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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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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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What the Future Holds for Risk Management in Banking

The past decade saw some impressive changes brought into the aorta of risk management. And the change is showing no signs of slowing down, now.

 
What the Future Holds for Risk Management in Banking
 

In order to keep pace with the changing times, you need to get to the crux of these five trends that are shaping the role of risk management in banking sector: Continue reading “What the Future Holds for Risk Management in Banking”

Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

In the aftermath of the Great Recession and the credit crunch that followed, the financial institutions across the globe are facing an increasing amount of regulatory scrutiny, and for good reasons. Regulatory efforts necessitate new, in-depth analysis, reports, templates and assessments from financial institutions in the form of call reports and loan loss summaries, all of which ensures better accountability, thus helping business initiatives.

Help yourself with credit risk analysis course online at DexLab Analytics.

Also, regulators have started asking for more transparency. Their main objective is to know that a bank possesses thorough knowledge about its customers and their related credit risk. Moreover, new Basel III regulations entail an even bigger regulatory burden for the banks.

What are the challenges faced by CRM Managers?

  • Sloppy data management – Unable to access the data when it’s needed the most, due to inefficient data management issues.
  • No group-wide risk modeling framework – Banks need strong, meaningful risk measures to get a larger picture of the problem. Without these frameworks, it becomes really difficult to get to the tip of the problem.
  • Too much duplication of effort – As analysts cannot alter model parameters they face too much duplication of work, which results in constant rework. This may negatively affect a bank’s efficiency ratio.
  • Inefficient risk toolsBanks need to have a potent risk solution, otherwise how can they identify portfolio concentrations or re-grade portfolios to mitigate upcoming risks!
  • Long, unwieldy reporting processManual spreadsheet based reporting is simply horrible, overburdening the IT analysts and researchers.

What are the Best Practices to fight the Challenges Noted Above?

For the most effective credit risk management solution, one needs to gain in-depth understanding of a bank’s overall credit risk. View individual, customer and portfolio risk levels.

While banks give immense importance for a structured understanding of their risk profiles, a lot of information is found strewn across among various business units. For all this and more, intensive risk assessment is needed, otherwise bank can never know if capital reserves precisely reveal risks or if loan loss reserves sufficiently cover prospective short-term credit losses. Banks that are not in such good shape are mostly taken under for close scrutiny by investors and regulators, as they may lead to draining losses in the future.

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Adopt a well-integrated, comprehensive credit risk solution. It helps in curbing loan losses, while ensuring capital reserves that strictly reflect the risk profile. Owing to this solution, banks buckle up and run quickly to coordinate with simple portfolio measures. Fortunately, it will also lead to a more sophisticated credit risk management solution, which will include:

  • Improved model management, stretching over the whole modeling life cycle
  • Real-time scoring and limits monitoring
  • Powerful stress-testing capabilities
  • Data visualization capabilities and robust BI tools that helps in transporting crucial information to anyone who needs them

In summary, if your credit risk is controlled properly, the rest of the things are taken care by themselves. To manage credit risk perfectly, rest your trust on credit risk professionals – they understand the pressing needs of decreasing default rates and improving the veracity with which credit is issued, and for that, they need to devise newer ways and start applying data analytics to Big Data.  

Get more insights on credit risk management including articles, research and other hot topics, follow us at DexLab Analytics. We offer excellent credit risk management courses in Delhi. For further queries, call us today!

 


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SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

The noteworthy triumphs over us, humans, in Poker, GO, speech recognition, language translation, image identification and virtual assistance have enhanced the market of AI, machine learning and neural networks, triggering exponential razzmatazz of  Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these digital natives command the daily headlines, a tug of war has been boiling of late between two ace developers –  Equifax and SAS – the former is busy in developing deep learning tools to refine credit scoring, and the latter is adding new deep learning functionality to its bouquet of data mining tools and providing a deep learning API.

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Banking Business and Banking Instruments-3: Mortgages

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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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Credit Risk Analytics and Regulatory Compliance – An Overview

Credit Risk Analytics and Regulatory Compliance – An Overview

 

Post the Financial Crisis of 2008, there has been an increase in the regulatory vigilance of the capital adequacy of commercial banks across the globe. Banks need to be compliant with different regulatory capital requirements, so that they can continue their operations under situations of stress. A majority of analytical work in Indian BFSI domain is to provide analytical support to US based multinational NBFC’s. We would like to throw some light on the opportunities and scope of credit risk analytics in the US banking and financial services industry. The Federal Reserve requires the banks to be compliant with three main regulatory requirements: BASEL- II, Dodd Frank Act Stress Testing (DFAST) and Comprehensive Capital Analysis and Review (CCAR).

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