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Insurers get personal to address the changing industry and customer landscape

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Udi Ziv, CEO, Earnix

 

While the insurance market has historically been very stable, with a tolerance for only gradual change, this is no longer the case. Changes in technology, consumer behavior, and claims cost demand faster evolution. It’s clear that the insurance industry is at a crucial inflection point.

Even though widespread adoption of Usage Based-Insurance (UBI) was not quickly realized soon after its introduction decades ago in the 1990s, it was the first big shift in the industry. When a mature and famously conservative market starts to undergo change, the instinct is to hold tight, not march toward new technologies or processes.

After many months of remote working and quarantining, consumers started questioning the need to pay the same amount for car insurance when they were not driving nearly as often. When insurers started seeking ways to satisfy the changing demands of consumers, it resulted in an ineffective and incremental re-engineering of how the insurance sector already does business.

With that now in place, 2022 is the year for carriers to address the connection between business processes and the technology. It doesn’t matter how smart, hardworking, and innovative a team of actuaries, underwriters, or data scientists is if they are challenged with poor processes or legacy technology that can’t keep up with market changes and customer demands. Deployment of dynamic pricing and personalized solutions is the key.

 

Right time, right place, right policy

A hyper-personalized, ready-when-you-are Netflix type experience has become the standard expectation for everything – even insurance.

To address this, carriers must find a way to marry the strength of their deep knowledge and expertise with today’s technology to ensure they provide fully personalized dynamic offerings that customers require. A key step forward will be deploying advanced analytical tools, business processes and technological infrastructure to respond swiftly to market changes. This will provide improved understanding of customers’ goals and offer the capability to quickly and efficiently develop the right insurance offer to meet their needs, at precisely the right time.

To achieve this, insurers must quickly model complex scenarios to improve decision-making, and develop the perfect product at the perfect price.

 

Insurers innovate to accelerate products and pricing

Agility is precisely what insurers need today to become faster, leaner, and more effective in addressing current and future market and consumer changes. IT and Business functions are being challenged to create highly personalized offers more efficiently with fewer resources. According to a McKinsey study an agile approach will allow insurers to launch new products or update new pricing models up to five times faster and boost customer experience. Many insurers are limited by legacy IT systems, which can stifle innovation. However, technological, and operational challenges can be overcome by adopting composable and agile technologies, that sit on top of the legacy IT systems, and are designed to digitally transform businesses. This enables insurers to model prices and deploy policies in a much shorter timeframe with significantly lower investment.

The right process and technology are key for innovation. Therefore, it is crucial carriers address the connection between business processes and the technology that supports them. According to Strategy Meets Action “insurers will need modern platforms that can plug and play with different channels and absorb new data sources”.

The opportunities already exist with instantaneous access to better, more comprehensive data coming from devices providing IoT and telematics data. But an IoT device, or any data source for that matter, cannot provide value on its own. It must be incorporated into business processes to provide sources of innovation in pricing, rating, etc. Automation can also accelerate data collection and data processing, speeding up processes, eliminating errors and reducing time to market to weeks and even days. For example, when it comes to UBI and pricing processes, insurers are running telematic data with machine learning and artificial intelligence models to offer personalized policies instantaneously.

 

A new policy for success

Analytics offers a powerful competitive advantage in modeling highly complicated scenarios to stay a step ahead of changing market dynamics and customer needs. Depth of insight and automation via process systemization gives pricing and product professionals the power to make decisions quickly. With the ability to make pricing and product business decisions quickly, efficiently, and accurately, changes that need to be deployed, whether price or product related, can be done rapidly – improving overall organizational agility.

The past is the past, today we’re seeing insurers implementing intelligent, composable, and agile technologies. As they do, they are reaping the benefits of developing and deploying new, highly targeted, personalized offers – faster than ever. This bridges the gap between what insurance companies need for their book of business and what consumers demand.

 

 

 

 

 

 

 

 

Business

Netflix-style ransomware makes your organisation’s data the prize in a dark subscription economy

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By John Davis, UK & Ireland Director, SANS Institute.

Today’s subscription economy makes accessing nearly any service as easy as hitting enter. The same model has now entered the dark web. The same Netflix-style instant-access menu is now part and parcel of the online criminal’s lifestyle. Ransomware-as-a-Service (Raas) is opening up the hacking talent pool, giving amateurs access to sophisticated ransomware toolkits – a plug and play option that has seen hackers run rampant.

Once ad hoc acts were committed by hackers using simple phishing attacks to gain entry. They have now become complex and targeted, using the latest purchasable ‘toolkits’ allowing any dark actor to get a slice of the ransomware pie by simply subscribing to a ransomware toolkit.

A growing proportion of ransomware attacks are being carried out using the RaaS model and it is clear that the toolkit creators and their customers are cashing in. So, what can organisations do to ensure they aren’t victims of these cookie-cutter attacks?

Sophisticated criminal service providers

RaaS providers sell their services using sophisticated business and marketing strategies to appeal to hackers wanting maximum return for minimal effort. These providers operate in the grey zone between legal and illegal, marketing themselves on the dark web; they appeal to criminal clients interested in purchasing a single attack or even maintaining a retainer-style relationship for ongoing attacks. The client can pay a monthly fee for advice and assistance, usually in cryptocurrency. Like the best subscription providers, this can even include around-the-clock support that covers technical aspects of an attack and matters such as negotiations with a victim. The client also may share a portion of any payment extracted from a victim with the RaaS provider.

John Davis

The RaaS model makes attribution of attack difficult but not impossible. In some cases, there are elements, such as snippets of malicious code, that can help authorities trace an attack back to a perpetrator known to be running a RaaS operation, and attackers, when caught, may give up relevant details.

RaaS providers sell expertise and prefer keeping the client at arm’s length to avoid detection and prosecution. Indeed, it can be harder to prosecute RaaS than conventional ransomware attacks because there are more moving parts, and they may move in several jurisdictions governed by competing laws and authorities. The advent of RaaS and ransomware, generally, have increased the impetus to harmonise laws and foster law enforcement cooperation in this area.

Cloud gives and takes

RaaS providers are taking advantage of IaaS (Infrastructre-as-a-Service) and the economics of cloud-based computing and storage the same way legitimate businesses do. The participation of most IaaS companies is usually unintentional. The desire to maintain their clients’ data security and their own reputations makes legitimate IaaS providers a formidable ally in the war against ransomware and RaaS providers.

Just as in legal, and commercial undertakings, ransomware skills are continually honed, and standards are elevated through competition. As RaaS providers raise their game, the stakes for potential targets are also raised. The threats they face will be more acute, at least until cybersecurity professionals and law enforcement raise their game and improve their methods for combating threats. Similarly, organisations that find themselves on the wrong end of an attack are not helpless.

Resisting the rise of RaaS

As the risk of RaaS attacks increases The Centre for Internet Security has shared a series of Critical Security Controls that should go a long way to fending off RaaS and other types of ransomware attacks and to mitigating damage should one occur. These include:

  • Taking inventory of all electronic assets. Take stock of all fixed, portable, or mobile devices that can connect to your technology platforms. This will allow you to spot any unauthorised or unmonitored devices and remove them or make them secure. Do the same with software assets, including operating systems, programs, and apps. Review credentials and permissions for each employee, and limit access to files, folders, apps, programs, and external websites to those that are appropriate for their duties.
  • Monitoring access points. Your infrastructure is most at risk of a breach at the points where it meets the outside world. Enhance malware detection and defence techniques, focusing particularly on these points and the means through which a breach is most likely to occur, such as web links and emails. This, plus a rigorous permissions regime, could prevent a considerable expenditure of time and money if Dave from accounting decides to click on the wrong Pornhub banner ad when he is supposed to be processing invoices.
  • Anticipating vulnerabilities and responding to threats. Use industry resources to stay aware of the latest threats and ensure that your operating system and other software are updated, and patches applied when available. The most significant vulnerability is reusable passwords. Most financial services now require Multi-factor Authentication for login. Using this simple form of MFA stymies over 99% of all phishing attacks.
  • Making the most of your human assets. If properly trained your employees can be an additional factor to aid in thwarting attackers. Their understanding of and reaction to ransomware attacks and other threats should be evaluated and sharpened through the development of security awareness programs that work to change user behaviour when presented with a bogus email or web page. There should be simulations of threat scenarios to put these procedures and preparations to the test.
  • Investing in your security team’s skills and tools. There is a lot of concern about a “cybersecurity staffing shortfall,” but successful security organisations have found that there is more of a skills gap than a headcount shortfall. By upskilling security analysts in critical areas such as cloud security, purple teaming, and machine learning, you get a double benefit: the need for additional staff is reduced, and surveys show that security staff that get regular training are less likely to jump to another company for a salary increase.

Continual proactive protection

The RaaS model only increases the likelihood of an attack, making it a feasible option to a broader population of bad actors. There is now no choice but to take proactive steps to protect against this genuine threat, continually evaluating the threat backdrop and monitoring systems and people. When it comes to a potentially business-breaking attack, it’s increasingly not a question of if but when.

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How Big Data is Transforming Bilateral Trading

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By Stuart Smith, Co-Head Business Development – Data & Risk

 

Since its inception, Big Data has been an important part of how firms have identified and constructed quantitative trading strategies with hedge funds depending more on quant strategies which rely heavily on big data driven analytics.

As big data technology continues to move from being a specialised technical capability to being a commoditised capability available on a range of easily consumed technology platforms, its use within the financial derivatives will continue to increase beyond the initial quantitative driven capabilities.

At the same time, the number and range of available data sources is increasing rapidly. Whether it’s the increase in alternative data sets or new technology enabling firms to simply keep more of the data they have been creating, the volume of data available is increasing dramatically.

 

Big Data in Risk Management

Risk Management has always had requirements which have driven a close collaboration between business and technology to make available risk analytics useful for the business to make better decisions. As technology becomes more advanced, the metrics available continue to improve as well. This is typically because many risk metrics require high numbers of scenarios and valuations to correctly identify risks in multiple scenarios. To maintain flexibility, this has led to an explosion of data to manage. Firms are increasingly keeping all this data available which can run into many Terabytes (TBs), much of which needs to be ‘In Memory’ to make it accessible to analysts.

Stuart Smith

To achieve this big-data, technology is critical to allow firms to move large volumes of data quickly and easily from affordable long-term storage into high performance in-memory analytics. Big Data technology is ideal for this type of problem to enable large volumes of data to be recalled from across multiple stores and appropriately aggregated or filtered based on the analysis which users are requesting. Whereas in the past, analysts would have to accept that data outside of the last 3-5 days is only available in a summarised format, they can now expect that the data can be re-hydrated quickly and easily from cloud data stores and available to them in an easy-to-consume web interface.

This can enable much more dynamic types of analysis, for example where a new risk is identified, through analysis of a recent data set it’s now possible to find a long history of that risk, whereas previously it would have been lost through summarisation and fixed reporting processes.

 

Collaborative Data Sets

More big data stores are being created as the industry becomes more collaborative and uses increasing numbers of fintech solutions and platforms. With this change come new ways to analyse data and provide new insights.

For instance, through the automation of collateral exchange, an historical store of margin calls, payments and disputes has been created. This history provides a resource for banks to understand their performance in accurately issuing and making margin calls based on derivatives and compare their performance to that of the industry as a whole. The example below shows how a firm can be benchmarked while holding other institutions data private.

These types of analysis are new and could not be delivered without the centralised collaborative data model. It can prove to be instrumental in improving firms’ overall operational efficiency and client service.

It also provides an opportunity for Machine Learning techniques, based on big data sets, to analyse and predict payments requests which are likely to be disputed and potentially identify causes before an actual dispute is even raised. This type of ‘self-healing’ process can only be enabled by a large history of data through which algorithms can be trained.

In the case of Initial Margin (IM) calculated by ISDA SIMM* a new set of challenges have been introduced through having a two-sided risk calculation as part of the process of deriving payment information. This adds another level of complexity to the resolving of disputes; however, the potential offered by having large volumes of data opens up new options on how this challenge could be solved. The long history of Common Risk Interchange Format (CRIF)** data provides a long-term view of the sensitivities for most OTC derivatives, which can enable firms to identify basic issues like stale market data day over day. However, as with most detailed analysis differences in models, they can also be identified through looking at differences over long periods of time. Identification of these types of model discrepancies can help firms to be more proactive about reviewing their modelling deficiencies to ensure that differences don’t lead to disputes.

 

Looking ahead

The sheer volume of data can be an industry-wide challenge with firms having to manage disparate, needlessly duplicated and ultimately overwhelming information. Creation of an industry standard for reporting and analytics is, therefore, crucial to enable firms get clarity and valuable insights from the masses of data and centralise the information as a single data layer. Acadia has designed Data Exploration (DX) suite to be one-of-its-kind big data analytics platform to help sell-side, buy-side and fund administrators see its market positioning, trends and analysis of industrywide metrics.

The impact of big data will only grow and the industry is left with no choice than to evolve the use of technology, whether that is to drive quant strategies for hedge funds, more dynamic forms of risk management or larger shared industry data sets. All of these applications rely on underlying big data technology platforms to provide distributed analysis capabilities. As these capabilities continue to develop so will the types of analysis which are available to firms.

*The ISDA Standard Initial Margin Model (ISDA SIMM™) is a common methodology for calculating initial margin for non-centrally cleared derivatives, developed as part of ISDA’s Working Group on Margin Requirements (WGMR) to help market participants meet the BCBS-IOSCO margin framework for non-cleared derivatives.

** The CRIF file (Common Risk Interchange Format) is the industry template used to hold and exchange sensitivity data. ISDA’s calculation specifications are used to produce Delta, Vega and Curvature sensitivity numbers at Risk Factor-level

 

 

 

 

 

 

 

 

 

 

 

 

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