Daniele Grassi, CEO at Axyon AI
For many active managers across the world, the last few years have been challenging. Failure to meet their key benchmarks has become more regular, and investors have started to pursue passive funds instead.
In an attempt to recapture their edge and regain the faith of investors, active managers have increasingly started to embrace machine learning and its tremendous analytical and predictive power. As a result, a recent Morgan Stanley poll found that 51% of investment clients said machine learning was either a component or central to their investing process, up from 27% in 2016. However, some have found it difficult to harness the potential of big data and meld it with their traditional approach to investing.
Long road ahead
An immediate problem that investment companies face, especially smaller and medium-sized ones, is the fact that access to relevant talent in the machine learning space is limited. This means that building a suitable internal team that has knowledge of both machine learning and general asset management can be difficult and can take time. At the same time, finding a reliable third-party to incorporate machine learning can also be hard, as firms will need to make sure that any third-parties have produced measurable results in a timely fashion for previous clients.
Once the internal team is set up, the challenge then becomes developing the tools required to complete the implementation process. Consistently and rigorously applying machine learning to historical financial data is a difficult task; without the right tools at hand, the team can’t train and test the machine learning to the proper standard and results will ultimately suffer when it goes live.
Perfecting the process
Due to their complexity and pattern-recognition power, machine learning techniques are at a high risk of overfitting, which occurs when the technology learns to over-detect patterns that are present in the training data, yet disappear in a “live” environment. A strong implementation process can mitigate this, but the machine learning team will need to take a solid and rigorous approach to how the data is handled, as well as training and selecting which predictive models to use.
Communication is another crucial aspect of a good implementation process. The machine learning team will need to work closely with the portfolio managers to frame the problem that machine learning has been brought in to solve.
How the problem is framed has a significant impact on how the predictive models are applied in a real-life scenario – and thus what value they can bring to the business and its investment strategy. For example, if the problem is framed in a way that doesn’t fit the investment constraints of the fund manager, or the real trading conditions of the desk, the results, however accurate, will be mostly irrelevant.
Also, if the implementation process isn’t thorough enough, it will be difficult to account for factors that may not come into play until the machine learning process goes live. Commission structures, liquidity, and price delays can all impact predictive models during their live application in support investment strategies. This can mean that the machine learning models produced are accurate, yet fail to generate value when faced with the real market conditions they encounter in a “live” scenario.
For the marriage of machine learning and traditional methods to deliver the best results, there needs to be clear perimeters as well as transparency. This not only means being clear in what machine learning will be doing in terms of approach, but also what problems it will be solving during the investment process, such as supporting asset selection. This will make it much easier to see the benefits that the technology is bringing to the company.
Asset management firms should also leverage their machine learning partnerships for educational purposes, using methods like workshops to increase knowledge within their company and to provide greater transparency on what the machine learning tools are being used for. This approach can reduce some of the confusion which may exist within the team about where machine learning fits into their current processes and methods.
Implementing machine learning can be a complicated process with challenges at every step of the way. The talent pool is limited, suitable third-parties can be hard to find, and many in a company may be suspicious of the results it can produce. Additionally, a number of factors can impact the success of machine learning when it goes live, from overfitting to data leaks.
A strong implementation process, however, driven by good communication and a sound understanding of what the company wants the machine learning models to achieve, can mitigate these risks significantly. Ongoing communication between the machine learning team and rest of the organisation will also be essential, as it can prevent predictive models being siloed and underused by the company.
In order to achieve these goals, the role of machine learning must be clearly defined from the outset, and teams must be educated on how to maximise the potential of this technology. With these steps in place, machine learning can help active managers to evolve and re-gain their competitive edge.
STOP THE CONFUSION: HOW TO KNOW IF YOUR BUSINESS MAY BE INSURED AGAINST COVID-19
By Alex Balcombe, Partner at Harris Balcombe
The last few weeks has seen businesses in hospitality, tourism, retail, leisure and more forced to close their doors following the Government’s orders that they should close to prevent the spread of coronavirus.
While this is expected to flatten the curve and reduce the number of coronavirus cases, it will of course have an impact on businesses and employees alike. For small businesses especially, there are many concerns about how they can claim on their insurance to weigh the fall of this impact.
In response to calls to help struggling businesses, the Government has informed the public that companies who are facing turmoil will be able to claim on their business interruption insurance during this difficult time. For most, this is wrong.
The insurance industry has also been extremely vocal that there is no cover for any coronavirus-hit businesses during this tough financial period. This isn’t strictly true either.
How can businesses see through the mixed messaging and best secure their future and their livelihoods and reduce money worries? It’s an extremely stressful time for many companies, and confusion over whether or not they can be covered can only cause more unnecessary stress.
Since it’s a new disease, most businesses will not be covered for business interruption due to COVID-19. In fact, the vast majority of policies do not cover anything related to COVID-19.
That said – don’t rule out the idea that you may be covered. There is a chance that you will be covered against COVID-19, but not know it. This is a very small chance, but your current cover may already protect your business against the consequences of coronavirus, and the nationwide response to it – though those with this cover are unlikely to realise it.
How Could I Be Covered?
Not everyone has business interruption insurance, as it’s not a legal requirement. It is entirely up to the policy holder to weigh up the benefits of having it, and their ability to trade should a disaster happen.
To be considered for cover for COVID-19, there are two types of policy extensions to your business interruption cover that can potentially cover you for this situation:
Infectious Disease Extension
Many policies expressly state which diseases fall within the realm of being an infectious or notifiable disease. If this is the case, your policy will not provide cover. As it is a new disease, these policies will not have included COVID-19.
Other infectious disease extension policies will define the disease with reference to the actions of the government. Since the UK Government has named COVID-19 as a notifiable disease throughout the UK, it is possible that your business may fall into this definition, thus meaning you may be able to make a claim.
However, again, it’s not always that simple. Many policies require the disease to have been on your premises, while others specify a radius from your premises in order to qualify.
Denial of Access Extension (non-damage)
Denial of Access Extension (non-damage) policies may cover you if you’re prevented from accessing your property. This could be due to an event, or by the actions of a competent authority, which could cause your business interruption cover to engage.
If covered by this clause, there are often very subtle differences in wording in your policy. This could depend on the insurer or policy. You may well be covered, but it will depend on your particular circumstances, and the specific policy wording.
It’s clear that the Government needs to do more in ensuring there is clear messaging for businesses, and to help the insurance market look after policy holders. This is an unprecedented situation, and with many people looking to claim on their insurance, we’re already seeing major delays which could have a domino impact.
People throughout the world are understandably facing all kinds of worries because of the current pandemic. Our ways of living have changed, and many business owners will not have experienced a situation like this in their life times. If you own a business and are unsure about whether you can claim for business interruption, or are confused about ambiguous wording, get in touch with a loss assessor.
These claims are not simple, but loss assessors will be experts in business interruption insurance, and will specialise in large and complex claims. They will be able to help and guide you along the way, check your wording and work on your behalf to make sure you get everything you are entitled to.
HARNESSING ANALYTICS IN THE FIGHT AGAINST FRAUD
By Anna Lykourina, EMEA Fraud Analytics Expert at SAS
In the past, the fight against fraud has been a bit hit-and-miss. It has relied on auditors to identify patterns of behaviour that just didn’t quite fit. They often only detected problems months after the event. And then organisations had to claw back stolen funds through legal processes.
In a world where transactions happen in under a second, however, this is no longer acceptable. We need to be able to detect fraud immediately, if not before it happens. Customers want safe and protected data that is not vulnerable to identity theft through company systems. But they still want to be able to pay online and in seconds. The stakes are high, but fortunately new tools and techniques in fraud analytics are enabling companies to stay ahead of fraud.
Trusting machines to do the work
Machines are much better than humans at processing large data sets. They are able to examine large numbers of transactions and recognise thousands of fraud patterns instead of the few captured by creating rules. On the other hand, fraudsters have become adept at finding loopholes. Whatever rules you set, it is likely that they will be able to get ahead of them. But what if your system was able to think for itself, at least to a certain extent?
New approaches to fraud prevention combine rules-based systems with machine learning and artificial intelligence-based fraud detection systems. These hybrid systems are able to detect and recognise thousands of fraud patterns and learn from the data. Automated analytical-based fraud detection systems can reveal novel fraud patterns and identify organised crime more consistently, efficiently and quickly. This makes them a good investment for businesses across a wide range of sectors, including public sector, insurance, banking, and even healthcare or telecommunications.
How, though, can you harness analytics as a tool in your fight against fraud?
Identifying needs and solutions
The first step is to identify which options you need. Probably the best way to do this is through a series of company-wide workshops with the fraud analytics experts to determine what analytics you need, which data to include and techniques to use, and what results to report. They can also identify the ideal combination of rules-based and AI/ML approaches to detect fraud as early as possible.
Companies looking towards advanced analytics for fraud detection will need to make a number of decisions. They will need to optimise existing scenario threshold tuning, explore big data, develop and interpret machine learning models for fraud, discover relevant information in text data, and prioritise and auto-route alerts. There may be industry-specific decisions to make, too, such as automating damage analysis through image recognition in the insurance sector. By automating these areas, companies can both significantly reduce human effort – reducing costs – and improve their fraud detection and prevention.
Benefits of an analytical approach to fraud detection and prevention
Companies that are already using an analytical approach for fraud prevention have reported several important benefits. First, the quality of referrals for further investigation is better. Investigators also have a much clearer idea of why the referral has been made, which improves the efficiency of investigation. Analytics also improves investigation efficiency by reducing the number of both false positives (that is, alerts that turn out not to be fraud) and false negatives (failure to spot actual frauds). This improves customer experience and reduces risk to the company.
Analytics makes it possible to uncover complex or organised fraud that rules-based systems would miss. Companies can group together customers and accounts with similar behaviors, and then set risk-based thresholds appropriate for each scenario.
There are several sector-specific benefits too. For example, insurance firms can identify fraudulent claims faster to prevent improper payments from going out. Claims investigation is likely to be more consistent because claims are scored through technology, algorithms and analytics, rather than by people. Finally, it becomes possible to shorten the claims process through automated damage analysis. It is no wonder that organizations across a wide range of sectors are placing analytics at the heart of their anti-fraud strategy.
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