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Wealth Management

ACHIEVING COMPETITIVE ADVANTAGE IN INSURANCE

– Tony Farnfield, Partner at BearingPoint

 

“Loyalty that is bought with money, may be overcome by money”. Seneca’s famous proverb might be a few thousand years old but couldn’t be more current and relevant. When looking at consumer behaviour over recent years across industries and product categories, there is a common trend – brand loyalty is less relevant; consumers are becoming ruthlessly focussed on price rather than brand. Convenience when switching, which used to be a hurdle, is not considered an issue anymore with the advent – and now dominance – of marketplaces and price comparison platforms.

The insurance industry is a good testament to this. Insurance customers used to pay the price for remaining loyal to one provider, with new customers getting the best deals – commonly referred to by the term “loyalty penalty”. Switching providers, however, was often arduous and involved a fair amount of research and deal comparison that not many customers were willing to undergo. The introduction of price comparison sites offered customers a quick and easy way to compare deals and switch, and has now become the mainstream option when buying or renewing policies.

 

The FCA and the “loyalty penalty”….

The so-called loyalty penalty has recently been under scrutiny by the FCA. The regulator found “hidden discrepancies” in the amounts customers were paying for a service, and warned general insurers that it “will not hesitate to intervene” if firms fail to meet their obligations to customers. Whilst we were still waiting for the official FCA investigation results on market practices and fairness, some insurance providers were good to react. For instance, Saga is now offering a three-year price promise on car and home insurance, while Aviva has introduced AvivaPlus which offers a renewal price guarantee.

 

How can insurers respond to fierce competition and change  

In this environment of fierce competition and brutal pricing, insurers are forced to constantly innovate, reduce bottom line, adapt, and respond quickly to a changing economy and society.

Bottom line requires rationalisation and standardisation. For many years, identifying process improvements has traditionally been a well-proven but heavy, slow, and manual process. However, advances in technology and the advent of process mining tools address many of the legacy challenges of process improvement, benefit realisation, and sustainable improvements. It allows clients to link core systems & technology through API’s to visualise live end to end process to understand critical issues in performance, variation, and compliance.

Typically, there will be a common path that is frequently used, but not always the most efficient. Within a matter of weeks, it provides a deep process analysis and clarity on potential automation and process improvements. Additionally, process mining delivers an enduring connection to the core systems and dynamically visualises the impact of change. Be it new customer onboarding, procure to pay, change of details, or new product development, process mining offers process transparency in its raw form. It also enables rapid standardisation which is essential for driving cost efficiency and offering the necessary room and platform for adapting, changing, and scaling.

When process mining addresses the need for rationalisation, standardisation is addressed with new technologies that offer configurable rules engines automating existing processes and avoiding lengthy approaches to change. These solutions offer rule-based modelling, expressed as configurable and repeatable rules within the application. Not only does this cut back operational effort but avoids the likelihood of manual errors and process related incidents.

 

The answer from a tech view

 One of the main drivers for adaptability and change is the ability to deliver scalable digital capabilities at a faster pace. Advanced analytics, IoT, and cognitive applications demand technology capabilities that are scalable and flexible. Cloud providers constantly evolve their capabilities and work with system integrators to create tailored industry solutions. Market participants can tap into powerful ecosystems that will provide them with the flexibility to make quick business decisions.

Making the most of cloud technologies requires robust medium and longer-term planning, especially when it comes to deciding which legacy systems to migrate to the cloud and when. Criticality and complexity should determine when to migrate to the cloud and the effort required to do so. For that reason, a phased cloud migration plan would act as the most effective way to manage change of this scale and to also allow the required room for the deployment of new applications.

In addition, by migrating legacy systems on Cloud not only gives flexibility but allows the organisation to maintain these at a fraction of the cost. With the introduction of new API platforms, migrating to the cloud is no longer onerous.

Elsewhere, blockchain has been used as a lever in the battle of reducing bottom line and responding to downward pricing pressures. Distributed Ledger Technology (DLT) and Blockchain has been the epicentre of insurers’ focus, mainly in understanding how this can be used to collaborate with competitors better and drive down costs. Proofs of concept have established the ability that DLT has, but only a few market players have gone past that stage. AIA in Hong Kong recently launched a blockchain-enabled bank assurance platform, and AXA in Europe is offering flight delay insurance cover through a blockchain platform. Whilst we won’t see immediate application of blockchain, the industry is set to undertake more meaningful and tangible blockchain initiatives that will completely change the scale and shape of insurance operations.

 

When product innovation is more than just a buzz word

 Flexibility should not only transcend in the tech stack that insurers should be using, but to product and policy development that responds to customer needs such as customisation, personalisation, and greater control and self-management. An example of this real-time, as and when needed self-managed coverage is Trōv. Trōv is an on-demand insurance agency that uses an application which allows customers to insure single items they purchased (e.g. cameras, tablets or other digital devices) with a coverage that can be activated and terminated as and when needed and can be switched on and off through the app. InsurTech innovators are looking to disrupt not only how policies are currently offered to consumers but also tapping into new niche markets, some of them not pre-existing.

Product development can also be accelerated by backing InsurTechs that do not face the usual policy and legacy burdens. Personal insurance has been the main focus of these companies, however it is expected that life insurance and commercial will soon be the target.

 

Time will set apart leaders from followers….

 All of the above are topics are often discussed within the insurance world, but the broader fundamental challenge lying ahead is how insurers will create the springboard and set themselves ready for adapting and keeping up with changing customer and wider societal shifts. The very blurring of the boundaries between industries owed to the sharing economy and the generation of vast real-time data, is set to create gigantic shifts presenting new market opportunities and threats.

Critically, insurers will need to get the basics right; embracing new technology as an enabler and designing services rather than products in a collaborative manner through the use of an ecosystem. These challenges are not set to become the industry norm soon, but it will all depend on who is the quickest to react first. Time is ticking away.

 

https://www.fca.org.uk/news/press-releases/insurance-firms-failing-consider-value-products-and-services-provided-consumers

 

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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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