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REMEMBERING THE PAST TO MAKE THE FUTURE BETTER

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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.

Enhancing analysis

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.

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Finance

HOW FINANCIAL ORGANIZATIONS CAN PROTECT THEIR DATA

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Yuval Wollman, President, CyberProof and Chief Cyber Officer, UST

 

Top executives from Wall Street’s largest banks pinpointed cybersecurity as the greatest threat to America’s financial system, at a Congressional hearing that took place in May.

The concern of financial industry leaders with cyber-attacks is neither surprising, nor new. The attraction of cybercriminals to banks and other financial institutions makes sense, given the fact that the financial sector functions as gatekeepers – not just of financial assets, but also of valuable Personally identifiable information (PII).

Threat actors are attracted to attack financial institutions to earn a profit through increasingly sophisticated attacks that range from ransomware attacks to identity theft. But while the threat continues to grow, there is much that can be done to mitigate the risks.

 

The Downsides of Digital Banking

The number of attacks on financial institutions increased sharply in the last two years due to the upheavals wrought by COVID-19, which prompted a dramatic rise in the number of online transactions.

With so much of today’s financial transactions done on both web and mobile devices, threat actors have more opportunities than ever before. Take, for example, the growing importance of Man in the Middle (MITM) Attacks, which impersonate another party online and give criminals access to personal data, passwords, and banking details.

With the widespread adoption of digital banking, consumers have become increasingly worried about cyber-attack. As a result, there’s growing demand to create better consumer protection laws that respond to the rapidly evolving technology. The U.S. Federal Trade Commission (FTC), for example, recently strengthened security safeguards for consumer financial information.

 

It’s Not “Just” About the Money

Financial organizations are at risk not just from threat actors looking for profit, but also from nation-states and hacktivists acting out of idealistic motives or as a means of achieving specific political ends.

The most famous examples of this type of attack include Russia’s 2016 attack on Ukraine’s electric grid and North Korea’s 2017 attack on Britain’s National Health Service.

Because of the extent of the damage that this type of attack could cause, NATO established cyberspace as the “fifth domain of warfare” in 2016. It developed a definition of when foreign factions are banned from attacking financial institutions, due to the fear that this type of attack could directly lead to a country’s destabilization.

 

Recognizing Risk Factors

The digital transformation of financial services helps banks and other financial institutions provide more a more convenient customer experience.

And while significant customer demand has led many banks to implement changes such as the transition from legacy to cloud-based solutions, these shifts also have the potential to create additional security risks.

For example, if we’re talking specifically about cloud migration, there’s need for additional security layers to protect organizations working with public cloud providers from the range of attacks targeting the financial sector: ransomware, account takeover, data theft and manipulation, phishing attacks, identity theft, and more.

Another example is the extensive use of third-party vendors, which has increased the risk of attack for organizations in the financial sector. Because third-party vendors enlarge the attack surface, they create more entry points to the system and make it harder to protect customer data.

 

Accelerating Detection & Response

By adopting an agile approach that supports continuous improvement, financial organizations can facilitate proactive identification of evolving threats and vulnerabilities in the wild. More specifically, by placing an emphasis on use case optimization – which starts by mapping out an organization’s threat detection gaps to a framework such as MITRE ATT&CK – enterprises can prioritize threats and invest their time and resources in mitigating risk more effectively.

For organizations transitioning to the cloud, what’s key is managing the migration process in a way that provides optimal visibility in the cloud and supports ongoing optimization at the enterprise level. Digital playbooks are a crucial tool in providing improved detection and response, creating automated or guided responses that allow faster, more effective, collaborative action.

The development and regular review of incident response plans similarly allows for efficient response in emergency situations and helps reduce the business impact of cyber-attacks.

 

Targeted Threat Intelligence

Threat intelligence that’s tailored to the financial services sector is another key component of timely detection and response. By working with expert Cyber Threat Intelligence (CTI) services, organizations can obtain up-to-date information about industry-specific threats in real time – information that is a highly valuable tool in strengthening the defense of an enterprise.

 

Cyber Hygiene

Employees make mistakes; after all, it’s only human. But these errors can lead to massive data breaches. For example, when someone clicks on a phishing email or leaves passwords for a company computer on a slip of paper that’s easily seen by the wrong person, the damage can be astronomical.

Providing regular cybersecurity training programs for employees can help minimize the risk of an accidental or careless action leading to cyber-attack. To be effective, training programs should not only explain how to spot cybersecurity risks like phishing emails but should also discuss how and where it’s safe to access company information.

Aside from employee training, there are fundamental cybersecurity-related decisions that should be implemented at the enterprise level such as Zero Trust, DevSecOps, and multi-factor authentication (MFA). From a policy perspective, for example, it’s crucial to enforce MFA for all applications. Moreover, technology-related vulnerabilities can be minimized through frequent patching and updates for systems. Audits, as well as vulnerability and penetration tests, must be conducted regularly.

 

For the Financial Sector, “Best Practices” are Key

With the growth in number and complexity of cybersecurity attacks on financial organizations and the increased risk of nation-state attacks, proactively approaching the question of cybersecurity and implementing “best practices” makes the difference in reducing the degree of risk to an enterprise.

By modernizing the SOC with a carefully navigated migration to the cloud, adopting continuous improvement of use cases and the development of digital playbooks that improve detection and response – as well as by leveraging targeted threat intelligence and maintaining strong cyber hygiene – enterprises can put themselves in a stronger position to minimize the potential business impact of a cyber-attack on their organizations.

 

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Top 10

IF IT’S A LOSS, YOU’RE TOO LATE – WHY THE INSURANCE INDUSTRY NEEDS TO FOCUS ON FIRST NOTIFICATION OF RISK

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Simon Dicks, Insurance Channel Manager EMEA, Lytx

 

Insuring commercial fleets can be an expensive business. Average repair costs have increased by up to 40% in the past 8 years and disputes about who was responsible can drive up expenditure for both fleets and insurers.

Part of the problem is that the insurance industry hasn’t had the tools to forecast costs and premiums accurately enough in this sector. Underwriting decisions are still made in the same way they always have been, by looking back at historical data from previous years. This approach simply isn’t giving insurance companies an accurate indication of potential risk – or a proper indication of the impact of driver behaviour.

Technology is helping insurers to an extent by providing information about First Notification of Loss (FNOL) – automatically sending notifications when unusual G-force readings are captured within a black box tracking device as a result of sudden braking or impact. This is good, but far better is the ability to use proactive technology to detect when an incident is at risk of occurring and when a driver is distracted.

The only way to address this is to put a highly accurate level of camera technology both inside and outside cabs, supported by sophisticated technologies such as Machine Vision (ML) and Artificial Intelligence (AI). This way, we can see not just that an incident has happened, but why it happened. What’s more, we can assess risk before an accident happens at all and prevent it happening in the first place. We call this First Notification of Risk (FNOR) – and it’s a whole step up from FNOL.

Machine Vision scans the internal and external environment of the vehicle to identify distracted driving behaviours such as mobile phone use, eating, drinking, smoking, inattentive behaviour or failure to wear a seatbelt. AI, comparing the behaviour against a vast bank of accumulated data, is then able to determine the riskiness of that situation and whether it needs to be flagged to the fleet manager, driver, or insurer via a short video clip. The big difference in this approach is that it’s proactive, not reactive. For the first time, fleets and insurers can identify adverse driving and distracted driving in real-time for the first time.

This includes the ability to alert drivers of any momentary slip-ups or distracted behaviours. Using the same technology, drivers will receive an audio or visual alert to help keep them on track and to lessen the likelihood of a moment’s distraction becoming anything more.

When insurers have access to these insights, they can also start to see patterns from the data over time. For example, a fleet manager might start to see that there’s a peak in risky driving behaviours on a Friday afternoon when lots of drivers are rushing to finish for the weekend. As a result, they may decide to spread the shifts differently so as to avoid that pattern of behaviour.

When insurers are only looking at FNOL, it’s already too late. A driver could be unthinkingly driving whilst smoking, on their phone, and nobody would never know. Whereas with FNOR, both managers and insurers are provided with insights that remove the guesswork, and underwriters have the information they need to assess risk with far greater precision.

There’s still a long way to go in making the move towards FNOR. With so many different companies selling cameras and telematics systems and producing information in hundreds of different formats, claims data will have to be standardised before the sector can really transform. However, by starting to embrace ideas like FNOR, the industry can move towards a solution that saves them time, money and lives.

To find out more, visit  www.lytx.com/FNOR

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