, this understanding is not always enough to prepare or offset the negative impact of future events.
Every time a black swan occurs, the circumstances causing it appear to be different. Nevertheless, the result always ends up disrupting and damaging many investors and institutions.
Part of the reason for this lack of preparation is due to how firms currently adapt to market change. Traditional stress tests are not perfect, as they tend to replay a limited set of scenarios in order to predict how future market changes could affect a portfolio or the risk balance of an institution in general.
Even when these historical scenarios have more complex variations included, they often use a limited range of variables that do not account for deeper market shifts. This is where scenario analysis needs a more effective process.
With the proper tools, human investigations can be enhanced to provide more accurate predictions of the market. However, technology is often still a stumbling block for many financial institutions, due to a fundamental lack of understanding and whether it will deliver practical benefits for the business.
The reality is far more positive, as AI solutions can actually help workers to be more efficient in their role. As a result, financial professionals should welcome the use of technology to help predict future trends and market issues.
New machine learning techniques, such as Generative Adversarial Networks (GANs), can support this goal by modelling the market with far greater accuracy. The data used in the formation of these scenarios can be more detailed and broader in scope, which means that the technology can look at data points coming from a range of actors that influence the market – such as economic data, fundamentals, sentiment and news. GANs can then use all of this nuanced information generate scenarios that take into account the inner workings of the market and not just surface-level events.
In practice, this is achieved by having two AIs working against one another. One AI produces fake scenarios while the other decides whether that data is real or false. As the AI learns to spot the false data, its counterpart improves its practices to make the next set of data, or market scenario, even more realistic. By using this kind of synthetic data, potentially in the form of thousands and thousands of years of realistic market scenarios, the planning and preparation for market changes can be constantly developed and not limited to static events that have taken place in the past.
While currently existing at a research-level, this technology is likely to reach mainstream adoption in the next few years. If firms are prepared to make this transition, they will be able to gain a far better understanding of how the market will shift, not only by drawing directly from historical events, but also by understanding how variations on these events can shape the market as well.
TIME TO THINK OUTSIDE OF THE BLACK BOX
Mike Brockman, CEO, ThingCo
If you have the unbridled joy of parenting a teenager you’ll probably know what telematics insurance is. In very simple terms, telematics or ‘black box’ insurance enables insurance companies to track driving behaviour using technology fitted to the car or via a smartphone app. It is the first practical example of IoT – machine to machine communication of real-time data.
Telematics has been crucial to helping thousands of young people get experience on the road who would otherwise have found the cost of insurance too high. When you look at the number of road casualties in the UK over the last nine years there is a clear correlation between the rising adoption of telematics and a fall in young driver casualties[i]. The problem is that as soon as they can, young drivers chuck in telematics and take traditional insurance. As such telematics insurance has got stuck firmly in a rut.
So why is that a problem?
First, telematics saves lives – think what it could do if more drivers had it.
Secondly motor insurance costs are linked to claims costs – if we can bring down the cost of claims through the engagement, speed of response in accidents and anti-fraud benefits of using telematics data to its full potential, everyone could access cheaper insurance.
Thirdly we are living in a world deeply impacted by COVID-19. Travel trends were already altering prior to the pandemic but have changed and could remain significantly changed for the foreseeable future. Consumers are beginning to think more deeply now about their motor insurance and value for money. This may create demand for motor insurance cover that is more responsive to people’s individual driving behaviours – why pay an annual premium when you only use the car once or twice a week? On the flipside, those nervous of using public transport could see an increase in their car use. Telematics allows insurance providers to offer insurance based on actual rather than predicted use.
The fundamental reason for telematics getting stuck in a rut is insurance companies are not offering something consumers actually want and they are not deriving value from their investment in the technology. Different telematics devices give different qualities of data and that data determines the economic equation they have to resolve in terms of how much they pay for the technology and what value they get from it.
Another key factor is that if you give something away – as the insurance industry has done with telematics ‘black boxes’ – you are sending a strong signal to the customer that the technology is of no value to them and only there to serve the insurer’s need.
You need to make the device a desirable piece of technology that consumers would value in their own right – rather than something that is imposed on them to get cheaper insurance. By introducing new technologies into these devices such as Voice, camera, ADAS, black spot warnings, it becomes a truly connected device that not only helps the driver but also creates incredible amounts of data that’s useful to the insurer to manage risk and provide better customer services.
With next generation telematics, the data is no longer a one way street direct into the insurer. You can feed that data back to the customer and develop additional services such as a voice alert when they have been driving for too long without a break, an incentive of a coffee at the next rest-stop.
Telematics also transforms the claims process for the customer and the insurance provider. A crash alert can kick in and activate a voice command in the device and that will ask the driver if they had an accident, whether they need help and will alert emergency services if necessary.
This is where the data brings huge value to the insurance provider providing a whole range of detail – like a liability assessment, video footage, fault, g-force etc. This data is dynamite to First Notification of Loss team with an insurance provider.
But the biggest difference next generation telematics offers is it really strengthens the relationship with customers and insurers can make it fun as well. Insurance and fun aren’t usually two words you see in the same sentence but unlike traditional insurance, or old school telematics, it allows engagement and the opportunity to provide incentives without any big brother feeling about it.
Technology has changed massively over the last ten years, the quality of devices has developed and the Cloud has opened the potential for telematics products to be designed for customers in the most attractive way. Barriers around trust and big brother can be broken down by being absolutely clear that the data belongs to the driver – they can choose how it is used to their benefit, spelling out the advantages, being transparent and flexible.
COVID-19 is providing an opportunity to stand back and think about telematics differently – how to make it customer friendly and how to make the economics work. By leveraging next generation telematics technology the insurance market has a window of opportunity to turn the motor insurance grudge purchase into something consumers really start to value.
BRANCHES ARE THE HUMAN FACE OF YOUR BANK?
Sudeepto Mukherjee, Senior Vice President, Financial Services Lead EMEA & APAC Publicis Sapient
Branches have always played a pivotal role in a bank’s ability to acquire and service customers. Historical surveys have consistently pointed to the fact that proximity to branches is one of the key reasons that determine who consumers choose to bank with. Even with the increased adoption of mobile banking in the past decade, research from data specialists CACI had found that surprisingly, the decline of branch visitors has been modest, equating to less than 2% per year, with digital channels supplementing the customer experience rather than replacing it.
The COVID pandemic has changed all of that. It has suddenly forced consumers away from branches into call centres and web/mobile channels to meet their banking needs. So the big question is what role should branches play as we recover from this pandemic? Will branch centric business models like that of Metro Bank still thrive or will the digital only banking offerings like those from Starling and similar win out?
Banks will always have 2 different faces to consumers. The first face is one that is human and relationship based. This is the part of the bank that consumers rely on to get advice on how to manage their life savings. The face that they call upon when they are in financial distress and need help overcoming that. The face that helps them make product choices on what credit type would best suit their circumstance. The second face is that of the bank as an efficient machine that uses the best available technology, data and AI to meet transactional needs quickly. This is the face that consumers rely on to make payments in real time and conveniently. The machine that provides the ability to quickly respond to queries around account balances and transaction history. The machine that alerts consumers when certain actions are performed on their accounts. Customers expect both these faces from their bank. However, the financial crisis and the PPI scandals saw banks loose the trust and credibility of customers as they were seen to be driven more by internal profits rather than consumer needs. The human face of the bank was no longer visible to most consumers and the machine failed to live up to the expectations set by the Big Tech giants like Apple and Amazon that seamlessly provided services via their digital platforms.
The Bank Branch can play a pivotal role going forward in re-establishing this human side by helping a bank build trust and become the primary advisor for our financial needs. Instead of just meeting transactional needs like check deposits and account openings, banks can now transition branches into relationship centres where their employees are 100% focussed on financial advice and well-being of their customers. They are teachers and coaches, life-cheerleaders and financial partners – they are many in number.
Historically this model has been difficult to achieve because of the high cost of such personalised service at scale in branches. However, advancements in technology/AI coupled with the propensity of customers to use digital channels for transactional needs now make this imminently within reach .
This transition will require a fundamental shift in 3 big areas:
- Creating a strong digital infrastructure to enable an omni-channel service: Banks will have to double down on their digital transformation efforts and build an infrastructure that can serve most transactional needs seamlessly via digital channels and call centres. The operational burden on both call centre and back office staff will have to be significantly reduced by automating as many processes as possible and providing the right tools and insight to help consumers efficiently.
- Culture and Capability: This will also require a big shift in both capability and culture. Every function of a bank (like risk, finance, product control) will have to get more comfortable in leveraging technology to do a majority of the tasks currently done by humans while investments will be needed in new capabilities so front line staff can focus on building relationships at scale and provide good advice to consumers.
- Bringing customers along on this journey: All this will work only if there is also a strong focus on educating customers on how best to interact with a bank and use branches only for the most complex needs while relying on other less expensive channels for day-to day banking services.
Making this transition will not be easy. Constrained finances and a higher compliance burden, have resulted in a large technology debt and complex operating models in most banks. Banks have to take a more ambitious approach to “jump” to this new model. Digital leaders like Amazon and Netflix have shown how a shift from physical stores to a more digital centric ecosystem can not only be more efficient but also create value for consumers.
Now is the time for banks to seize this opportunity to redefine the role of branches and re-establish them as essential advice centres for meeting their communities financial needs.
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