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Cyber Value-at-Risk Model: Quantifying the Value-at-Risk

Cyber Value-at-Risk Model: Quantifying the Value-at-Risk

Cybersecurity attacks are the new potent threat to businesses. Diligent professionals and big mouth board members have started reviewing their company’s cybersecurity frameworks, while establishing better security controls and discerning deeper insights about the business impact of cybersecurity attacks: what kind of risks are they exposed to? Are they expending too much and need to curtail down? What amount of risk can be reduced using the proposed info security budget? Cyber-insurance, will it fetch better results?

What objectives to secure with Cyber value-at-risk models?

This is the epic question that has triggered the development of Value-at-risk models, especially in the domain of information security. Also known as Cyber VaR, these models are a game-changer. They offer a sound base for quantification of information risk coupled with infusing discipline into the whole process.

Market Risk Analytics: What It is All About – @Dexlabanalytics.

The objective of VaR is:

  • To help risk professionals formulate the notion of cyber risk in plain financial language without using any technical jargons.
  • To enable business professionals achieve a standard balance between safeguarding an organization and running the business by making cost-effective decisions.

Enterprises powered by VaR models for cybersecurity make complicated decision-making as easy as pie. They trigger risk-related discussions, where risks become more consistent, and business-goal driven.

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling – @Dexlabanalytics.

What exactly is cyber VaR?

In the world of finance, value-at-risk modeling is the statistical methodology to appraise the level of financial risk that a firm is exposed to over a specific period of time.

The VaR is ascertained using these three variables:

  • The amount of conjectured loss
  • The probability of that amount of loss
  • The time frame

Probabilities are effective to evaluate likely losses from the cyber attacks during a specific time period. Top notch global organizations, like World Economic Forum and several regulatory bodies, like The Open Group are revolutionizing the concept of cyber VaR models.

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What is its benefit?

VaR was initially developed in 1990’s to boost the investment banking sector, wherein managers were to identify the risks that popped up daily in multiple market reports. From the name itself, you can understand, it is more likely a measurement tool to analyze the financial impact of risky events within a particular time frame.

The most beneficial effect of VaR is that it not only quantifies risk but also pens it down in economic terms that are easily understood by all. It also assists in mitigating long-term challenges by aggregating cyberrisk with various other operational risks within an enterprise risk management system.

Here’s All You Need to Know about DexLab Analytics’ Market Risk Modelling Live Demo Session – @Dexlabanalytics.

How to determine the value of cyber VaR?

 CISOs, Chief information security officers decipher what exactly VaR offers in terms of cyberrisk management. This hi-tech concept is too good to help with crucial decision-making, like addressing cyberrisk appetite and defining the optimal allocation of cyber risk management resources.

Market risk analytics is a new concept in the make. Many organizations have realized its crucial importance, while many are yet to decipher. For the best enterprise risk management certification, drop by DexLab Analytics. They are a leading economic capital model training institute offering state-of-the-art courses to the candidates.

 

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Here’s All You Need to Know about DexLab Analytics’ Market Risk Modelling Live Demo Session

DexLab Analytics brings Market Risk Modelling training to India. Internet has helped people become technology-driven. Digital transformation is evident all around us. No more, gaining knowledge is a task like moving mountains – right from the confinements of your home, you can now get access to a plethora of information and knowledge, thanks to online learning. Several professionals and students are opting for e-learning method of education, owing to its flexibility and ease of access. And India is not lagging behind in this. Several online classes and sessions are being organized by premier data science learning institutes in India, and DexLab Analytics is one of them. 

 
Here’s All You Need to Know about DexLab Analytics’ Market Risk Modelling Live Demo Session
 

DexLab Analytics is here with an intensive live demo session on Market Risk Modelling Online for free. The online workshop is taking place on 25th October, 2017 from 10:00PM IST onwards, and will solely focus on how Market Risk Analytics has grown to be the new in-demand analytics course for the financial sector. Our in-house trainers will extensively explain the nitty-gritty of MRM, including its importance, major components, and why is it a must-to-have skill for the future. The interested candidates are asked to register as soon as possible by penning down a mail to DexLab Analytics, mentioning they would attend the workshop on the specified date and time.

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Market Risk Analytics: What It is All About

Market Risk Analytics: What It is All About

With time, firms need more efficient, versatile and highly functional analytics tools to address new, complex issues related to market risk. Market risk analytics involve a comprehensive set of integrated, scalable and productive solutions for wide-range risk management across various verticals of asset classes.

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling – @Dexlabanalytics.

Why Risk Analytics?

Risk analytics basically help organizations realize the existence of risks lying under business activities – by facilitating enterprises to identify, determine and manage their company risk. In lieu of this, the pressing need for risk analytics is going to increase across industries in the coming few years. New developments, like real-time risk analytics, which is an advanced form of traditional risk analytics process that calculates risk on a real-time basis, are influencing the entire market, while accentuating its mitigating abilities.

DexLab Analytics Introduces Market Risk Analytics and Modelling Online Session – @Dexlabanalytics.

What the Course Offers?

Many top notch education-providing companies are now offering Market Risk Analytics and Modelling online course to better alleviate and handle risks. Increasing needs to address particular risk-induced challenges and excessive focus on the financial market sector is driving the risk analytics market in India. Hence, learning and honing your skills on market risk is indispensable – DexLab Analytics brings Predictive modelling of market risk using SAS to India. The course module will address key issues, like the different types of risks faced by banks, the 1990’s financial crisis, sources and scope of market risk, theoretical probability distributions, volatility forecasting and clustering models, value at Risk Modelling, quantitative models of market risk and description of key financial products.

Some of the most common types of risks that banks are exposed to are Credit risk, Market risk, Operational risk, Liquidity risk, Business risk, Reputational risk, Systemic risk and Moral hazard. All banks need to establish separate risk management departments to manage, monitor and mitigate such high-flying risks. The concept of probability distributions sheds light on investing options – stock returns are expected to be distributed normally, but the reality may vary. They are mostly used in risk management to determine the probability of an event as well as the proportion of losses that it would strike based on a distribution of historical returns. Clustering models is another branch of risk analytics that helps in identifying groups of similar records and marking the records in accordance to the group in which it belongs. These models are also known as unsupervised learning models. Apart from this, other valuable concepts will be addressed during the online live sessions.

Closing Thoughts

Emergence of real time risk analytics is boosting the market of risk analytics. Technology being the driving factor for real-time analysis trades data to the organizations to balance market volatility. Leading service providers are on their quest to design and develop dynamically configurable risk analytics frameworks for clients. And why not, risk analytics boasts of widespread applications, starting from fraud detection to liquidity risk analysis, credit risk management and product portfolio management – various industries are nowadays looking up to market risk analytics, including banking, financial services, government, healthcare, insurance, manufacturing, transportation and logistics, consumer goods and retail, energy and utilities, telecommunication and information technology (IT), media and entertainment, and many others.

Reach us at DexLab Analytics for over-the-top SAS risk management certification course. Their courses are truly remarkable and perfect to take a step into the world of analytics.

 

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To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling

We are back again with some great news! Technology enthusiasts and hardcore industry professionals got another reason to cheer for DexLab Analytics, as we feel extremely delighted to announce our new Market Risk Analytics and Modelling online live sessions. We welcome hundreds and thousands of young, aspiring data enthusiasts from various parts of the country who are driven by hunger, passion and robust dreams of a data-friendly future to get enrolled in our online course on Market Risk Analytics using SAS. In our quest for expanding our horizons, these types of analytics course play a significant role.

 
A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling
 

Recently, Market Risk Analytics have gained a lot of prominence – a lot of tech pundits and industry practitioners have repeatedly emphasized on the importance of having sound market risk management policies and strong internal controls. Especially, since the global financial crisis, the critical aspect of risk management analytic has doubled.

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Success factors for Business Intelligence program

Success factors for Business Intelligence program

To implement a successful Business Intelligence program, one needs to understand the dimensions that are critical to success of the BI program. Here we will discuss six critical success factors for the BI program.

Critical Dimension 1 – Strong Executive Support

If there is one dimension or critical attribute that has major influence on successfully implementing the BI program would be strong executive support. If there is any lack of enthusiasm at the top will filter downwards. A key component of obtaining strong executive support is a convincing and detailed business case for BI.

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Critical Dimension 2 – Key Stakeholder Identification

Early identification and prioritisation of the key stakeholders are crucial. If we do not know who will benefit from a BI solution, it is unlikely that we can persuade anyone that is in their best interest to support the BI initiative.

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Critical Dimension 3 – Creation of Business Intelligence Competency Center (BICC) Many organizations have created a separate BICC to manage the lifecycle of analytics processes. Organizations must keep in mind while creating a BICC is that the need of Business Intelligence. They should ask all the strategic and tactical question before creating a BICC. Some of the key objective of BICC shall be

 

  • Maximise the efficiency, deployment and quality of BI across all lines of business.
  • Deliver more value at less cost and in less time through more successful BI deployments.
Also read: Business Intelligence: Now Every Person Can Use Data to Make Better Decisions

Critical Dimension 4 – Clear Outcome Identification

This dimension determines what outcomes the organization desires, and whether they are tactical or strategic.

  • Knowledge – What knowledge is needed for desired outcomes and where is it?
  • Information – What information structures can be identified from knowledge gathering and how can these same structures be beneficial.
  • Data – What sources of raw data are needed to populate the information structures?

Pursuing the answers to these questions requires both logic and creativity. We also need specific information at various steps in the BI process.

Also read: Trends to Watch Out – Global Self-service Business Intelligence (BI) Market 2017

Critical Dimension 5 – Integrating CSFs (Critical Success Factors) and KPIs to Business Drivers

Many business initiatives aim to obtain benefits – greater efficiency, quicker access to information that are hard to quantify. We can easily accept that greater efficiency is a good thing, but trying to quantify its precise cash value to the organization can be a challenge. These benefits are essentially intangible but need to be measured.

Therefore, when identifying these key values, they can be classified as “driving” strategy, organization or operations.

Strategic Drivers Influence:
  • Market attractiveness
  • Competitive strengths
  • Market share
Organisational Drivers Influence:
  • Culture
  • Training and development
Operational Drivers Influence:
  • Customer satisfaction
  • Product Excellence
Also read: Role of R In Business Intelligence

Critical Dimension 6 – Analytics Awareness

Organizations have a tendency to measure what is easy to measure – internal transactional data. Extending the sensitivity of the organization to external and internal data presents a fuller picture to decision makers of the organization and the competitive environment. If measures are appropriate, the organisation can start to improve the processes.

When the above mentioned six critical dimension of BI solution are place. Organizations can benefit the value from the BI solutions are exponential in manner.

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For better implementation of BI Program, why not take up effective market risk courses offered by DexLab Analytics! Market Risk Analytics is a growing field of study; for more details visit the site.

 

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Banking Business and Banking Instruments-3: Mortgages

How to Leverage AI Strategy in Business?
 

In this blog we discuss the final banking instrument- Mortgages, for which models are developed extensively. A mortgage is a debt instrument, secured by the collateral of specified real estate property that the borrower is obliged to pay back with a pre-determined set of payments. Mortgages are used by individuals and businesses to make large real estate purchases without paying the entire value of the purchase upfront.

 

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Mortgages are mainly of two types: (a) Traditional Mortgages (b) Adjusted Rate Mortgages.

 

Traditional Mortgage is a fixed rate mortgage, where the borrower pays the same a fixed rate of interest for the life of the mortgage. The monthly principal and the interest payments never change from the first payment to the last. Most fixed rate mortgages have a 15-30 year term. If the market interest rate rises, the borrowers’ payment does not change. If the market interest rate drops significantly, the borrower may secure the lower rate by re-financing the mortgage.

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