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Wealth Management

THINGS TO CONSIDER BEFORE LEAVING A PROPERTY EMPTY

A recent study by HomeProtect on unoccupied homes has revealed that there are in excess of 200,000 properties across the UK sitting empty. This could be due to them being bought as an investment, used as a holiday home on AirBnB, or simply being left empty while they’re up for renovation or sale. Whatever the reason is for the property being vacant, there are a few things to consider when leaving a home unattended.

If you’re about to travel long-term, are closing your business or have renovation work planned, here are a few things you should do before leaving the property empty.

 

Upgrade your security

When a property is left empty for a prolonged period of time, it becomes vulnerable to thieves, vandals or even squatters, so it’s important to make sure that the building is as secure as possible. This may mean upgrading the locks on doors and windows around the property if the current ones are old or insubstantial. Grilles can also help to further secure windows, especially at ground level.

For high-risk areas, you may also want to think about installing some more advanced security equipment, such as a burglar alarm or CCTV system. Even when not active, CCTV cameras can work as a deterrent for potential intruders.

Inform a trusted neighbour

It’s worth informing a trusted neighbour and providing them with a spare key when you’re going to be leaving your property empty for a period of time, as they’ll be able to react much quicker in the case of an emergency than a friend who lives further away. They may also be willing to pop by to check on the property from time to time, to ensure everything is in order.

Adjust your central heating

If you’re going to be leaving your property unoccupied during cold weather, set the boiler at a low temperature for at least an hour at night to avoid your pipes freezing. Frozen pipes can be incredibly problematic, as not only will they leave your property without running water, but it can also lead to a pipe bursting. Burst pipes can cause flooding and further damage to the property as a result, in addition to steep water bills accounting for the amount of water lost during the leak.

Turn off the water and all electrical appliances

A further way to safeguard yourself from water damage is to turn the water supply off entirely at the mains. There will be no need for it to be on while the property is not in use, and this can prevent any issues occurring.

There’s no need to turn off the entire property’s energy supply, however. In fact, it helps to keep this on so that you can set timer lights around the home to give the impression of somebody being in. Instead, turn off all electrical appliances at the wall.

Keep the property well-maintained

Nothing will draw people’s attention more to the fact a property has been left vacant than a poorly maintained exterior. If you have a front garden, this will be the biggest giveaway. Making sure that the lawn is regularly mown, and overgrown weeds are trimmed back can help to give the impression that the property is occupied. The same goes for post gathering on the doorstep.

If you’re not able to visit the property on a regular basis to do this, it could be worth enlisting the help of a neighbour or a gardener to keep it looking presentable.

 

Remove all valuables

Even if you’ve worked on securing your property to the highest possible level, it’s still sensible to avoid leaving anything of value in the property. If possible, keep anything worth a lot of money or of sentimental value to you in an alternative secure location, such as in your current home, with somebody you trust or in professional storage.

Consider unoccupied home insurance

If your property is left unoccupied long-term, it’s likely that regular home insurers won’t be able to insure the property while it is empty, due to the increased risks that come with it being vacant. Before leaving the property, contact your current insurer to inform them of the change of circumstances as there is a chance they may be able to adapt your policy. If this isn’t the case, however, there are specialist home insurance companies that offer unoccupied home insurance to protect you from risks such as fire, vandalism and water damage.

Council tax

If your property is unoccupied you will usually still have to pay council tax, however, some councils offer a discount for empty properties. This varies between councils and the size of the discount is up to them to decide. Likewise, if your property is empty for over two years, you can be charged up to double your council tax.

 

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Wealth Management

COMBATING INSURANCE FRAUD WITH MACHINE LEARNING

By Georgios Kapetanvasileiou, Analytical Consultant at SAS

 

Most insurance companies depend on human expertise and business rules-based software to protect themselves from fraud. However, people move on. And the drive for digital transformation and process automation means data and scenarios change faster than you can update the rules.

Machine learning has the potential to allow insurers to move from the current state of “detect and react” to “predict and prevent.” It excels at automating the process of taking large volumes of data, analysing multiple fraud indicators in parallel – which taken individually may often be quite normal – and finding potential fraud. Generally, there are two ways to teach or train a machine learning algorithm, which depend on the available data: supervised and unsupervised learning.

 

Predictive modelling

In predictive modelling or supervised learning, algorithms make predictions based on a set of examples from historical data. You can present an algorithm with historical claims information and associated outcomes often called labelled data. It will attempt to identify the underlying patterns in fraudulent cases. Once the algorithm has been trained on past examples, you can use it to infer the probability of a new claim being fraudulent. AKSigorta Insurance is using advanced predictive modelling as part of its investigation process. The company has managed to increase its fraud detection rate by 66% and prevent fraud in real time.

There is a wide variety of predictive modelling algorithms to choose from, so users should take into account issues such as accuracy, interpretability, training time and ease of use. There is no single approach that works universally. Even experienced data scientists have to try different methods to find the right algorithm for a specific problem. It is, therefore, best to start simple and explore more advanced machine learning methodologies later. Decision trees, for example, are an excellent way to start exploring complex relationships within data. They are relatively easy to implement and fast to train on large volumes of data. More importantly, they are very easy to understand or interpret, and can be a good starting point for new business rules.

 

Other options for more accuracy

Decision trees can, however, become unstable over time. When accuracy becomes a priority, practitioners should look at other options. Support vector machines (SVMs) and neural networks are capable of learning complex class boundaries and generalise well to unseen cases. They have been extensively used for fraud detection. Tree-based algorithms, such as gradient boosting and random forests, have also become more popular in recent years. Ideally, analysts should try multiple approaches in parallel before deciding what works best.

Supervised learning is effective in identifying familiar cases of fraudulent activity but cannot uncover new patterns. Another challenge is the limited numbers of fraud examples with which to train the algorithm. Fraud is a relatively rare event, after all. The ratio between fraud and nonfraud cases can sometimes be as much as 1 to 10,000. This means that predictive algorithms tend to be overwhelmed by the sheer volume of nonfraud cases, and may miss the fraudulent ones. Labelling new data for training a model can also be time consuming and expensive.

 

Unsupervised learning

Unsupervised learning algorithms are trained against data with no historical labels. In other words, the algorithm is not given the answer or outcome beforehand. It is merely asked to explore the data and uncover any “interesting” structures within them. For example, given certain behavioural information, unsupervised learning algorithms can identify groups (or clusters) of customer transactions that appear similar. Anything that appears different or rare could be flagged as an anomaly (or an outlier) for further investigation.

Unsupervised learning methods can, therefore, identify both existing and new types of fraud. They are not restricted to predefined labels, so can quickly adapt to new and emerging patterns of dishonest behaviour. For example, a New Zealand health insurer used unsupervised learning methods to identify cases where practitioners were deliberately overcharging patients for a particular procedure or providing unnecessary treatment for certain diagnoses.

Unsupervised anomaly detection methods include univariate outlier analysis or clustering-based methods such as k-means. However, the recent move towards digitalisation means more data, at higher volumes, from a wider range of data sources. New algorithms, such as Support Vector Data Description, Isolation Forest or Autoencoders, have been introduced to address this. These may be a more efficient way of detecting anomalies and allow for faster reaction to new fraud.

 

Social network analysis

These methods are useful for identifying opportunistic fraud. However, many fraudsters today operate as part of professional, organised rings. Activity may include staged motor accidents to collect on premiums, ghost brokering, or collusion between patients and health practitioners to inflate claim amounts. These career fraudsters can repeatedly disguise their identities and evolve their way of operating over time.

Social network analysis is a tool for analysing and visually representing relationships between known entities. Examples of shared entities could be different applicants using the same telephone number or IP address, or a motor accident involving multiple people. Social network methods can automate the process of drawing connections from disparate data sources and visually representing them as a network. This significantly reduces the investigation time – in one case, from 10 days to just two hours. In the UK, a large P&C insurer made £7 million savings per annum by uncovering groups of collaborating fraudsters using network analytics.

 

A hybrid approach

No single technique, however, is capable of systematically identifying all complex fraud schemes. Instead, insurers need to combine sophisticated business rules and advanced machine learning approaches. This will allow them to cast the net wide, but improve accuracy and reduce false positives, making fraud detection more efficient.

 

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Wealth Management

FALLING INSURANCE PREMIUMS DON’T NEED TO BE A CAR CRASH FOR INSURANCE COMPANIES

By Manan Sagar, Chief Technology Officer for Insurance at Fujitsu UK&I

 

It’s no secret that buying car insurance can be a frustrating experience. Probably one of the most common complaints is the lack of accuracy around pricing and the increased charge for customers’ ongoing loyalty to a vendor.

Therefore, it’s been a welcome relief for customers as the price of premiums continues to drop within the UK car insurance industry; decreasing by 1% in the third quarter. This has been pushed down by uncertainty from the personal injury discount rate change in July 2019 and the market watchdog’s interim report on general insurance pricing practices.

However, this is less exciting for insurance companies. It’s a worrying sign for the way the industry currently works, and this warning should be taken as an indicator that it’s time to change lanes in the way we approach pricing practices in UK insurance.

 

Out with the old, in with the accurate

The hinderance to customer satisfaction for insurance companies has been the result of generic circumstantial ‘repair and replace’ pricing systems. Insurance premiums in this archaic model are based on historical data which makes projections about potential outcomes based on trends. This often causes specific groups of people – such as young adults – to be penalised as underwriters and actuaries use past data sets to look for loss patterns and make projections about future outcomes.

As a result, this has created a conception that insurance providers have unfair and inaccurate prices, and unfortunately digital transformation in such a model is limited to enabling “easier” purchase and claims processes.

But now technology is giving insurance companies the opportunity to alter this model. Traditionally prices are formulated through a calculation of stakeholders: the client + the broker + the insurer. But now the addition of technology providers has increased insurers’ capabilities to process, analyse and use data to provide more tailored premiums and accurate results. In other words, technology is enabling insurers to become a force for good, and rather than just reimbursing for damages and losses, to predict and prevent these from happening. In the grand scheme of things, this would benefit not only the industry, but society as a whole.

For example, rather than filling out generic questionnaires to conclude a pre-determined price, technology will be able to look at current and real-time data to consider the customer’s behaviour before establishing a price point. This means insurance companies will have capabilities to offer more bespoke policies that better reflect their customers, their lifestyle and their needs. In some cases this precision will reduce insurance costs on an on-going basis – the benefit being an increase in customer satisfaction and retention.

This is all possible thanks to technology that already exists. Powerful analytics tools and the Internet of Things (IOT) has opened the door for insurers to provide “smart policies”, and make dynamic projections about future outcomes, calculating pricing models based on this new approach. For car insurance, this means that data can provide insights not just into when and where, but also how the customer drives – ultimately promoting safety on roads. Some car manufacturers like Tesla have already spotted the opportunity and have, earlier this year, announced that they will be offering insurance to their car owners in the US at a 30% reduction.

 

The insurance industry has its brakes on

Increases in customer satisfaction and customer retention are no doubt the goals of every insurance company, and achieving this through digitisation seems like a promising offer.

However, it’s not that simple.

Insurance is an age-old industry that is deeply rooted in the traditional business model it currently operates in. Most of these companies are also big, which makes a change of this nature more of an upheaval than an agile step-change.

This has made actions within the digital transformation process, such as implementing automation to harness the power of “data”, extremely slow for some organisations. But insurance companies need to think how they can start adapting to the new customer demands, and how they can revolutionise their own industry and stay relevant.

To get in gear, insurance companies need to challenge their traditional mindset and see technology as a supplement to their services.  Ultimately, to thrive in today’s market, insurers will have to shift their focus on prevention, and “smart policies”. Soon enough, policyholders – whether the public or businesses – will no longer accept the old way of doing things.

The UK car insurance industry is at a cross roads. And how well insurance companies use technology will determine whether they go down the route of futureproofed customer experience, or a dead-end.

 

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